THE DATA DIGEST Volume 9 | 2025-2026
Connolly Alexander Institute for Data Science
This is a student-produced zine highlighting research and reflections. The views and opinions contained herein by the various authors do not reflect the views, opinions, or policy of Tulane University or CAIDS. The following materials are the views and opinions of the authors themselves.
DATA IN
ACTION
CONTRIBUTORS Associate Director of Student Engagement Jacquelyne Thoni Howard, Ph.D. Senior Professor of Practice
Lab Managers Kayla Carey Lab Manager/Scrum Advisor
Jordan Williams Lab Manager
Data Research Interns (DRI) Meghan Nagia & Julia Miller (Product Developers) Katie Bogdanow Xen Troutman
Kai Williams Kiet Huynh
Carmen de Juan-Roncero Anh C. Pham Nahema Dumonteil Cabanas Danh Ngo
Emmie Toulouse Alexander
James Hartridge
Katelyn Mann
Data Ambassador Council (DAC) Nina Moore (Product Developer) Addie Carmouche Lucy Booth
Keira Almazan Jacob Glazer-Fleishman Justin Moore Jayna Syrett Julia Monconduit Jaret Soloman
Zine Editorial Team Meghan Nagia
Julia Miller
Nina Moore
Zine Design Team Xen Troutman
Carmen de Juan-Roncero Toria Smith
Nahema Dumonteil Cabanas
PROJECTS & PARTNERS Project Name
Project Partner
Beautiful Sisterhood of Books
Dr. Kate Adams & Dr. Jacquelyne Howard
The Frances Joseph Gaudet Legacy Project
Dr. Rosanne Adderly & Nell Bolton
Feminist Pedagogy for Teaching Online
Liv Newman, Dr. Jacquelyne Howard & Dr. Claire Daniel
Civil War Monuments Database
Kris Plunket, PhD Student
The Center for Sport
Teresa Belt
Murphy Institute
JR And & Hussain Hadah
Kallion
Mallory Monaco Caterine
Data Lab Website
CAIDS Data Lab
LETTER FROM THE EDITOR
Dr. Jacquelyne Thoni Howard
The best part about working in the field of student engagement is seeing the growth of students year-to-year. This year, at CAIDS, our student interns and research assistants have put in a tremendous amount of work to grow into new skills, network, and share knowledge about data. Our student programs directly align with CAIDS mission “to create, foster, and support a comprehensive and inclusive culture of data literacy”. Each of our data student programs provide a different pathway for students to connect data work to their own data literacy, community outreach, and research, on their own terms. ·The Data Peer Mentors support their peers in gaining data literacy in the classroom. The Eboard leaders of our Tulane Data Collective build community and mutuality around data while networking.
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The Statistical Consultants support students in problem-solving and troubleshooting technical work in their research. The Data Research Interns work together to push forward the data research projects of faculty and community partners. The D3L Research Assistants document and measure data-driven research about discrimination and socio-economic disparities. The Data Ambassadors help get the word out about CAIDS programming and provide invaluable feedback as a focus group. The Community Engaged Research Interns supported community projects and initiatives about and using data. Our “Data Ops” and “PDs” within the Data Leadership program demonstrate sustained resilience through data collection and management processes, project management, and team leadership.
Dr. Jacquelyne Thoni Howard
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Whether they call themselves mentors, leaders, consultants, interns, ambassadors, or research assistants, all of these students have contributed to our shared goal of establishing a culture of data literacy on Tulane’s campus through community.
The following pages of this zine showcase the work that students have worked on and completed over the past year. As a portfolio of their work it highlights the multi-disciplinary and inter-disciplinarity of data work at CAIDS. It also emphasizes CAIDS cocurricular offerings beyond the classroom. Our student’s work features data in action – that is the different methodologies used, the variety of fields engaged, and numerous educational and research contributions our student and their faculty mentors have made by working at CAIDS. While each of the projects and programs are remarkable on their own, I am most proud that our students amplify through their actions and words our unofficial motto at CAIDS that “Data is for everybody!” I am heartened and confident that this hopeful message will gratify, strengthen and inspire our students as they pursue data skills, careers, and hobbies after their time at Tulane.
Sincerely,
Jacquelyne Thoni Howard, Ph.D. Senior Professor of Practice Associate Director of Student Engagement
Table of Contents Meet the Lab Data Lab Leadership 2025-2026 Data Research Interns Data Research Internship Program Project Posters Data Research Internship Program Articles 2025-2026 Data Ambassadors Data Ambassador Council Posters Data Ambassador Council Articles Other CAIDS Program Posters Beyond the Lab
MEET THE LAB
Back row left: Xen Troutman, James Hartridge, Kai Williams, Kiet Huynh, Anh Pham & Danh Ngo. Front row left: Katelyn Mann, Julia Miller, Meghan Nagia, Emmie Alexander, Carmen de Juan Roncero Sanchez & Jacquelyne Thoni Howard, Ph.D Not Pictured: Nahema Dumonteil, Katie Bogdanow
C.A.I.D.S
From left: Julia Monconduit, Keira Almazan, Jayna Syrett, Addie Carmouche, Nina Moore, Jaret Solomon, Jacob Glazer-Fleishman Not pictured: Justin Moore and Lucy Booth
DATA Lab Leadership
Our Work
At CAIDS, students have an active role in program management and leadership. Across different programs, students help run dayto-day operations, plan for the future, and provide feedback to faculty and staff. They hone team leadership skills and help make student programming sustainable.
MN
Q&A with the Lab Leads
JM
NM
KC
Meghan Nagia, Julia Miller, Nina Moore, Kayla Carey
Introduce yourself. MN “Hi, I’m Meghan Nagia! I’m a senior studying exercise science and one of the product developers for the
DRI Program.
JM “Hi, I’m Julia Miller! I’m a senior studying anthropology and environmental studies and also one of the
product developers for the Data Research Internship (DRI) Program.
NM “Hi, I’m Nina Moore! I’m a senior too, studying Chemical Engineering and Computer Science, and the
product developer for the Data Ambassador Council (DAC).”
KC “Hi, I’m Kayla Carey! I’m a second year masters student and the Lab Manager.
How long have you been working for CAIDS, and what has been your favorite part about it? MN “I've been working for CAIDS for 4 years. My favorite part about working with CAIDS is the
collaborative environment. I love working alongside people from different disciplines and seeing how data, technology, and digital humanities come together to support meaningful projects.”
JM “I’ve been working for CAIDS for 2 years. My favorite part about working at CAIDS is working as a
team. My coworkers have always been very uplifting and helpful, and our team meetings feel fun and productive, which has allowed me to meet cool people from other programs and school years.” NM “I have been working for CAIDS for 2 years. My favorite part about working with CAIDS is the people. I
have enjoyed working with all the student workers and professors. They are really kind hearted and nice.”
KC “I started working for CAIDS this school year, and I've been a Lab manager for about 6 months now! My
favorite part about working with CAIDS is working with very talented students towards positive change!”
What does it mean to be a PD? MN “Being a Product Developer for the DRI program means to work as a manager, liaison, and advocate for
the Data Research Internship team. Julia and I meet with project partners to determine their goals, translate those goals into timelines and actionable outcomes, and then assign those smaller tasks to the DRI team, while also supporting interns as they build the skills needed to complete the work. We are responsible not just for completing tasks, but for guiding a project from planning to execution and making the work sustainable and impactful.”
NM “To be a PD to me means to lead through collaboration. To me, a PD's biggest strength is not the ability to
just assigned tasks but their ability to work with all members of CAIDS to help promote our missions and goals around campus. With this being a relatively newer program, working as a PD means to be flexible, patient, and considerate with others and yourself and to most importantly have fun!”
KC “The question of "what does it mean to be in my role" is a question that I asked myself a lot as I was
starting as a Lab Manager. "Administrative Assistant" is a great description of my role, as my overall goal is to help Dr. Howard with whatever she needs to make a better functioning, sustainable CAIDS.”
What are some of the projects you work on in your role? JM “Meghan and I work across a wide range of projects with different partners, missions, and technical
needs, but they all are related to social and human issues. Our projects involve skills like historical archiving, database management, data visualization, data entry, website design, etc. Some of our current projects are focused on physician residency data, historical monuments, sports executive information, women and the 1884 World's Fair, online teaching, and women's health in Louisiana. We enjoy working on them because they are community-focused, interdisciplinary, and meant to be accessible and useful beyond the university.”
NM “I work on implementing and documenting our Liaison Groups, which is the externally facing side of the
DAC. These groups allow us to help assist CAIDS is more ways and collaborate with other student organizations to help promote data literacy and equity on campus. I organize our tabling events to get the word out about CAIDS, help with planning DAC socials, and also help set up some of our events.”
KC “My big projects so far include working on the AI Policy at CAIDS and developing a New General Intern
Orientation for all of CAIDS' programs.”
What is a challenge that you faced in the role, and how did you overcome it/what did you learn from it? MN “One challenge has been learning how to write out clear, concise tasks so the team has a strong set of
instructions and resources to complete the work confidently. Early on, unclear expectations slowed progress, so I improved through trial and error, feedback, and more thoughtful communication. By refining how I structured tasks and adding check-ins, I was able to create a better balance where the team felt challenged without being overwhelmed. This ultimately strengthened my ability to communicate expectations effectively and lead in a more intentional and confident way.”
JM “Initially, I had a hard time figuring out what to prioritize. There is always so much work to do or can be
done and figuring out what needed to get done first was a skill and a challenge. It honestly took a lot of time and patience with myself to figure it out. Some things I learned was that it is usually important to prioritize things with deadlines and things that must happen for some reason.”
NM “One of the biggest challenges I have faced in clarity in the direction the DAC is going. The projects we
are responsible with changes at times and so knowing what we should do and therefore having enough work for the sprints has been challenging. What has been helpful for me is going through the Notion pages of the past PD. Keeping in regular contact with Dr. Howard and taking time to think more deeply about what our goals are and what needs to be done to accomplish them. I learned to never give up even when things are uncertain.”
KC “One of my most recent challenges is properly managing my meeting times with Dr. Howard. Each week
when I meet, Dr. Howard, I have a finite time to discuss my week's progress, and I find myself struggling to properly make use of that time. I realized that I needed to set time before my meetings each week to reflect on what I have accomplished, and create a ranked list on what issues are most important to me as I move to the next week, and what bullet points I can leave unanswered. I am grateful for this practice, as learning how to prioritize is essential for long-term success.”
What is your favorite part of your role? MN “My favorite part of the role is the community and getting to work with such an amazing team. It makes
the work feel fun and like we’re all learning together. It’s also really rewarding to see a project grow from an outline into something tangible that can be shared with a partner or the broader community.
JM “My favorite part of the role has been watching our team grow together, become better friends, and gain
new skills. It has been a privilege to learn more about them and for all of us to grow in our roles together.”
NM “I love game night!! It's so fun to be able to plan our game night this year. I enjoyed getting together and
have fun as a group.”
KC “My favorite part of my role is running the weekly PD meetings. Being able to talk with the PDs and see
all the amazing things they and their teams are doing is the highlight of my week.”
What have you learned in this role? MN
“Through this role, Julia, Nina, and I have gained more confidence in our abilities and became more comfortable talking about our work and the skills we each bring to the team. We’ve learned how to manage and break down complex projects, balance the needs of partners, interns, and lab leadership, and adapt when plans do not go exactly as expected. This experience also helped us improve our communication and time management skills, and showed us the importance of trusting and relying on others for support when challenges come up.”
KC
“This role has definitely given me an appreciation and understanding of what it takes for an initiative to be successful. Working with Dr. Howard on more "big-picture" projects have given me a better view of the various factors that impact when, how, and if, a project will be implemented and the importance of patience when working on such projects.”
If you could give one piece of advice for the future PD’s what would you say? If not a PD, what advice would you give to someone in your role? NM “Meghan, Julia, and I would say to stay organized and communicate often with your partner PD, the other
PDs, lab leaders, and Dr. Howard. Staying organized helps you keep track of deadlines and prevents things from falling through the cracks, while regular communication gives you support and keeps projects running smoothly. It is also important to understand your goals early on and make a plan for how to implement them. You do not need to have every answer right away, but being open to asking questions, checking in, and adjusting plans when needed makes the role much easier. This experience taught us that leadership is less about having everything figured out and more about staying flexible, supporting your team, and creating a positive environment where everyone can do their best work.” KC “One thing I cannot stress enough for future Lab Managers is Organization, organization, organization!
Between leading the weekly PD meetings, meeting with Dr. Howard, and self-directing a majority of my weekly tasks, organization is essential to long-term progress, especially on tasks like the AI Policy and Orientation which are months-long projects. As Dr. Howard preaches, "documentation is care work", and as I am going through my second semester with CAIDS, the organization structures and documentation I made as I worked on projects last semester are definitely a gift past me made to my future self!”
Week in the Life of the Lab Leads Your paragraph text
By Kayla Carey, Nina Moore, Meghan Nagia, and Julia Miller
As lab leaders, we work to develop programming, plan and run weekly meetings, assign tasks to other students, and create documentation for the future. While each of us work with different programs and have separate goals, we all support each other’s work and keep in mind CAIDS’ mission of data literacy and equity.
