Computer Science Magazine - Fall 2014

Computer Science 8 | fall 2014

redefining the realms of possibility

DEEP LEARNING

what machine learning tells us about ourselves PRIVACY AGENTS | ON THE JOB AT FACEBOOK

Dean’s Report Fall 2014 has turned out to be crazy busy – in a good way!

Enrolments in our Faculty continue to rise with students new from high school up 49% over last year. While this is a huge increase, we have the capacity to handle this and more.

Industry has been knocking on our door seeking to hire our students.

Our students are being hired for both co-op and careers from Halifax to Seattle. Unemployment in the ICT industry continues to run at less than 3%.

During this past fall term, we hosted five special speaker events. Dr. Stan Matwin, CRC Tier 1 and Director of the Institute for Big Data Analytics, welcomed students and alumni to his Homecoming lecture, “Big Data: Opportunities and Challenges”. We collaborated with the Dalhousie Art Gallery to host three distinguished artists on Computer Science, New Media and Space as part of the 2014 Killam Lecture Series.

We also hosted our second Distinguished Lecture in Computer Science.

Dr. Yoshua Bengio of Université de Montréal filled the auditorium with his talk, “Deep Learning: The Path to Artificial Intelligence”. This topic coincides nicely with this issue’s cover story, featuring Dr. Thomas Trappenberg and his HAL lab.

Thanks to Cisco Systems, we now have a state-of-the-art networking lab (both hardware and software) for teaching and pursuing research even further in networking and security, an area featured in our Fall 2013 issue with Dr. Nur Zincir-Heywood, Dr. Malcolm Heywood, and their NIMS lab.

As you continue to read, you will see more success stories. There have certainly been more than we can feature in one single column, and even in one magazine!

Any comments or suggestions, drop me an email (shepherd@cs.dal.ca).

Michael Shepherd, Dean Faculty of Computer Science

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Deep Learning and Neural Networks How HAL lab uses machine learning to better understand our own brains Well known companies such as Google, Amazon and Facebook, as well as many smaller tech companies, are hiring computer scientists with backgrounds in machine learning.

Machine learning—the art of teaching machines from data— has matured considerably in the last few years. Such methods are now behind many advanced data mining techniques such as speech recognition on android phones or image search on Google. Indeed, machine learning is a major technique for analyzing big data, a marriage made in digital heaven.

Within machine learning, there is now a new old kid in town named deep learning. Deep learning mostly refers to good old neural networks that were popular in the late 1980s and early 1990s.

Similar to what is now visible, business journals were raving at that time about the possibilities that such methods were bringing to data mining and forecasting.

By the late 1990s, however, it seemed that progress had halted and the level where machines could compete with humans in tasks like object recognition and speech analysis could not be reached.

Methods like causal modeling then took over and neural networks even got a bad name.

While neural networks have been lying low during the following two decades, much progress was made in understanding them. It is now understood that not enough example data and computer power existed in the 1990s to get to the regime where these networks could outperform humans.

Deep learning on the rise The availability of fast graphic processor units (GPUs) has played a big factor in progress. GPUs are very good at crunching numbers in matrix operations behind

most graphic rendering—and neural networks use similar operations. With the help of GPUs, networks can now be trained in three weeks, rather than months on regular workstations.

Big data has also played a major factor in progress. Many companies are collecting lots of data but do not yet know how to use it efficiently. Most basic approaches of neural networks are based on supervised learning where labeled data is needed. Companies have invested in using such data through crowd sourcing and now have databases with millions of pictures that are labeled with thousands of different categories.

All of these advances mean that large neural networks can now be built. Not only can these networks be large, they can now have many stages of representations of the data.

These many layers are what deep learning is all about. Deep networks are now winning many different data mining competitions, resulting in new state of the art approaches to things like speech recognition and computer vision.

“It is certainly an exciting field where new applications are within reach,” says Dr. Thomas Trappenberg.

HAL Lab Dr. Thomas Trappenberg of the Faculty of Computer Science, runs the Hierarchical Anticipatory Learning (HAL) Lab. The HAL Lab works in three areas that are essentially connected: computational neuroscience, machine learning and robotics.

“We are most interested in understanding how the brain works—in particular how activities in neurons and the architecture of the brain enables high-level thinking,” says Dr. Trappenberg. “A central ingredient for all of this is how humans and animals learn.

This brings us to the scientific area of machine learning.”

A lot of progress has been made recently in understanding the important principles of learning. The lab also has the added benefit of using these methods for data analysis or data mining.“We usually make computer simulations to study brains but we want our research to lead us to building models of how the brain really works,” he continues.

“We now think that an even better way to study and evaluate these models is to build artificial agents—robots— to show that they can do high-level tasks like finding objects or planning movements.”

Hal Lab projects Many research projects from the HAL Lab cross over between two of the three research areas (computational neuroscience, machine learning and robotics), combining the strengths of the entire team.

As an example, the lab works with local company, Mindful Scientific, to apply machine-learning techniques to understand EEG data in order to evaluate possible brain injuries, combining machine learning with neuroscience.

The lab works with another company, Pleiades, to make a drone (flying quadcopter) that can follow objects while learning that the appearance of these objects can change, combining robotics and machine learning.

The team is also building a biologically realistic robot arm controller that can move to a target even when the camera input is sometimes interrupted, combining robotics with neuroscience.

Ultimately all three areas are tightly interwoven and the HAL Lab hopes to play an important role in the continued progress of deep learning.

HAL Lab members interacting with various robots. Above: Postdoctoral fellow Dr. Hossein Parvar and PhD student Farzaneh Sheikhnezhad Fard. Below: Graduate students Chun Kwang Tan, Vignesh Babu, and Yoshimasa Kubo. Left: Dr. Trappenberg is facing a quadcopter drone which has learned to follow him.

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Privacy: A Social Bridge Enabling E-Commerce researchers in dal's e-Privacy Lab are working on ways to uplift privacy “Without security and privacy, consumers will not trust…We are counting on the digital economy for jobs and growth in EU,” said

Paul Timmers, Director of the Sustainable and Secure Society Directorate DG CONNECT, European Commission, in Athens 2014.

Dr. Peter Bodorik (Dalhousie Faculty of Computer Science), Dr. Dawn