Monday
Kayla: Lab Manager My Mondays are where I get the bulk of my work done for the projects I am currently working on. This semester, my main project is updating the Canvas Orientation Page for General Student Engagement Programs. I have been working from a Sandbox course to edit and add to the current Canvas orientation.
Tuesday
Meghan: DRI Product Developer My Tuesdays are usually spent prepping for our weekly DRI meeting and getting everything ready for the next sprint. I create and finalize sprint cards by looking at our overall project goals and recent partner requests, then breaking bigger objectives into smaller, manageable tasks with clear instructions for the DRI Team. A lot of this sprint’s work is focused on the Zine since it’s our next big project, but I also made cards for visualizations (MAC and Zine project posters), data entry (Monuments and the Murphy Institute), and web design (BSB and the Data Lab website). Tuesdays are also when I post on FPTO’s LinkedIn to promote the open-source guide, Feminist Pedagogy for Teaching Online, and help expand its reach and increase engagement.
Wednesday Nina: DAC Product Developer I spend the morning going through my weekly to-do. I also have a weekly meeting with Dr. Howard where we discuss updates about the DAC, usually the progress of our Liaison Groups. This meeting is really helpful. This usually involves preparing for our PD meeting that is later on that afternoon.
Wednesday cont. Julia: DRI Product Developer
I help run the DRI weekly meeting with Meghan where we share announcement, and interns share their work, questions, and successes. Meghan and I explain the tasks we have created for the next week and have the interns pick what they want to do. We also catch up with everyone we work with since this is the one weekly meeting that we have as a team. After this, I catch up on a bit of work that I need to do, and then, I go to the PD meeting to touch base with the other PDs and Lab Leads
Thursday
Julia: DRI Product Developer I spend my Thursday doing a mix of things! Meghan and I sometimes schedule meetings with our project partners on Thursday, but we didn’t have any today. We meet with partners to touch base about their projects and what they would like us to do. I did meet with Dr. Howard today to figure out the best way to promote the Feminist Pedagogy for Teaching Online on Substack. I also worked on making tasks for the team for next week and updating some of the documentation we use to keep track of what we have accomplished.
Friday Nina: DAC Product Developer We have our DAC meeting every other Friday from 3-4 pm. In this DAC meeting, I like to start with an Icebreaker. Then I discuss any announcements for the next two weeks. After that, I open it up to the DAC to discuss the work they did in the previous sprint and what they accomplished in their Liaison Groups. I end the meeting by discussing the tasks for the next sprint
Nina: DAC Product Developer We have our meetings biweekly,, so when we don’t have one, I usually work on the tasks I received during our previous PD meeting. This usually involves creating sign-up sheets for events, scheduling tabling, or writing cards for the next sprint and planning the next meeting. I also look at our yearly goals to see how well we are accomplishing them and whether we are accomplishing them on time.
20252026
Data Research Interns D a t a Re s e a rch I n t e r n s h i p P ro d u c t D eve l op e r
Meghan Nagia (she/her) Meghan Nagia is a senior from Atlanta, Georgia who is majoring in Exercise Science with a minor in SLAM on the pre-med track. In addition to her work in the Data Lab, Meghan is the Vice President of the South Asian Association, a member of the Phi Mu Fraternity, and a student researcher at LSUHSC. Meghan is passionate about combining data, research, and design, and hopes to attend medical school after graduation. Outside of school, she enjoys working out, music, crafts, trying new restaurants, and spending time with friends and family.
D a t a Re s e a rch I n t e r n s h i p P ro d u c t D eve l op e r
Julia Miller (she/her) Julia Miller is a senior from Baton Rouge, Louisiana who is studying Anthropology and Environmental Studies with a Geographic Information Systems certificate. Beyond working as a Product Developer, Julia is president of Tulane Anthropology Club, treasurer of Gardening Club, and a member of the Feminist Alliance of Students at Tulane. She is interested in mapping, data equity, natural disasters, libraries, and digital humanities. In her personal life, Julia loves reading and sewing, and you can often find her trying new restaurants.
D a t a Re s e a rch I n t e r n
Emmie Alexander (she/her) Emmie Alexander is a sophomore from Chicago, Illinois majoring in Political Science and International Relations. Beyond her work at CAIDS, she is a student worker at the Tulane University Office of Financial Aid and a member of the Tulane chapters of Pi Sigma Alpha (the Political Science Honor Society) and Epsilon Eta (the Environmental Sustainability Fraternity). Emmie is interested in diplomacy, governance, military science, and aviation. Outside of class, she enjoys reading, swimming, traveling, and spending time with her friends and family.
D a t a Re s e a rch I n t e r n
Katie Bogdanow (she/her) Katie Bogdanow is a Junior from Dallas, Texas studying Mathematics and Sociology with a minor in Strategy, Leadership, and Analytics (SLAM). She has served at CAIDS since the beginning of her studies at Tulane University, where she also participates in Girls Who Code, Tulane Jewish Leaders, and Sigma Delta Tau. In her free time, Katie enjoys watching films, trying new foods, and spending time with her friends.
D a t a Re s e a rch I n t e r n
Carmen de Juan-Roncero (she/her) Carmen de Juan-Roncero is a senior from Zamora, Spain studying Finance and Management. Because she is especially interested in the intersection of technology, data and business, she is also pursuing a Computer Science certificate. In addition to her work in the Data Lab, Carmen serves as the Professional Development Chair of Alpha Phi Omega, where she leads the fraternity’s professional programming. She enjoys challenges, both professionally and through sports, especially CrossFit, and values time with friends and family, exploring new cities and trying good food.
D a t a Re s e a rch I n t e r n
Nahema Dumonteil Cabanas (she/her) Nahema Dumonteil Cabanas is a Sophomore from Merida, Mexico who is majoring in Mathematics and Computer Science. In addition to her work in the Data Lab, Nahema is a Division I Track and Field athlete and a member of the Student-Athlete Advisory Committee (SAAC). Nahema is trilingual in English, French, and Spanish, and is interested in applied math, data science, and formal normative system design. Outside of school, Nahema enjoys listening to music, dancing, learning about new topics, and journaling.
D a t a Re s e a rch I n t e r n
James Hartridge (he/him) James Hartridge is a freshman from Memphis, TN studying Computer Science. In addition to working within the Data Lab, James is a member of the Google Developer Group, Tulane AI in Society Club, and Tulane Data Collective. He also has two applications he is building as projects both for classes and personal experience. James is interested in data engineering and management, machine learning, data analysis, and software development and engineering. Outside of work and class, James likes to try new foods and deserts, watch sports, movies, and Youtube, listen to music, bowl, travel, and hang out with his friends.
D a t a Re s e a rch I n t e r n
Kiet Huynh (he/him) Kiet Huynh is a sophomore from Da Nang, Vietnam. He’s majoring in Computer Science and Finance with a minor in Mathematics. Besides his work in the Data Lab, Kiet is also an Information Technology Support Intern at Tulane Information Technology. Kiet is interested in data science, applied math, and business analysis. He also likes to listen to music, work out, and tan.
D a t a Re s e a rch I n t e r n
Katelyn Mann (she/her) Katelyn Mann is a third-year undergraduate student from Houston, TX, studying neuroscience. Before working as a Research Intern, she worked as a Communications and Systems Intern for CAIDS. Outside of CAIDS, she is an active member of DASH and a contributing writer for the data section of The Tulane Hullabaloo. She also works as an Administrative Assistant for TUSABE, serves as Vice President of Green Club, and is the Vice Chair of Administration for CACTUS. In her free time, Katelyn enjoys yoga, working out, writing, and listening to music.
D a t a Re s e a rch I n t e r n
Danh Ngo (he/him) Danh Ngo (Danny) is a sophomore from Ho Chi Minh City, Viet Nam, studying Earth and Environmental Sciences and Data Science at Tulane University while pursuing a Geographic Information Systems certificate. In addition to working in the Data Lab, Danny serves as the Earth Science Club secretary and is a Research Assistant in a paleoclimate lab. He is interested in climate and environmental change, GIS, and data storytelling that helps people understand complex environmental topics. Outside of school, Danny enjoys collecting figures, listening to music, and watching movies.
D a t a Re s e a rch I n t e r n
Anh Pham (she/her) Anh Pham is a junior from Ha Noi, Viet Nam, studying Finance and Math, while pursuing a certificate in Computer Science at Tulane University. In addition to working at the Data Lab, Anh is active in the Algorithmic Trading Club and Badminton Club, serving as treasurer. Anh is interested in actuary science, insurance, and risk management. Outside of school, she enjoys traveling with friends, meeting new people, and cooking.
D a t a Re s e a rch I n t e r n
Xen Troutman (she/her) Xen Troutman is a senior from Orlando, Florida majoring in Homeland Security. In addition to her work in the Data Lab, Xen is the Vice President of the Tulane Organization for Global Affairs and the Secretary for her major’s honor society (OSS). Xen works part-time as a local barista and is passionate about public service research, U.S. policy data, and national security. After graduation Xen hopes to join a volunteer program to expand on her current skill set. Outside of school, she enjoys working out, playing piano, hiking, and reading with friends from her southern states book club.
D a t a Re s e a rch I n t e r n
Kai Williams (he/him) Kai Williams is a Junior from Chalmette, Louisiana, who is studying Information Technology with a concentration in Cybersecurity. Kai Williams has earned the CompTIA A+ certification and is looking to earn more in the future. In addition to working within the Data Lab, Kai is an Information Technology Intern with the Tulane Newcomb Institute, where he keeps workflow and classroom technology up and running. Kai Williams is interested in social justice related to data and the tech divide, particularly tech accessibility in impoverished communities. He also enjoys playing sports and hanging with friends.
DATA RESEARCH INTERNSHIP PROGRAM
PROJECTS
This paid internship provides students with tangible experience while working on the digital scholarship projects of Tulane faculty in the humanities, social sciences, and sciences. Technical areas include: digital media, digital archiving, web design, database development, coding, data entry, design thinking, UX/UI, and audio/visual editing.
DATA RESEARCH @ CAIDS Introduction Our Lab At the Connolly Alexander Institute for Data Science (CAIDS), we build supportive, interdisciplinary, ethically-driven data communities through scholarship, collaboration, mentorship, and networking to shape the data literacy landscape at Tulane University.
Objectives Solve multi-disciplinary research questions using data research methods supported by ethical data frameworks. Work collaboratively as a team on a range of data research projects of Tulane faculty and community partners while building a portfolio of data and essential skills. Continually focus and reflect on building projects and relationships around ethical data frameworks such as partnership and reciprocity, collaboration, leveraging resources, and agentic co-education.
Research Values Intersectionality Inclusiveness Community-Engaged Research Care and Connectedness Expertise, Innovation, Transparency & Accountability
Check out our website!
Projects Center for Sport
Data Lab Website Feminist Pedagogy for Teaching Online Kallion: Human Focused Leadership MAC: Visualizing Health Disparities Historical Monuments: Entering Data to Understanding the Past
The Beautiful Sisterhood of Books Datasets of Physician Residency Restrictions Frances Gaudet Legacy Project
An Overview of the Data Research Internship Program
Skills
Outcomes
Data cleaning, Dashboard design
Used Excel and R to clean and organize data. Used Power BI and SharePoint to create and share a dashboard of visualizations.
Website development Graphic design
Continued designing and updating the website on WordPress using the Divi Theme. Created website graphics in Canva.
Website development, Database development, Graphic design
Added new sources to database using Excel. Created promotional materials for the guide. Updated WordPress website with news.
Website development, Website management
Updated the website using WordPress and Colibri. Audited the website to determine the status of specific pages.
Data visualization, Graphic Design
Designed data visualizations for research report using Canva and Flourish.
Data entry, data cleaning
Updated spreadsheet with primary and secondary source material using Google Sheets to support historical research.
Desi
Data visualization, Website Development
Completely remade the look and feel of the website. Created data visualizations using Python, Flourish, and JSON.
Data entry, data cleaning
Compiled data from PDFs into Excel to create a new, usable dataset.
Transcription, digital archiving
Transcribed oral histories using tools like Otter.ai for research and accessibility. Prepared to transfer files.
The Frances Gaudet Project Project Partners: Dr. Rosanne Adderly and Ms. Nell Bolton
Mission The Frances Gaudet Legacy Project aims to share widely the example of her extraordinary life in order to guide and inspire similar work in our present times. We are pursuing this aim through extensive research, focusing particularly on her work in education and prison reform in the greater New Orleans area. We also seek opportunities to bring her story to wider audiences.
Project Goals We focus on the digitally preserving historical records to ensure Frances Gaudet’s legacy endures through accessible digital archives. We continue preforming archival research and cross reference sources to maintain accuracy, depth, and historical integrity.
Skills Website Development, Digital Archiving, & Primary Source Cross-Referencing
Technology Omeka
To view more visit our site!
Digital Archiving: Process of collecting and storing data from Frances Gaudet to enable future access for users or researchers.
Video Transcription: Transcribed oral histories with some help from Otter.ai to makes the videos more accessible and help researchers search the transcript for keywords
FEMINIST PEDAGOGY FOR TEACHING ONLINE Project Partners: Liv Newman, Jacquelyne Thoni Howard, Claire Daniel
Mission
Feminist Pedagogy for Teaching Online provides resources about integrating feminist pedagogy and technology into online, hybrid, and traditional undergraduate courses. The editors specifically designed the guide to support interdisciplinary topics and fields and to focus on active learning practices in social sciences, the liberal arts, the humanities, and STEM subjects.
Skills & Technology Skills: Database Development, Data Cleaning, Web Development Technology: WordPress, Microsoft Excel, Google Sheets, Google Form
What We Did Added and documented new sources in the FPTO database, including writing tenets and abstracts. Organized and improved website content, including assignment tables and site sections. Created promotional materials such as a YouTube video and FPTO Chapter posts using Canva. Developed new site exhibits, including Archival Collections and Campus Protests pages.
FPTO Database
Chapter Post
Website’s Author Page Scan to see more!
Website and Posts
This Beautiful Sisterhood of Books A Journey of Recollection of The Missing Works and The Lost Voices of The 1884 New Orleans World Fair
This Beautiful Sisterhood of Books is a digital humanities recovery project that recreates the Woman’s Literary Department from the New Orleans 1884 World’s Fair and “recovers the exclusion” of Black women and their work from the Woman’s Department exhibit. We do this through multiple projects in which we collect books, journals, newspapers, and sheet music donated by publishers and collected by “lady delegates” from around the US in the 1800s. This data is then used to research and recreate exhibits from the fair.
Main Projects
Skills and Tools Used Website Development Database Design, Ethics, and Management Data Validation, Visualization, and Storytelling Metadata Standardization
Excel HTML CSS JavaScript Python WordPress Floursih
Latest Work Cleaned Black Women’s Writers Spreadsheet and added more Authors
Retouched Multiple Pages on the Website for better Accessibility and Reader Experience
Created Multiple Distinct Began Development on a Visualizations to help visualize Virtual Library, giving users the works that we have a virtual experience of what it collected and researched would be like to interact with the exhibit in real life
Project Partners Dr. Jacqueline Thoni Howard and Kate Adams
Center for Sport Skills Used Data Cleaning Data Visualization Database Development Web Development
Tools Used Power Bi R programming language Excel
Project Partner: Tess Belt
The Center for Sport Database is a student-compiled resource that tracks professionals in the sports industry, with a focus on executives and analysts. Students built the database over time by researching sports organizations and the people who work in them. It is designed to help sports law students explore career paths, find relevant contacts, and understand what sports and organizations are available by region.
Data Cleaning using R: Before making any visualizations, we needed to clean the data. We had to turn N/A strings into null values and split the Education variable column into 2 columns based on the “ /”.
Data Visualization with Power BI: Some examples of the visualizations made with the cleaned data. Dashboards and visualizations
Scan me to see more!
Murphy Institute Investigating The Extent To Which Restrictions On Residency Positions Have Contributed To Escalating Healthcare Costs
Project Partners Hussain Hadah & JR Ang
Our Goal Through a rigorous analysis of historical data and policy decisions, we aim to explain how artificially constraining the physician workforce influenced the U.S. healthcare market, helped drive up expenditures, and widened the care gap between different groups of people.
Our Work Currently the data needed is inaccessible. We turn the data found on the inaccessible PDF files into data in Excel sheets, allowing the data to be used for research purposes.
Tools & Skills Used Microsoft Excel Data Entry/Encoding, Database Management
Data Transfer
Event Study: Medicaid Expansion and Graduate Medical Education
Event-study results from “The Medicaid Expansion and Graduate Medical Education: Hospital-Level Evidence from 2010–2019,” presented at the 95th Southern Economic Association Annual Meeting in Tampa. The figure compares pre- and post-expansion trends relative to Medicaid expansion, showing stable estimates prior to expansion and a decline in the estimated treatment effect after the policy change. This work was part of a session on healthcare labor markets, staffing, and organizational performance.
MARY AMEILA CENTER FOR WOMEN’S HEALTH EQUITY RESEARCH Project Partner: Melissa Evans
Mission At the Mary Amelia Center for Women's Health Equity Research (MAC), we believe that opportunities for health begin where people are born, grow, learn, work, and play and are shaped by the policies and systems that form the foundation of our society. We envision a world where all people are afforded equal opportunity to achieve their full potential for a happy and healthy life. The DRI works on data visualizations for MAC reports.
Skills & Technology Data Visualizations, Graphic Design Tableau, Flourish, Canva
What We Did Created and redesigned data visualizations for the Health of Women & Girls in Louisiana report. Developed graphics for sections on structural racism, healthcare access, education, and economic factors. Converted BRFSS data from PDFs into organized Excel datasets. Simplified graphics and tables to improve clarity for the final report.
The health of women and girls in Louisiana: Racial disparities in brith outcomes
The social-ecological model and organization of this report.
Project Logo
Wage gap in the state of Louisiana and the United States of America
Percentage of low birth weight babies among Black women 2009-2011
Proportion of the population of the state of Louisiana overall and population enrolled in medical school in Louisiana by race, 2014.
MONUMENTS Project Partner: Kris Plunkett
Mission Monuments is an ongoing project for Kris’ PhD dissertation. The database will be made publicly available for educational use and outside research. Monuments are a community issue, and this database (and the corresponding analysis) will provide communities with context to help understand their local memorials.
Our Work Reviewed external monument datasets and verified entries in the project database. Added missing monuments and abolitionist memorials to the spreadsheet. Cross-referenced datasets to ensure database accuracy and completeness. Flagged uncertain entries and tracked progress across monument datasets.
Skills & Technology Database Development, Data Cleaning
Box, Google Sheets
Cross referencing and making sure monuments fit the set criterias led to a clean specific set of monuments being added to the spreadsheet
For our annual Love Data Week, we organized a Monuments Datathon where we invited all students from all majors to come help cross-reference and add new monuments to our spreadsheet. Not only did we receive some amazing help, but we also gave the opportunity to students to work on a real world data set!
Data Lab Website
Project Partner: Jacquelyne Thoni Howard
mission As we know, CAIDS aims to improve data literacy among the Tulane community. An issue that we face is finding engaging ways to attract students across fields of study and classifications. We decided to take on a Data Literacy Project that all the DRI Interns could contribute to in the form of a website.
skills Web Development Education Communication
tech used WordPress Canva
Our Work
Each Data Lab member contributed in different ways; We used WordPress to build the webpages shown above showcasing work from DRI and DAC. We collected all kinds of information related to The Data Lab and our work to showcase it on the web: values, teams, goals and more. We worked thoroughly to create meaningful work by pushing for quality over quantity, this means cross-referencing and double checking. Through this work, we promote data literacy among students on campus. Here our DAC and DRI Page below.
QR CODE HERE
QR CODE HERE
Kallion Overview Kallion is a non-profit started over 5 years ago. They provide leadership development programming for those who study/work with humanities. They are working to incorporate leadership development into pre-existing educational programming.
y g o l o n h c e T Skills and
esign D d n a t n e m p Website Develo ocs, D le g o o G i, r b li WordPress, Co Excel Internet Archive
Work Done Website Restoration. Went through project partner’s documents and made sure it aligned with the website. Audited the website and created a log of what pages were public and private. Utilized sliders with WordPress on the Find Your Learning Community page to have the texts move horizontally across the screen. Scan me to see more!
Project Partner: Mallory Monaco
DATA RESEARCH INTERNSHIP PROGRAM
ARTICLES
These articles, written and designed by members of the Data Research Internship Program, reflect and examine the different ways that data intersects with real life issues. In this issue, students examine biased data, data privacy issues, advertisements, air pollution, and even emojis, and showcase possibilities and issues related to data.
Impact of Biased Data and Biased AI on Social Justice By Nahema Dumonteil
What is AI?
AI Artificial Intelligence is the ability for machines to solve problems, make decisions and learn, all things that only human intelligence could do. AI works by running algorithms to analyze big sets of different kinds of data and finding patterns that help it make predictions and decisions. With time and more data, AI learns more and more.
What is Biased AI?
AI will trust the data it is given to learn. It won't question whether it is true or false, it just cares to learn it and find patterns. This means AI does not care to notice if the data is ethical, fair or correct. If we give AI discriminatory data, the AI will learn from it and discriminate. This can become a big social justice issue when companies use AI to make certain decisions. Unfortunately we live in a biased world where there is eurocentrism, gender bias, economic bias, racial bias, along with unknown inherited biases. With time, this information shapes societies, and in turn AI. Just how humans learn this bias and become biased, AI can do that too. A big and important realworld example of the effects of biased AI is the healthcare risk predictor that was used in the United States.
Racism in the US Health Care System A healthcare risk predictor algorithm was used to predict which
AI risk predictor algorithm
patients would need extra care and closer monitoring by identifying the “sicker” patients. The data used in the algorithm included information from the patient's past expenses in medical needs. In the quantitative world, past expenses do a good job in telling us how many health needs a patient has had, which can be a good prediction on how much they will need in the future. The problem with this is that there is a socioeconomic part to the data that developers didn’t notice because they were not looking at the bigger picture. There is a big gap between past expenses from white patients and past expenses from black patients. These gaps are a result of unconscious racial bias and economic bias, leading to black patients receiving lower quality care and black patients having lower income and using less medical services. This led to the algorithm indirectly favoring white patients over black patients. These gaps create something called a “Data Gap” which is when crucial information is missing which can lead to uninformed decisions. In this case the real socioeconomic gap creates a data gap, and to fix this we need to fill in the gaps with what should be there.
Graph 1: Estimated Health Care Spending by Race and Ethnicity From 2002 Through 2016 (Dieleman et al.)
In the bigger picture, we can clearly see white patients spend more money on healthcare than any other racial group. This bias is so normalized that many weren’t aware at first. The only reason people started investigating this healthcare issue is because people started noticing gaps between how many white vs black patients were receiving extra healthcare. Fixing the algorithm made a huge difference on who was receiving extra care.
Graph 2: Number of chronic illnesses versus algorithm-predicted risk, by race (Obremeyer et al.).
Graph A shows the number of chronic conditions by race vs the algorithm risk score. Graph B shows Black patients at or above a given risk score from the original algorithm results, and from the simulated algorithm results that removes the bias. The graphs clearly show how even when Black patients were sicker, their risk score was lower so they received less extra medical care while white patients who were less sick were getting extra medical care, which is nonsensical and unethical. “Remedying this disparity would increase the percentage of Black patients receiving additional help from 17.7 to 46.5%” (Obremeyer et al.). This is a significant difference, which ironically quantifies the amount of change bias can bring. The fact that developers missed something that would significantly affect the decision making algorithm is concerning. Their mistake was in the data they chose to use, which was biased. If the developers had a bigger picture mindset, they would have realized this earlier.
Biased AI pushes out marginalized groups even more. In this example, AI was following systematic discrimination instead of helping us advance as a society.
How to fix this
This is a hard fix because of the historical bias in our society, making any random piece of data biased. The first step is to recognize that data can be biased and some types of data should not even be used to make certain decisions. Modifying the data is also as important as choosing it because algorithms built on this data should not only translate and communicate what is in it, but also adapt to and address its gaps and biases. The data being used for these algorithms should not be “what is” but rather “what should be”. This can be achieved by using diverse data from different ethnicities, genders, races, socioeconomic backgrounds and demographics. The diversity will help each perspective to be learned by the algorithms, ensuring a more reliable and ethical AI. It is also important to compare different social groups and make sure they are not a factor in the different decisions AI is making. This can be achieved by testing the algorithms and making sure attributes like race and gender are independent from the predictions.
Citation: Carvão, Paulo. “Can AI Be Fair and Unbiased?” Harvard Advanced Leadership Initiative Social Impact Review, 14 Dec., https://www.sir.advancedleadership.harvard.edu/articles/can-ai-be-fair-and-unbiased. Datatron. “Real-Life Examples of Discriminating Artificial Intelligence.” Datatron, 2025, https://datatron.com/real-life-examples-of-discriminating-artificial-intelligence/. Dieleman, Joseph, et al. “US Health Care Spending by Race and Ethnicity, 2002-2016”JAMA, August 17, 2021, https://jamanetwork.com/journals/jama/fullarticle/2783068 “Ethical AI: Developing Fair and Transparent AI Models for a Better Future.” Data Engineer Academy,https://dataengineeracademy.com/module/ethical-ai-developing-fair-andtransparent-ai-models-for-a-betterfuture/#:~:text=Understanding%20Ethical%20AI,to%20benefit%20as%20a%20whole. Obremeyer, Ziad, et al. “Dissecting racial bias in an algorithm used to manage the health of populations”, Science, https://www.science.org/doi/10.1126/science.aax2342
Send To:
From: TulaneSecurity.edu
TulaneAffiliate.edu
TAP. CLONE. ENTER : By Kai Williams
Hello, Tulane affiliate, this is an email from Tulane University. Regarding privacy protections, someone has stolen your Splash Card information. This is important because it provides access to many campus resources, such as your meal plan and dormitory.
Have you handed your splash card to someone or lost it in the past few weeks? We are asking this because, in the wrong hands, individuals can exploit your resources. One of the more advanced attacks is to clone your RFID-enabled splash cards (the tap feature on the splash card) and transfer them to a duplicate card, giving the duplicate card unauthorized access, meaning that this duplicate card will act as if it is the original, all while you may have the original card in your possession.[1]
Someone with this duplicate splash card can access your dormitory without your authorization and then gain access to your room and belongings. Once inside someone's room, the actor can gain access to personal belongings, IDs, passports, credit cards, or personal computers, and can access confidential information such as emails, passwords, and personal media. This opens the door to deeper malicious behaviors.
Gaining access to a personal computer, including information such as emails, could allow a malicious actor to carry out phishing attacks that are either personalized for a specific group (spear phishing) or sent to a large group (bulk phishing). In this case, the individual would have access to an authorized Tulane email account, making the email appear legitimate and allowing attackers to lure affiliates into entering credentials on counterfeit Gibson portals, Splash Card login pages, or Tulane Single Sign-On screens. Some attacks even hide malware, such as key-loggers, that record the victim's inputs, further exposing their sensitive information. Over 3.4 billion spam emails are sent daily, making it a major source of data breaches.[2]
Once the attacker has this information, the cycle continues: they can access other individuals' credentials and use them at will, in some cases to further exploit or even hold individuals' private information ransom, similar to blackmail. Email accounts are especially at risk when students use weak or predictable passwords based on personal information shared online, such as birthdays, pets, or usernames. For example, a student named Molly posted a picture of her dog, Roxy, on her social media, and with these context clues, an attacker could guess the password, such as “Moxy&Roxy10312020”. Typically, individuals use one password for many different accounts and systems, so knowing this password gives them access to Tulane systems, personal data, the ability to impersonate a student, or the ability to use these credentials to gain access to other accounts under their authentic account.
Treat your Splash card with the same caution you would for sensitive personal information, and never share it with others. Using safeguards such as RFID-blocking protection will prevent the radio signal emitted by your splash card from reaching card readers. This means that when you do not intend to use your splash card for authorization purposes, it remains protected from the radio signals that would typically provide authentication for your specific ID number.
Protect your email by creating a strong, unique password with at least 12 characters and enabling two-step verification, such as Duo Mobile (which Tulane uses).[3] Having a strong password is just as important, but you still have to be cautious of suspicious links or messages. The strongest password cannot protect you if the credentials are stolen. Now that you have the guidelines, treat your personal data with the same care as you would when rushing to The Boot; one careless click, share, or lost card can compromise your privacy, cost you money, and restrict your access. Always use a unique password, enable 2-step verification, and be cautious not only in person but also online with your personal information.
References: [1]2CQR. (2025, June 30). RFID cloning in 2025: Are we doing enough to prevent tag-level hacks? 2CQR. https://2cqr.in/rfid-cloning-in-2025-are-we-doing-enoughto-prevent-tag-level-hacks/ [2]AAG IT Services. (n.d.). The latest phishing statistics. AAG IT. Retrieved October 25, 2025, from https://aagit.com/the-latest-phishing-statistics/ [3]Karbauskas, Š. (2025, April 16). How to create a strong password in 2025. Cybernews. https://cybernews.com/best-password-managers/how-tocreate-a-strong-password/
PALANTIR IS Introduction WATCHING In recent months, YOU government agencies, both
By: Xen Troutman
civilian and military, have What is Palantir, Exactly? rushed to secure multi-year The company describes its contracts totaling billions of mission as a data analytics dollars with an analytics firm organization that leverages called Palantir Technologies. The organization is known for artificial intelligence to automate its intelligence platforms used decision-making. The company specializes in areas related to for predictive modeling and defense reformation and automated decision-making, intelligence gathering through particularly in warfare large-scale digital infrastructures. strategies. With the recent surge in public-sector Unlike traditional data firms, enthusiasm towards Palantir, where information is collected the public has begun to ask a and scattered across critical question; is Palantir departments, formats, and simply the next iteration of security levels, Palantir’s the traditional public-private platforms ingest data from a variety of locations and sources partnership, following in the to create a unified network of footsteps of defense giants information. This architecture like Boeing or Lockheed enables Palantir to uncover Martin, or is it something connections that would entirely different? And should otherwise remain hidden due to we be concerned? fragmentation and complexity.
Palantir and the U.S. Government In the wake of federal budget cuts, the nullification of grants, and widespread contract terminations, Palantir has managed to thrive within the public sector. In 2025, Palantir signed contracts with the U.S. Army, U.S. Immigration and Customs Enforcement (ICE), the Department of Health and Human Services (HHS), the Department of Veterans Affairs (VA), the U.S. Air Force, and several others agencies. The most notable being a $10 billion, 10-year Enterprise Service Agreement with the U.S. Army, announced in August, 2025. This deal consolidates 75 separate software contracts into a single framework, allowing units to purchase Palantir’s platforms selectively to support battlefield intelligence, cyber operations, and multi-domain coordination based on need.
Why You Should Possibly Be Worried Palantir provides platforms that aggregate personal, biometric, and behavioral data into centralized systems designed to identify patterns and predict threats. For example, Palantir’s Gotham platform powers ICE’s Investigative Case Management (ICM) system with the ability to track individuals across databases, social networks, and geographic locations. In practice, this looks like a cross-agency data fusion search for individuals in real-time. The system could pull information from visa applications, criminal records, health records, biometric databases, social media platforms, and any other database Palantir has access to, building detailed profiles of suspected noncitizens. The company’s tools have also been used by police departments for predictive policing and by the U.S. Army to track enemy movements. What makes Palantir’s role particularly concerning is the opacity of its systems: the logic behind its recommendations is proprietary and difficult to audit. Even Palantir employees themselves cannot fully explain how the software works. Former Employee Linda Xia said, “It's really hard to explain what Palantir works on or what it does.” This is because the organization is structured for lateral movement, and engineers spend their entire careers working on a singular objective without understanding the full scope of their work.
Conclusion With Palantir’s software increasingly being used to guide decisions about who is monitored, detained, or targeted, and its creation of a federally connected network of information, it raises ethical questions about privacy and civil liberties. Questions that the general populace, apparently, lacks the proper security clearance to ask.
The Economic Times. (n.d.). Palantir’s AI is helping pick targets in war zones, and its billionaire boss Alex Karp is boasting about it. The Economic Times. FedScoop. (n.d.). Palantir: Federal agencies and government data. https://fedscoop.com/palantir-federal-agencies-government-data/ Metz, R. (2023). What Palantir actually does. Wired. https://www.wired.com/story/palantir-what-the-company-does/ Franck, T. (2025, August 1). Palantir lands $10 billion Army software and data contract. CNBC. https://www.cnbc.com/2025/08/01/palantir-lands10-billion-army-software-and-data-contract.html Wired. (n.d.). Palantir’s government contracting push. https://www.wired.com/story/palantir-government-contracting-push/ USAspending.gov. (n.d.). Contract award: H9222216C0078. https://www.usaspending.gov/award/CONT_AWD_H9222216C0078_9700_-NONE-_NONEPalantir Technologies. (n.d.). Palantir. https://www.palantir.com/
COUNTING THE ADS
VOL. ##, NO. ##
KATELYN MANN
CAIDS ZINE
HOW ADVERTISING BECAME IMPOSSIBLE TO ESCAPE If you had to estimate how many advertisements the average American is exposed to on a regular day, what number would you guess? Seventyfive? One-hundred? Maybe even fivehundred? To estimate this, let’s consider a typical weekday for an American working a standard 9-to-5 job. At the start of the day, while getting ready for work, the average person may decide to watch the news channel and proceed to view around eight minutes of commercial breaks in the duration of watching 30 minutes of cable television (“Ad Breaks on TV Lengthen Breaks from TV”). On the average 27-minute commute (“Average One-Way Travel Time to Work Rises”), they might pass by billboards that line the highways and city streets, adding up from up to 37.9 billboards per 100 miles, depending on the state’s law (“Billboard Faces per Highway Mile”). During their lunch break, they may choose to stop by a local cafe and see that they’re promoting a new seasonal drink and while eating, actively scroll on social media, where users in the United States are reported to be exposed to dozens of ads per hour of active use.
Depending on how some might spend their evening, the advertisements continue to appear in the promoted results at the top of search engines, sponsorships on the courts of sports games, and in the commercial breaks of streaming services. By the end of the day, without actively seeking them out, the average American may have encountered anywhere between 300 to 10,000 advertisements. Many articles and market research experts claim that an average person is exposed to anywhere between 4,000-10,000 advertisements daily, while others suggest that this number is closer to 300700 but this number is almost impossible to pinpoint (“Brand Messaging and the 5,000 Ads Myth”). To fully understand what this number is and why the range of guesses is so large, we need to explore the various forms that we include in our definition of an advertisement.
BUY NOW! Traditional media (television commercials, weekly ads, billboards) and digital sources (social media, websites, apps) are methods of marketing that are obvious to viewers. Sponsorships are more nuanced because they can either be explicitly or subtly promoted, most times in sports arenas and funded events, but also companies supporting or being supported by professional athletes, celebrities, movies, TV shows, and online influencers. It’s often for viewers to make fun of overtly obvious product placements in advertisements. For example, when Mean Girls (2024) released to widespread audiences, there was rampant discussion over the placement of Elf products in Mean Girls (2024) with fans criticizing that the character Regina George wouldn’t use drugstore products. This issue of promoting products that don’t necessarily align with a character or creator’s values and beliefs is a recurring issue that feels dissonant for fans. Another example is Bobby Parrish, a content creator that posts about health and nutrition on Tiktok where he advocates clean eating and encourages audiences to stay away from artificial ingredients.
ALERT!
His endorsement for Khloe Kardashians’s protein popcorn called “Khloud” received backlash by fans because he was promoting a product with artificial ingredients that he would normally warn others to stay away from. These marketing strategies can have negative consequences of distracting from the original message and risks diminishing the quality of the content or viewing experience. There are negative connotations around sal es but now even more so in today’s era of constant advertising, especially with products that don’t have scientific backing or add to someone’s quality of life. Due to this stigma, some companies and influencers choose to promote products subtly, sometimes compl etely undisclosed to avoid facing criticism.
ON SALE!!! If you are a student and an active TikTok user, you may be familiar with posts from student creators, sharing study tips and highl y suggesting others to use some obscure AI website to hel p you study. Or maybe you’ve seen TikTok videos of content creators recording their reaction to accepting a highl y competitive position and highl y paid offer by the hiring manager on a call . While accepting the offer, the manager might read a word-for-word script expressing how impressed they are and asking how they found the position, and the creator will promote an AI website that hel ps with the job searching process. It seems like an innocent video at first glance, believing that the creators are speaking with good intent. But the abundance of videos with the same script and no subtext that informs the viewer that the video is a paid par tnership, it becomes blatantl y obvious that it’s an undisclosed adver tisement. In my experience of reading the comments of these undisclosed videos, many users show immediate disinterest once they realize they are being sold something, especiall y an ar tificial intelligence product, and will leave negative feedback on this form of adver tising content because it is tricking the viewer for monetar y gain. Companies have gotten smar ter at learning their target audience and have learned how to captivate attention in a fashion that can be positivel y recepted. It’s impor tant to build a relationship with the target audience and listen to feedback because it can strengthen a community if you give them something to bond over. It’s why Dr. Pepper using the jingle from a Tiktok viral video by Romeo @romeosshow was profound and also why the Bridger ton Season 3 received praise for listening to
Search
In November 2021, a content creator executed a marketing scheme that did well because he knew his target audience, but it quickl y received backlash in how he went about it. Kyle Scheele created a series of posts that invol ved a surprise sponsorship deal with Kum & Go that viewers could suppor t throughout his journey. He hooked audiences with a prank of putting a cardboard cutout of him promoting a fake meal in a local store to see if anything would happen. A followup video of him checking if the cutout was still there he informed audiences that he was contacted by Kum & Go because they thought it was clever and was good publicity for them to go off of. However, when it was revealed that they were working together from the star t, he lost a lot of suppor t for deceiving audiences and created an apology video. It created discussion about using publicity stunts to adver tise which has been a concept as earl y as the “Torches of Freedom” campaign led by Edward Bernay at the American Tobacco Company in 1929 to hel p with the liberation of women smoking but ultimatel y to encourage women to smoke their tobacco products. It begs the question: how much pressure do these companies face to have to go through such extreme measures to simpl y adver tise themsel ves?
Modern marketing increasingly prioritizes capturing attention over accurately representing products, raising concerns about whether companies are sacrificing quality in favor of publicity. While advertising has always aimed to inform and persuade, today’s strategies often blur the line between promotion and exploitation. One major tactic is the use of celebrities to endorse products. Hiring high-profile figures is extremely expensive, and those costs are often recouped through consumers. In industries like gambling apps, for example, companies rely on heavy user spending to afford endorsements from wealthy public figures. This suggests that, rather than investing in improving the product itself, companies may focus more on visibility and appeal. Even outside of gambling, many industries exploit users’ time and attention instead of their money directly.
creators themselves. These teams design campaigns based not on the product’s true content, but on what will attract the largest audience. For instance, the book covers in the romance genre are frequently designed to appeal to young adults, regardless of what the story entails and its maturity level. This is relevant for the libraries of secondary school districts holding inappropriate books to lend out to minors. Similarly, some films are not advertised faithfully because the creator is not always involved in the process of making trailers. Movies like Wonka and Mean Girls avoided presenting themselves as musicals due to the genre’s mixed reception. In these cases, marketing does not accurately represent it but is misconstruing themselves to expand their target This shift is especially evident in audience. entertainment marketing. Advertisements for movies and books At the same time, advertisements are often shaped more by marketing are becoming more personalized. Many apps now ask users for teams than by permission to track activity across other apps and websites. In some cases, tracking settings were historically enabled by default, leaving it up to the user to understand and disable them.
These systems help “perfect the algorithm,” allowing companies to predict interests with remarkable accuracy. An ad that appears in moments after you search for or talk about a product can feel invasive. Whether this precision results from advanced data aggregation or highly refined pattern recognition, it demonstrates how informed advertising has evolved. Ultimately, advertisements are no longer limited to commercial breaks or roadside billboards. They are embedded in social media feeds, woven into entertainment, disguised as personal recommendations, and powered by algorithms that learn from our behavior. The question is not just how many advertisements we see each day, but how many we fail to recognize. To conclude, modern marketing strategies often prioritize influence over honesty, shifting focus away from product quality. As advertising becomes more targeted and immersive, consumers must question not only what they are being sold, but how and why it is being presented to them.
References: “Ad Breaks on TV Lengthen Breaks from TV.” Oxford Leader, oxfordleader.com/articles/archive/ad-breaks-on-tv-lengthen-breaks-from-tv/. “Average One-Way Travel Time to Work Rises.” United States Census Bureau, 2021, census.gov/newsroom/press-releases/2021/one-way-travel-time-towork-rises.html. “Billboard Faces per Highway Mile.” Scenic America, scenic.org/why-scenicconservation/billboards-and-sign-control/billboard-facts-by-state/billboardfacts-comparative-charts/billboard-faces-per-highway-mile/. “Brand Messaging and the 5,000 Ads Myth.” Adrenalinex, adrenalinex.com/blog/brand-messaging-and-5000-myth/.
Air Pollution and Trees What the Data Tells Us? By Danny and Anh Pham Air pollution affects human health and the environment. It is not only caused by factories or large-scale industry, but also can come from daily activities such as cooking, cleaning, or burning materials. We will use air quality to show how these activities affect the air and why greener spaces matter. This bar chart shows how often each air-quality situation appeared in the dataset: normal conditions, preparing meals, smoke, and cleaning. The most common categories are normal conditions, cooking, and cleaning, while smoke appears less often.
Normal Environmental
Preparing Meals
Presence of Smoke
Cleaning
Most observations come from normal conditions, cooking, and cleaning activities. Noticeably, smoke-related activities appear much less frequently in the data. This suggests that smoke events may be less common, while everyday activities such as cooking and cleaning happen more often and can contribute to changes in air quality over time. In this way, the graph shows that air pollution is shaped not only by factories and industry, but also by frequent daily behaviors. Overall, this highlights why it is important to think about solutions that improve both pollution sources and the environments where people live.
Air pollution is rarely caused by only one factor at a time. Different air-quality measures often change together, which means pollution can build as a pattern rather than as one isolated problem.
This graph shows how air-quality measures relate to one another. Stronger relationships suggest that some pollutants or conditions tend to be related
This visualization suggests that air pollution is more complex than a single source or pollutant. Some air-quality measures appear to move together, showing that changes in the air often happen as part of a larger pattern. This matters because people are usually exposed to a mixture of conditions rather than one pollutant alone. Together with the first graph, this shows that air pollution is shaped by both daily activities and broader environmental patterns. Because of this, solutions should focus not only on pollution sources, but also on creating healthier environments overall.
Why Green Spaces Matter Air pollution affects the places where people live, study, and move. Because of that, solutions should focus not only on pollution sources, but also on improving the environments around us. Green spaces matter because they can support cleaner, cooler, and healthier communities (U.S. Environmental Protection Agency, 2025; World Health Organization, 2025).
Green spaces do more than improve appearance. They help create healthier surroundings by cooling urban areas and supporting better air conditions. EPA notes that trees and vegetation can improve air quality and reduce heat, while WHO highlights the value of greener urban spaces for healthier cities. In this way, green spaces are not a complete solution on their own, but they are an important part of a broader response to air pollution and urban health (U.S. Environmental Protection Agency, 2025; World Health Organization, 2025).
Solutions for Healthier Air Cleaner air requires both environmental design and human action. Green spaces are one part of the solution because trees and vegetation can help cool urban areas and support better surrounding conditions. At the same time, improving air quality also depends on reducing pollution at the source and creating healthier daily environments (U.S. Environmental Protection Agency, 2025; World Health Organization, 2024).
Actions We Can Take Protect and expand tree cover in neighborhoods and cities. Add more parks and green buffers near roads, schools, and homes. Reduce pollution through cleaner transportation and energy use. Improve indoor and nearby air conditions through better ventilation and lower everyday emissions. Support urban design that promotes healthier and more sustainable living.
Final takeaway Air pollution is shaped by both everyday activities and broader environmental systems. Because of that, solutions should address both pollution sources and the spaces where people live. Green spaces are not a complete answer on their own, but they are an important part of creating cleaner, healthier, and more sustainable communities (Shahane, n.d.; U.S. Environmental Protection Agency, 2025; World Health Organization, 2025).
References Shahane, S. (n.d.). Air quality dataset [Data set]. Kaggle. Cahayana, T. M. (n.d.). Air quality prediction using XGBoost & Extra Trees [Kaggle notebook]. Kaggle. U.S. Environmental Protection Agency. (2025, May 27). Benefits of trees and vegetation. World Health Organization. (2024, October 24). Ambient (outdoor) air pollution. World Health Organization. (2025, June 16). Green and blue spaces for healthier cities and cleaner air [Infographic].
20252026
DAC members Data Ambassador Council Product Developer
Nina Moore (she/her) Nina is a senior from Ponchatoula, LA, who is majoring in Chemical Engineering & Computer Science. Beyond her work in the Data Lab, she is a student worker at Tulane’s Newcomb Children's Childcare Center. She is also a Peer Mentor and an undergraduate researcher in the Montemore Lab in the Chemical and Biomolecular Engineering program. Nina is interested in process automation, data analytics, and cars. In her free time, she also enjoys learning Spanish and is at a B1 level. She also enjoys spending time with her family and friends, reading books, and traveling. After college, she will work at Syngenta in their Production and Supply Development Program as an Engineer!
Data Ambassador
Addie Carmouche (she/her) Addie is a sophomore majoring in political economy and minoring in public health with hopes of pursuing a career in public health policy. She is from Baton Rouge, Louisiana, and joined CAIDS to get experience in data science, most enjoying the project collaboration process that the CAIDS lab fosters and promotes.
Data Ambassador
Keira Almazan (she/her) Keira Almazan is a sophomore from San Antonio, Texas. She is majoring in Sociology and pursuing a Data Science Minor as well as a Creative Industries Certificate. She joined CAIDS because after taking DATA1010 with Dr. Howard, she was inspired and realized how important data was to everyday life as well as to her major in Sociology. Her career aspirations are a little broad as she’s open to anything. Currently, she’s looking into Marketing and/or a career field in data, and privacy/public policy.
Data Ambassador
Julia Monconduit(she/her) Julia is a junior pursuing a degree in both Legal Studies in Business and English, and she hopes to pursue law school to become an attorney within the public policy sector. As for Julia’s personal background, though Julia grew up in New Orleans, LA, her family now resides in Suwanee, Georgia, so she’s lucky to have a bit of home in both locations! As for how Julia got involved with CAIDS, upon arrival on campus, she wanted to find ways that she could not only build upon her already present interest, but also find growth opportunities. CAIDS seemed like the perfect opportunity, as it took her interest in data and built upon it to foster analytical and communication skills that will be indispensable in her future career journey.
Data Ambassador
Jayna Syrett(she/her) Jayna is a sophomore majoring in Political Science and minoring in Studio Art, but hopes to add a self-designed major or certificate in Data Science. She joined CAIDS because she loved learning about statistics and coding during high school and wanted to grow her skills in data to apply it to her studies in Political Science. Eventually, she hopes to combine these interests for a career in policy analysis and work on social justice issues.
Data Ambassador
Justin Moore (he/him) Justin Moore is a sophomore majoring in Finance and specializing in Energy while pursuing a Master’s in Accounting through Tulane's 4+1 program. Originally from Gary, Indiana, he has had the privilege of living in New Orleans, Louisiana, for nearly ten years. As a recent addition to the CAIDS community, Justin expressed his desire to join to further develop his data literacy skills, which he believes will be valuable for future career opportunities in the financial services industry. What he values most about CAIDS is the organization’s engagement with the student body through tabling in common areas on campus, hosting events to promote data literacy, and recently delivering class presentations designed to highlight CAIDS opportunities.
Data Ambassador
Jaret Solomon (he/him) Jaret is a Junior majoring in Math and Computer Science. He joined CAIDS because he wanted to be a part of a team that builds the framework for the future of Data at Tulane. His favorite thing about CAIDS is the Students and Professors that he has met along the way. Jaret wants to be an AI Integration Engineer, building the workflows of companies in the age of Artificial Intelligence.
Data Ambassador
Lucy Booth (she/her) Lucy Booth is a junior majoring in International Relations and minoring in Russian and Economics, and is excited for CAIDS to finalize its Data Science minor! Lucy first got involved with CAIDS after her freshman year DATA 1010 class, which she took as a prerequisite for Newcomb requirements. However, she immensely enjoyed the curriculum, as well as the tight-knit CAIDS community led by Dr. Howard. Especially as she’s gotten older, Lucy has really enjoyed the overlapping content between her classes and is especially interested in using data science to analyze the behavior of international institutions and world trade. Lucy aspires to work for an NGO, ideally one that regulates and works with international trade and alliances.
Data Ambassador
Jacob Glazer Fleishman (he/him) Jacob is a Chemical Engineering major from Seattle, WA. He joined CAIDS driven by a fascination with data and its profound impact on every facet of modern life. Within the organization, Jacob most values the creative freedom provided to members, allowing them to dive deep into the specific topics they find most interesting. Looking ahead, he aspires to apply his technical expertise and data-driven mindset to a career in the energy sector.
DATA AMBASSADOR COUNCIL
projects
The DAC supports CAIDS’ mission to enable Tulane students across all disciplines to understand how data shapes our environment by advocating for data literacy through Tulane community engagement. The DAC acts as a focus group that explores data initiatives and strives to increase interaction with CAIDS programming and initiatives.
Data Ambassador Council 2025 - 2026 RECAP Mission Statement: The DAC aims to support CAIDS’ mission to enable Tulane students across all disciplines to understand how data shapes our environment by advocating for data literacy through Tulane community engagement. The DAC acts as a focus group that explores data initiatives and strives to increase interaction with CAIDS programming and initiatives.
QUICK STATS 4 newsletters sent 6 Zine articles written 2 Liaison groups initiated 14 hours spent tabling
Our Year At a Glance ... September 17, 2025: Data Lab Table Setup Party November 5, 2025: Data Lab Opening November 12, 2025: Coded Bias Screening February 4, 2026: Love Data Week Game Night April 13, 2026: Data Showcase April 15, 2026: Zine Reveal
Liaison Groups: This year, the DAC established a new framework to increase CAIDS engagement with the Tulane Community - Liaison Groups. The Liaison Groups create small groups of DAC members who act as externally facing members of the DAC. We established two new Liaison Groups: the Leadership and Outreach Council Leadership Council Assists with CAIDS events, plans class visits to increase engagement with CAIDS, and collates LinkedIN learning trainings to assist in the DAC’s personal development goals Outreach Council Takes the lead in leading social media campaigns, interacting with the student body through polls, and keeping on the lookout for data-themed events that CAIDS members can participate in
Learn more about our programs and initiatives at: https://datainstitute.tulane.edu/ data-ambassador-council
Connolly Alexander Institute for Data Science Data Ambassador Council
Leadership Council Mission Within the Data Ambassador Council, there are workgroups that have been created to ensure that all aspects of the council’s work are maintained throughout the course of the academic year. The Leadership Council, specifically, focuses on increasing awareness of the DAC to the broader Tulane student population. Through scheduled class presentations, tabling, facilitations, and planned events, the Leadership Council hopes to continue making the resources of the DAC available to all students. To do so, awareness is necessary, and the crux of the work of the council is in increasing it.
G oa l s To create a list of professional development training modules for DAC employees. To develop a playlist for tabling to encourage more students to participate; have at least ten people stop by the table. To work with the Communication Specialist to be more involved in Social Media Campaigns. To assist with the development of our Zine.
To present CAIDS opportunities to at least one DATA 1010 class.
Looking for ways to get involved? Instagram: @tulanecaids
o
Ac c o m p l i s h m e n t s
Created a compilation of LinkedIn Learning Modules to facilitate student professional development amongst CAIDS student workers. Conducted various tabling sessions to increase awareness of CAIDS and its resources. Completed a slideshow to showcase the Council during in-class info presentations and other opportunities.
DAC Liason Group | Tulane University
OUTREACH COUNCIL 2025-26 Recap
Mission Statement: In support of CAIDS' vision and mission to "enable Tulane students across all disciplines to understand how data shapes our environment", The Outreach Council was created to maintain partnerships with other student organizations and aims to focus on projects related to data through a social justice lens.
Goals:
Events in the works...
Have 3 data spotlights Continuously update the CAIDS newsletter Maintain communication with the Hullaballoo
Q&A w/ a local data company Plan a data competition that is open to the broader student community
Meet the Team:
Julia Monconduit
Keira Almazan
Jayna Syrett
Jaret Solomon
Jacob Glazer
Hours spent... 15 Tabling on Campus
Meeting
Questions?
Email: caids@tulane.edu Instagram: @tulanecaids
Attending Events
Working
DATA AMBASSADOR COUNCIL
ARTICLES
Members of the Data Ambassador Council wrote and designed these articles that showcase how technology and data affect everyday life. In these articles students examine ethical AI usage, data privacy legislation, machines that make computer chips, data science degrees, and hazarad analysis.
An Introduction to AI Usage Ethics By Addie Carmouche and Jayna Syrett Introduction Artificial Intelligence (AI) technology may feel like it is becoming an increasingly unavoidable aspect of academia and interacting with the internet. Using AI can range from ChatGPTing an essay, creating visualizations for a class, getting math assistance, using AI for conversational purposes, making visual “art”, etc. Regardless of what you are using it for, you should be aware of how your usage impacts machine learning, the environment, and funding corporations. Some of the ethical considerations you should know about before using AI that will be expanded on in this article: intellectual property rights, AI and the propagation of nonconsensual explicit digital forgeries, the environment, and your own intellectual integrity.
Exploitive Explicit Material One ethical aspect of AI to consider is the dangerous role that it plays in the realm of pornographic material. Because of AI, the prevalence of explicit deepfakes (“an image or recording that has been convincingly altered and manipulated to misrepresent someone as doing or saying something that was not actually done or said” (Merriam-Webster).) has drastically increased. Subsequently, deepfake child pornography is increasing: reports of AIgenerated child pornography rose 1,325% between 2023 and 2024 (National Center for Missing and Exploited Children 2025). Safety and restrictive legislation is far behind the advancement of AI technology, and the first law to include restrictions on any kind of nonconsensual sexual AI material was not passed until May of 2025 (Take it Down Act), and is insufficient in providing victims a path to justice. OpenAI has announced that starting in December of 2025, they will begin allowing usage for adult and erotica content, including the creation of imagery. This increases the concern for child pornography, impersonation, and other forms sexual violence. Additionally, it also allows for a safe space for the advancement of dangerous/pedophilic behavior, explicit content exposure to minors, and other mental health concerns. All of these concerns have already been an issue, with just some examples being the prevalence of celebrity deepfakes or cases of chatbot assistance in suicides. With increased accessibility, the dangers of AI generated explicit content will become even more widespread. Intellectual Property: An Interview with Tulane Professor Chelsea Stieber Intellectual Property is a product that results from creativity and/or academic thought and research. Many laws related to copyrights involve the idea of personal intellectual property, but the formulation of generative AI models have caused AI companies to illegally obtain literary resources to train their models to emulate human speech. These materials were taken from the internet without the consent or compensation of the authors. This issue has affected many academic authors, one of which is a Tulane Professor of French and Italian, who had her book stolen to contribute to OpenAI’s generative language model.
Professor Stieber holds a staunchly positive stance "of radical open access, especially in scholarly settings where there is a massive disparity between institutions in the "global north" and the rest of the world." Acting on this stance, Stieber published her book and ensured that everyone would have easy and free access to engage with the content and language of her product. This act of educational inclusivity was ultimately exploited by OpenAI when this LLM model stole her work to train its generative AI model without the hindrance of paying for or acquiring consent to use this content in its training. The difference between AI utilizing her book and it being openly accessible on the internet is that the AI generative model directly counteracts the purpose behind making her book open access as well as her teaching philosophy and motivation: to build critical thinking skills through reading, writing, and discussion. Furthermore, this AI generative model cannot be trusted with articulating an accurate literary analysis of the nuanced, sensitive racial and political content of this book if students are using AI to learn about the content of it. Overall, while Stieber promotes open access to educational material, she finds the theft and use of her book affronting to her motivation of education and critical thinking because OpenAI's generative model shortcuts reading, writing, and discussion for the sake of efficiency at the cost of proficiency. Anti-Intellectualism Movement Transitioning from academia to the workforce is a future problem being perpetuated by current AI practices that offer an opt-out on critical thinking and genuine research. It’s easy to get caught up in the tedium of a student lifestyle, so it’s tempting to use AI as a shortcut on many seemingly low-stakes assignments and assessments, but when these intellectual cop outs add up, concerns about students’ abilities to put their degree to work in professional settings are raised. The abundant use of AI as a crutch in academic settings indicates an unwillingness to learn and apply knowledge as well as an inability to put aside the comfort of escaping schoolwork in the name of intellectualism and integrity. In junction with these intellectual concerns, there is a more nuanced position on AI use to include consideration of impoverished countries and individuals. While many are privileged with consistent access to an abundance of educational tools, this isn’t the case for many people around the globe. With this in mind, it is also important to acknowledge that there is a benefit to AI usage in certain circumstances for supplemental education and education accessibility. Environmental Concerns Conscientious efforts and progress in reversing the damage we caused through fossil fuel usage and urbanization (MIT) will not continue if we become complacent in allowing history to repeat itself by treating resources as anything but scarce and necessary for human survival. There has been an abundance of discourse about AI usage and how much water it uses. At first, the biggest environmental claim with the most power was water usage, with other studies combatting this with the claim that in comparison to many other practices, AI water waste isn’t as significant.
In addition to perspectives on water usage, there are many other extremely impactful consequences of AI data centers on the environment. For instance, the enormous amount of land that data centers require will significantly reduce the amount of farmland available in states. Another concern beyond water usage is energy use and on a note closer to Tulane, “a newer, AIfocused ‘hyperscale’ data center can use as much power as 100,000 homes or more. Meta’s Hyperion data center in Louisiana, for example, is expected to draw more than twice the power of the entire city of New Orleans once completed” (Lincoln Institute of Land Policy). The enormous energy and water use is only one way that communities with AI data centers are impacted. As explained by Techpolicy, “data centers moving into predominantly communities of color and lowincome residential areas can exacerbate historical trends that overlook community health for industrial needs; choosing locations as sacrifice zones, creating public harm for private gain that disproportionately affects minority health in the name of technological innovation.” Counterperspective AI usage is a nuanced issue, and it is equally important to examine if there are instances in which the benefits of AI outweigh the consequences. AI has the potential to improve healthcare access and increase general accessibility for a variety of purposes. In the context of healthcare, Artificial intelligence (AI) offers promising solutions to address these gaps by enhancing diagnostic accuracy, improving disease prediction, and optimizing treatment management (National Library of Medicine). In the United States, this kind of AI usage could help minimize the impacts of health deserts. However, it is important to consider that approximately 80% of the country lives in healthcare deserts (The Ohio Council). This raises the question of whether AI resources should be more than a small-scale and temporary solution as we concert more resources to address this harmful, nation-wide issue. When examining accessibility, AI usage for sign language applications, blind navigation, and education access are just some of the ways that this technology could be used. Conclusion As the presence of AI in our daily lives increases, interacting with this tool may feel unavoidable. However, those who have the privilege to be reading this article as a Tulane student, faculty, or person within the sphere of higher education, likely have the privilege to choose alternate tools and processes. Ultimately, the ethical evaluation of AI usage is subjective, but this brief overview of ethical considerations regarding pornographic material, anti-intellectualism, and environmental implications are only skimming the surface of the pool of negative impacts that AI usage has. Interacting with generative AI (creating content) compared to predictive AI (analyzing past
information) generally worsens the negative impacts outlined above. Hopefully, the considerations explained in this article implores readers to continue learning about the implications and ethical concerns of AI usage and to create a plan to minimize AI usage. *Please keep in mind that this article is based on information and discourse around AI at the time it was written (2025). The scope and context surrounding artificial intelligence is rapidly changing, so the ideas discussed will likely have more information available to support or falsify the article’s claims. We invite you to pursue your AI ethics education beyond this article*
Works Cited 119th Congress (2025-2026): Take it down act. Congress.gov. (n.d.). https://www.congress.gov/bill/119th-congress/senate-bill/146/text Davis, P. (2025, April 9). Spike in online crimes against children a “wake-up call.” National Center for Missing & Exploited Children. https://www.missingkids.org/blog/2025/spike-in-online-crimes-againstchildren-a- wake-up-call Jennifer Chu | MIT News. (n.d.). Study: The Ozone Hole is healing, thanks to global reduction of cfcs. MIT News | Massachusetts Institute of Technology. https://news.mit.edu/2025/study-healing-ozonehole-global- reduction-cfcs-0305 Merriam-Webster. (n.d.). America’s most trusted dictionary. Merriam-Webster. https://www.merriam-webster.com/ Marrinan, C. (2025, June 12). Data Center Boom Risks Health of already vulnerable communities. Tech Policy Press. https://www.techpolicy.press/data-center-boom-risks-health-of-alreadyvulnerable-communities/ Nivethitha, V., Daniel, R. A., Surya, B. N., & Logeswari, G. (2025). Empowering public health: Leveraging AI for early detection, treatment, and disease prevention in communities - A scoping review. Journal of postgraduate medicine, 71(2), 74–81. https://doi.org/10.4103/jpgm.jpgm_634_24 Wroth, K. (2025, October 23). Data drain: The land and water impacts of the Ai Boom. Lincoln Institute of Land Policy. https://www.lincolninst.edu/publications/land-lines-magazine/articles/landwater-impacts- data-centers/
How One Machine Props Up the Entire Global Economy An overview of the world's most important machine, ASML’s EUV lithography machines Jacob Glazer Fleishman Semiconductors and computer chips are everywhere. I’m sure hundreds of chips just helped me write that sentence. The process of making them is a global effort. Materials, knowledge, and expertise from every corner of the world are poured into every chip. There are many bottlenecks in this global supply chain. Almost all manufacturing is done in Taiwan, by TSMC. Nearly all of the designs are created and engineered in Silicon Valley, California. These bottlenecks are well-documented and understood. In their wake, some of the most powerful companies ever established have stepped up to fill these crucial roles. Apple, Nvidia, and TSMC are three of the largest and most influential companies in global history, with market caps well above $1 trillion dollars. There is one more bottleneck that doesn’t receive nearly enough attention, but without it, the whole system would grind to a halt. This is ASML and its advanced lithography machines.
What Is ASML and Lithography Machines? ASML is a Dutch tech company—now the most valuable company in all of Europe—and it has a monopoly on advanced EUV (extreme ultraviolet light) lithography machines. These machines are some of the most advanced things humans have ever produced. They are the size of a small bus, and just to get your hands on one would cost you well over $300 million dollars. Each machine has well over a hundred thousand parts, and shipping just one requires 20 trucks and three Boeing 747s if it is needed to go overseas. Due to these extreme logistics, ASML does nearly 90% of its business with just Intel, TSMC, and Samsung—the three largest computer chip manufacturers in the world.
How Does Computer Chip Manufacturing Work and Where Does ASML Fit In? In the simplest terms possible, this machine prints and inscribes incredibly small designs on silicon to make the material suitable for microchips. ASML breaks this down into five “simple” steps. 1. Deposition is the first step. This involves preparing the silicon wafer. Wafers need to be incredibly smooth; even microscopic flaws can be critical in microchip production. The wafers also shrink by small increments during different parts of the EUV process. This shrinkage must be accounted for in the initial wafer construction. 2. Photoresist: Photoresist is made up of two things: the positive resist and negative resist. When the positive version is struck by the EUV light, the thin film reacts with the concentrated light and silicon wafer, making the silicon wafer more soluble. This prepares it for etching and creates microscopic grooves that eventually become the computer chips. Negative resist does the exact opposite, making the silicon tougher and less soluble through an equal but opposite reaction. This process of perfectly coating the silicon with the two separate types of photoresists is one of the hardest steps in the entire process 3. EUV Light: Next is the part ASML actually handles. Large arrays of silicon wafers and photoresist are loaded into the EUV machines. Molten tin is vaporized by lasers, creating extremely concentrated EUV light. This beam is then focused through a series of lenses, filters, and mirrors. ASML describes these mirrors as “the flattest surface in the world.” Eventually, this light hits the wafers and reacts with the two types of photoresists.
Image 1 shows a rendering of what the EUV light would look like as it gets reflected through the series of mirrors and filters and concentrated onto the silicon wafer.
Image 2 shows a full-scale ASML lithography machine with two workers included to give a sense of scale. Less than 200 of these machines exist globally. .
4. Etching Process: This is made up of two steps. Chips are put into a chemical bath that helps remove unwanted material, such as remaining photoresist. Additionally, the chips are baked in specific environments to solidify their strength and integrity. These steps can be done interchangeably through various manufacturing processes.
5. Ion Implantation: The now fully manufactured chips are hit with a series of either positive or negative ions meant to tune them to be as perfect of an insulator and conductor as possible.
Future Outlook Without ASML, we would live in a vastly different world. ASML has far exceeded what it means to be a successful company and transcended into a strategic global necessity. Without ASMLs machines, the advanced computer chips we rely on and use hundreds of times every day would be more expensive and harder to replace. This boom in cheap, reliable and ever-advancing chip infrastructure has made the meteoric rise in AI computing and everyday AI use possible. The world's 7 largest companies (Nvidia, Apple, Microsoft, Amazon, Google, Broadcom and Meta) now make up a staggering 36% (!) of the S&P 500. It is painfully obvious just how detrimental any slowdown in the microchip supply chain could be to these 7 companies. Any reduction in ASML’s production quality and or quantity can have real and lasting impacts on the entire global market. ASML continues to push the boundaries of advanced lithography engineering. They have invested heavily in R&D for specialized mirrors and electric light beams. Though impossible to know for sure, some say ASML is decades ahead of its closest competitors within the EUV industry. Some processes ASML has mastered are still complete mysteries to the rest of the world. ASML will continue to grow into a global powerhouse; it is quietly already the 20th most valuable company in the world. With singular control and expertise over the most valuable manufacturing process the world has ever seen, this number will only rise.
DATA PRIVACY LAWS: US Privacy Act 1974 Fair Credit Reporting Act 1970
HIPAA 1996 General Data Protection Regulation (GDPR)
2018 ??? 2026
Data Privacy Laws: What’s Next? By Julia Monconduit
In a day and age where technology has become so prevalent in everyday life, there are concerns for how the data input into our technology, whether searching up a simple question on Google or utilizing AI tools, is then used after it is released into the broader public domain. Within the United States, data privacy laws cover a broad array of daily life. Whether it be the data collected when you go to the doctor’s office or even the financial information you provide when applying for a credit card, data is constantly being input and released into the greater environment for just anybody to access. Compared to the country’s counterparts in the European Union, the United States has not progressed in the same way in the world of data. This article will delve deeper into the details of the privacy laws that protect the citizens of the United States at this moment in time and how they compare to the nation’s counterparts in the European Union (EU) The Privacy Act of 1974 is arguably one of the most prevalent data privacy laws, as it “governs how federal agencies can collect and use data about individuals in its system of records.” Requiring the written consent of an individual prior to disclosing personal information in any capacity, the act allows individuals the opportunity to protect their own interests. If the individual reserves the right to request a change to their records or even to view the records in question, they will be protected against “unwarranted invasion of their privacy.” This is why this law is so important. It provides individuals with the autonomy to use their data in the way that they wish to use it, rather than leaving the decision to the government or any of its agencies. This is so important in a time when much data has been collected from individuals, including biometric data or otherwise. Setting boundaries for how the government and other entities can utilize this data is not only important but necessary to determine in an ever-evolving society. More recently, the American Privacy Rights Act of 2024 has worked to provide the public with a national framework for how data collected through consumers can be used to benefit companies or other entities that would profit from the data they receive. Prior to the introduction of this bill into the House of Representatives, there was no talk of a national standard to be developed for how data was to be collected. As one can expect, the lack of this standard can lead to confusion amongst the states as to how the laws should be enforced and if they have the authority to decide on that concern. In addition to the unified expectation that the American Privacy Rights Act of 2024 has provided for consideration, it has also addressed more recent technological advancements, like Artificial Intelligence (AI). As the use of AI has become almost commonplace for college students and professionals alike, it is more important now than ever that an expectation be set for how privacy is still enforced for all individuals, even while using a framework that takes in information and essentially makes it its own to be used countless times as requests go in and responses are spit out. As stated in H.R. 8818 - American Privacy Rights Act of 2024, the goal of the introduction of this bill is “[t]o provide Americans with
foundational data privacy rights, create strong oversight mechanisms, and establish meaningful enforcement, and for other purposes.” With much to look forward to in the formation of a unified policy regarding privacy law, one can also consider the implications of a lack of such a law being present in the United States. With each individual leaving a digital footprint, whether through social media or simply the texts they send back and forth with a friend, all of this information is accessible by governments, corporations, and even cybercriminals. Each of them have the opportunity to exploit the lack of a unified privacy law, and with the approval of bills like the American Privacy Rights Act of 2024, this can be prevented, keeping those with ulterior motives for the use of this information at bay.
Citations Aidun, Elias. “Data Privacy in the Digital Age: A Comparative Analysis of U.S. and EU Regulations.” University of Cincinnati Law Review, 2025, https://uclawreview.org/2025/03/05/data-privacy-in-the-digital-age-a-comparative-analysi s-of-u-s-and-eu-regulations/. “American Privacy Rights Act Cheat Sheet,” iapp25, June 2024, https://iapp.org/resources/article/american-privacy-rights-act-cheat-sheet/. “Data Protection in the United States.” DLA Piper, DLA Piper, 2025, https://www.dlapiperdataprotection.com/?c=US. Murray, Connor. “U.S. Data Privacy Protection Laws: A Comprehensive Guide.” Forbes, 21 April, 2023, https://www.forbes.com/sites/conormurray/2023/04/21/us-data-privacy-protection-laws-a -comprehensive-guide/. "Text - H.R.8818 - 118th Congress (2023-2024): American Privacy Rights Act of 2024." Congress.gov, Library of Congress, 25 June 2024, https://www.congress.gov/bill/118th-congress/house-bill/8818/text.” Wright, Verrion. “The American Privacy Rights Act (APRA): Everything You Need To Know.” Bigid, 16 April, 2024, https://bigid.com/blog/american-privacy-rights-act-apra-guide/?utm_campaign=US/NA++Data+Privacy&utm_source=google&utm_medium=cpc&utm_term=personal%20infor mation%20privacy%20act&hsa_ver=3&hsa_cam=19933445200&hsa_grp=14784996815 6&hsa_net=adwords&hsa_ad=706592949357&hsa_kw=personal%20information%20pri vacy%20act&hsa_mt=e&hsa_acc=9924524058&hsa_src=g&hsa_tgt=kwd-35205103910 1&gad_source=1&gad_campaignid=19933445200&gbraid=0AAAAADP9WRGmw0uwj
Data Science Is it the best degree in an era of AI? By Keira Almazan Abstract:
With AI in arguably its most transformative and progressive era, it has increased presence within the workforce. Many corporate institutions are working on their own AI system or integrating it into their place of work. The value of both AI and Data literacy has significantly increased in the past couple of years. Every company wants to stay up to date or ahead of the game. Hiring people with the knowledge to get them there has become pertinent. Yet, has this been reflected in undergrad college education? Have undergrads demonstrated growing interest in Data? There has been a rise in the importance of Data literacy in the job market for years now. However, with the overwhelming new era of AI in the job market it has become paramount. Students and parents have definitely caught on to this and demonstrated concern for their education. I am a tour guide for Tulane University. Every tour we encourage questions to be asked. A common question, particularly this school semester (Fall 2025), has been “are there any AI courses or Data focused curriculum”? Once one asks it they all become entranced by what I have to say in response. I tell them about CAIDS and our curriculum offered. Outside of Tulane though, many colleges and universities have begun integrating AI into their curricula and Data Initiatives. These institutions are working to prepare their students with the Data literacy skills needed for the future job market. Will Data and AI curricula truly benefit students? Researchers have confirmed that AI literacy positively impacts academic well-being and attainment. This research was done on undergrad students in China and Iran. Because AI improvement is international, this research is promising for US statements. It also reveals that on a global level AI literacy will give students an edge. One which will potentially guarantee them more opportunities than those who received no education of Data/ AI literacy. Even just on a national level since we are seeing an increasing demand for those who retain these skills. Students will need to at least take a couple classes on these topics to be prepared for hire.
Conclusion: So does this mean a degree in Data Science is the best to get in these times? Not necessarily. I’d argue that just a degree in the general field of Data and AI would be. Like if it has a different title, but curricula still covers AI and Data literacy. If one isn’t offered as a major, perhaps look into gaining a certificate or minor. Even joining some type of Data oriented club/ Org would significantly stick out on people’s resume’s. I do believe it’s imperative for students currently in undergrad to go out of their way to find classes to teach them these skills. It doesn’t necessarily have to be your major, however it would come in Handy if it were. ResourcesKim, Jeonghyun, et al. “Toward Measuring Data Literacy for Higher Education: Developing and Validating a Data Literacy Self‐efficacy Scale.” Journal of the American Society for Information Science and Technology, vol. 75, no. 8, 2024, pp. 916–31, https://doi.org/10.1002/asi.24934. Xiao, Jingyu, et al. “How AI Literacy Affects Students’ Educational Attainment in Online Learning: Testing a Structural Equation Model in Higher Education Context.” International Review of Research in Open and Distance Learning, vol. 25, no. 3, 2024, pp. 179–98, https://doi.org/10.19173/irrodl.v25i3.7720.
The Use of Artificial Intelligence in Process Hazard Analysis: Hazop Studies By: Nina Moore The concept of Artificial Intelligence (AI) has been evolving since the 1950s. AI encompasses any technique that enables computers to simulate human cognition and intelligence. The field’s immense capabilities can therefore be used in the chemical process industries (CPI). CPI transforms raw materials into valuable products through chemical and physical reactions, involving a series of steps like reaction, separation, and purification. The reactions to accomplish these outcomes can pose a threat if the checks/controls and safety equipment fail in their operation. Therefore, there is a continual need for high-quality process safety management (PSM) studies to prevent incidents and ensure the protection of people, assets, and the environment. This article will discuss a specific type of PSM study, HAZOP studies, and how AI can be leveraged to achieve quick and high-quality results in HAZOP studies within the CPI. Process Hazard Analysis (PHA) studies involve the identification, evaluation, and control of hazards in complex processes. A widely recognized and popular PHA study is a HAZOP study. HAZOP studies involve the examination of deviations from the design intent of chemical processes. HAZOP studies are performed during the front-end engineering design (FEED) stage of a new project when the detailed design is complete but before construction begins. HAZOP studies are conducted with the assistance of multiple personnel. During these studies, multiple documents detailing the process and operating conditions are obtained. The process is divided into smaller parts, called nodes, where each node is examined to determine deviations from the intended operational function. For each identified deviation, the team assesses the potential causes, the worst-case consequences, and the existing safeguards in place to prevent or mitigate the scenario. Current safeguards are examined to see if further safeguards are needed. All possible deviations, their causes, consequences, and safeguards are then documented for later use. As per the U.S Occupational Safety and Health Administration (OSHA), the regulatory body requires that PHAs be performed every 5 years. Due to the needed manpower to perform these PHAs, sparing personnel when the plant is running creates a challenging situation and barrier to daily plant maintenance. Therefore, the use of AI can help overcome these barriers.
AI in HAZOP studies can only be implemented after quality-labeled data has been gathered and trained personnel have been assembled. Below is a suggested plan of action for implementing AI into HAZOP Studies (1)
Select the Quality HAZOP Report
Select a Suitable AI Tool
Start with PHA (HAZOP) Revlidation to gain condifence
Develop Internal Team to Provide Feedback and Train AI Module
Implement Continuous Improvement Plans
Use Lessons Learned to train other AI modules
However, with the implementation of AI in HAZOP studies, quality-labeled data should be obtained, and the appropriate AI tools needed should be obtained and trained on the data. This introduces the need for quality personnel to be trained on the AI models. Some cons to implementing AI in HAZOP studies include a high implementation cost and potential bias from the training data if it is outdated or inaccurate. In addition, AI models and results can produce inaccurate values, which can have a detrimental effect on the process and the company. Care should be taken to evaluate the validity of the results produced by the AI models. In conclusion, the use of AI can enhance process safety studies provided that high-quality data can be obtained and the proper personnel are trained on the required AI software. Developing a competent team and human intelligence with AI will maximize the potential for success of Implementing AI in process safety (1). Resources 1. https://www.aiche.org/resources/publications/cep/2024/august/implementing-artificialintelligence-process-safety-studies
THE DATA BEHIND YOUR BINGE By: Jaret Solomon & Lucy Booth
In the modern digital economy, data is often compared to oil, a valuable resource that fuels innovation. However, like crude oil, raw data is messy, unrefined, and largely useless in its natural state. Every time you click or scroll, you generate data. For a business, the challenge isn’t just collecting this information; it’s making sense of it quickly enough to act. This is where automation enters the picture.
To bridge this large gap between raw chaos and actionable insight, companies utilize a process called ETL (Extract data, Transform data, Load data). This automated pipeline is the backbone supporting many of the services we use daily. This article explores how ETL pipelines function as the backbone of business automation, using Netflix as a primary case study to demonstrate how these systems turn billions of unstructured data points into a personalized user experience.
What is anWhat ETL Pipeline? For those new to data literacy, ETL stands for Extract, Transform, and Load. Think of it like a professional kitchen taking all of their raw ingredients, and cultivating a meal:
Extract This is the gathering phase. Data is pulled from various sources: logs, sales databases, or external websites. Similar to how a chef buys vegetables from one vendor and meat from another, businesses gather data from many different places.
Transform This is the most critical step. Raw data is often full of errors or is in the wrong format. During this phase, algorithms clean, sort, and transform the data into a standardized format. It’s like washing, chopping, and cooking the ingredients so they are safe and ready to eat.
Load Finally, the clean, processed data is stored in a destination where it can be accessed by the later phases of the process. This is similar to handing the plate to the wait staff and sending him on his way to the customer.
Netflix’s "Muse" and Content Personalization Netflix is arguably the gold standard for integrating automation using an ETL pipeline. With just over 200 million subscribers, the company does not have enough manpower to manually curate recommendations for every user. Instead, they rely on automated pipelines to make millions of decisions in real-time. A prime example is Netflix’s Muse system, which drives their data-driven creative insights. In fact, Netflix doesn't just recommend titles to you; it generates the artwork you see for those titles, according to your preferences.
HOW THE PIPELINE What WORKS AT NETFLIX Extract The pipeline pulls raw interaction data from their database. This can be anything from what titles you clicked on, your viewing history, or even how long you spent selecting a show.
Transform The system then aggregates this data to identify patterns. For example, if you frequently watch romcoms, the pipeline might assign you a title with artwork featuring a couple. Or if you consistently watch thrillers, the data might recommend titles similar to those that you watched.
BENEFITSBENEFITS & DRAWBACKS & DRAWBACKS
The integration of ETL automation offers immense benefits. It allows businesses to scale operations without the need for more employees as their user base grows. However, there are challenges. Implementing robust ETL pipelines requires significant upfront investment in cloud infrastructure and technical personnel. Additionally, there is the risk of "garbage in, garbage out." If the extraction phase pulls in biased or low-quality data, the automated transformation will simply reinforce those biases at scale. Companies must implement strict validation checks to ensure the integrity of their pipeline, maintaining professionalism and credibility.
CONCLUSION
Load These personalized preferences are loaded into your user profile, so the next time you open the app it is personalized specifically for your taste.
Works Cited "Data Pipeline Types and Examples." IBM, 2024, www.ibm.com/think/topics/data-pipeline-types. "Scaling Muse: How Netflix Powers Data-Driven Creative Insights." Netflix Tech Blog, 2025, netflixtechblog.com/scaling-muse-how-netflixpowers-data-driven-creative-insights-at-trillion-rowscale-aa9ad326fd77. "What Is An ETL Pipeline? Examples & Tools." Estuary, 2025, estuary.dev/blog/what-is-an-etl-pipeline/. "What Is ETL? - Extract Transform Load Explained." Amazon Web Services, 2024, aws.amazon.com/whatis/etl/.
Integrating automation through ETL pipelines is no longer a luxury for modern businesses; it is a necessity for survival. Cloud providers are introducing Artificial Intelligence based platforms as a way to bridge the gap between technical teams, vs non technical teams who need pipelines. By automating the tedious tasks of data collection and analysis, companies can focus on what matters most, providing value to the customer. For students and future professionals, understanding these pipelines is a foundational data literacy skill. As we move toward an AI-driven future, the ability to build and maintain these automations will be one of the most soughtafter skills in the workforce.
C.A.I.D.S
Other CAIDS Programs
Posters
CAIDS has a host of other programs that contribute to enhancing data literacy. These posters highlight other internships, clubs, and opportunities that provide space for students to practice and teach data skills.
DATA OPERATIONS
The DATA Leadership Interns supports CAIDS by analyzing and researching course data to evaluate effectiveness and inform future curriculum improvements. Led by Dr. Dilks, we are excited to share some of our insights. Clementine Roth and Megan Coveney
MOST STUDENTS REPORT MEANINGFUL IMPROVEMENT IN DATA LITERACY, WITH RESPONSES HEAVILY CONCENTRATED IN “A LOT” AND “A GREAT DEAL,” SUGGESTING DATA 1010 IS HIGHLY EFFECTIVE IN BUILDING CORE DATA SKILLS.
MOST STUDENTS REPORT HIGH CONFIDENCE IN THEIR ABILITY TO ANALYZE DATA AFTER DATA 1010, WITH RESPONSES CONCENTRATED IN “SOMEWHAT CONFIDENT” AND “EXTREMELY CONFIDENT.”
DATA ARE DRAWN FROM A POST-COURSE ANONYMOUS SURVEY COLLECTED ACROSS ALL DATA 1010 SECTIONS (ALL INSTRUCTORS) FROM FALL 2024 - SPRING 2025.
MOST STUDENTS REPORTED IDENTIFYING AS “DATA NEWCOMERS” OR “DATA APPRENTICES” AT THE START OF DATA 1010 AS MEASURED BY THE DATA LITERACY PROJECT QUIZ.
MOST STUDENTS REPORT THAT THEY FALL INTO THE “DATA GURU” CATEGORY, SUGGESTING THAT STUDENTS FEEL MORE COMFORTABLE AND EMPOWERED WHEN HANDLING DATA AFTER COMPLETING DATA 1010.
MOST STUDENTS SHOWED IMPROVEMENT IN THEIR DATA PERSONA, WITH THE LARGEST GROUP (44 STUDENTS) INCREASING AND NONE DECREASING.
Conclusion Across both assessments, we can see a clear shift in confidence engaging with data. This highlights the measurable impact of DATA 1010. As the Ops team continues to evaluate data from DATA 1010, insights will be used to guide the curriculum design in hopes of improving student outcomes and building a campus-wide culture of data literacy.
DATA PERSONA ASSESSMENTS FROM THE DATA LITERACY PROJECT WERE ADMINISTERED AT THE BEGINNING AND END OF DATA 1010 ACROSS ALL OF DR. DILKS’S COURSES FROM FALL 2024 SPRING 2025.
Tulane Data Collective
TUDC Members
Discussion on data, AI, and modern democracy With New York State Assemblyman Steve Stern and former Senator Craig Johnson
The Tulane Data Collective is a club which works alongside CAIDS to bring data literacy and access to students. We host general body meetings as well as fun events such as hackathons and speaker panels. TUDC works to provide information about internships and data opportunities to our members.
Our Annual Symposium Last year we held the first ever annual symposium on the implications of Data and AI. We hosted guest speakers New York State Assemblyman - Steven Stern, and Former New York State Senator - Craig M. Johnson. There was great discussion and students were able to gain first-hand advice and knowledge. This year they will be returning for our second annual symposium on the topic of AI use in politics.
first annual symposium
Data Peer Mentors The Data Peer Mentor program has been instrumental in my academic, professional, and personal journeys by preparing me with the skills to engage in equitable advocacy. As a Data Peer Mentor, I have developed my intellectual and interpersonal skills all the while supporting and learning from brilliant cohorts of peers. Being a data peer mentor has been rewarding and enriching, allowing me to redefine how I approach problemsolving and consider intersectional issues of data equity in higher education and beyond. -Lily Markus, DATA-1010: Introduction to Data & DATA-4030: Data Ethics/ Privacy/ Governance
Through mentoring in Data 1010, I have gained a deeper appreciation for how transformative it can be when students feel supported in their first encounters with data. I have loved helping students who come in feeling intimidated by numbers understand that data is something they can actually use to understand the world around them. One of my favorite experiences has been watching my students realize they can use data analysis tools and create their own visualizations. The role has reinforced my belief that data literacy grows best in collaborative and encouraging environments. - Lia Prados, DATA-1010: Introduction to Data
Being a peer mentor has taught me so much about interacting with my peers, what different learning styles look like, and how to build meaningful relationships with professors. It has also given me an insight into what my professors do daily and more logistical aspects of teaching. One of my favorite experiences is when you get the opportunity to explain a concept one-on-one, and you can see when it clicks for the other person! Megan Cassidy, DATA-1010: Introduction to Data
The most rewarding part of the CAIDS department has been the relationships. Whether it’s a quick question or a deep dive into a tricky problem, I enjoy every interaction. Many of my closest friends at Tulane started as students I assisted, which has made this role far more meaningful than I expected. It’s inspiring to be surrounded by like-minded individuals with such high aspirations Joseph Raphael, DATA-1010: Introduction to Data & Data Peer Mentor Manager
Discrimination, Disparities, and Data Lab (D3L)
Learn more about what Lauren Nelson, a research assistant in our Discrimination, Disparities, and Data Lab (D3L), said about her experience: "Over the past 18 months, I've served as a Research Assistant (RA) for the Discrimination in Access to Mental Health Care: Evidence from an Audit Correspondence Field Experiment project. I was interested in this role because mental health is public health. I've always been very passionate about the social determinants of health, and working on this study gave me the opportunity to gain first-hand insight into how discrimination impacts marginalized groups. While working on this project, I was also pursuing my Master's in Health Administration. As I begin my professional career in healthcare, this experience has only reinforced my passion for health equity and my commitment to dismantling barriers to accessing healthcare. I've absolutely loved my experience working for CAIDS and being a part of the D3L program. As a Master's student, I've been very busy throughout my time here balancing classes and other jobs at the same time, but the flexibility of this role has made it possible. Being able to work remotely and choose my own hours has truly helped me balance my RA responsibilities with my other commitments. It's also been a great experience working alongside people from different academic backgrounds (fun fact: all majors can apply)."
Statistical Consulting Center The Statistical Consulting Center offers free statistical research support to Tulane University sudents and faculty. The consultants have completed over 60+ hours of consultations! They work to guide people through common roadblocks and help them: Learn the basics of statistical software and understand their results Discover the best practices for data collection and management Explore different ways to analyze their data Find the righ resources and references to deepen their understanding of statistics
Catarina Vazquez (left) presents on her research on skeletal collections at the American Anthropological Association’s annual conference. The Statistical Consulting Center helped her with the data analysis for this project, including teaching her how to use R.
Beyond the lab
Posters
While lots of programming happens within the lab, CAIDS promotes data science outside the lab by providing programming for Tulane faculty, staff and students. Student workers at CAIDS also apply their data science skills in a wide range of context outside of the lab.
Office of Academic Excellence & Opportunity’s Dr. King Student Leadership Award Recipient
n a h g e M
a i g Na
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Meghan was recognized as one of 20 students honored during Tulane’s “Spring is King” series, which celebrates Dr. Martin Luther King Jr.’s values of service, civic engagement, and community impact. Nominated by Dr. Jacquelyne Thoni Howard, Meghan was described as “a role model on campus for bridging scholarship, collaboration, and welcoming community building.” Her work as a Product Developer on the Data Research Internship (DRI) team reflects this commitment, supporting digital scholarship projects, and fostering meaningful partnerships across campus and the community.
Tulane Innovation Institute’s Open MIC Night pitch competition Winner
n a d r Jo
s m a i Will Jordan's winning project, NORA (Navigating Obstetric Resources & Access), is an AI-powered maternal health platform designed to address the Black maternal mortality crisis in Louisiana. Accessible via iMessage, NORA flags risk factors in real time and connects underserved patients to the resources they need. Her work reflects the innovative, interdisciplinary spirit of the CAIDS community where data, technology, and social impact come together to drive real change.
LOVE DATA WEEK AWARD a i l u J
r e l l i M Julia won the 2026 Student Data Visualization Contest during Tulane’s Love Data Week. Her project, Food Access and Public Transportation in New Orleans, maps how long it would take to reach food access resources using public transportation and highlights what areas have a difficult time finding food resources. Her visualization showcases how data and data visualization can communicate information effectively and highlight a need for change.
LOVE DATA WEEK EVENTS
Through interactive workshops, visual exhibitions, and a campuswide competition, data literacy was brought to life across campus. Love Data Week empowered the Tulane community with essential skills to analyze information, discover meaningful patterns and apply data responsibly in research and daily decision-making.
CAIDS at
Grace Hopper
From left: Kai Williams, Julia Miller, and Nina Moore
The Grace Hopper Celebration is a conference that focuses on women in tech. Three CAIDS students received a grant from the Newcomb Institute to attend this conference. At the conference, these students were able to hear from leaders in the technology space, meet with top employers in the technology field and beyond, explore data science graduate programs and meet other people navigating careers in the tech industry.
The Data Digest · Spring 2026
Events & Community
FILM SCREENING
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RECAP
Coded Bias Screening On November 12, 2025 @ 4 PM, CAIDS & Tulane Student Body Members joined together for the screening of Coded Bias by Joy Buolamwini
The film follows MIT Media Lab researcher Joy Buolamwini as she uncovers a startling truth that facial recognition algorithms, used by governments and corporations worldwide, are significantly less accurate when identifying women and people with darker skin.
The DAC hosted its first-ever film screening, bringing members together for an evening centered around Coded Bias, a 2020 documentary directed by Shalini Kantayya. Guests settled in with refreshments and movie snacks as the room came alive with anticipation before the film began.
For over 50 years between 1890-1941, the Woman’s Literary Club of Baltimore met weekly to share their writing with the goal of getting it into print. And how they succeeded. Over 1000 publications by club members appeared in books, magazines, and newspapers across the US. Yet in less than 100 years, this astounding group of women—and their writings—have been all but forgotten. Dr. Jean Lee Cole will discuss how she and her students created the online Woman’s Literary Club of Baltimore Archive to restore these women and their writings to literary history, while grappling with the club’s troubling relationships with the Lost Cause, segregation, and feminism.
DATA LAB NEWSLETTER Scan to joIn!
The CAIDS Data Digest is a monthly newsletter that features upcoming events, internship and grant opportunities, course offerings, and student spotlights.
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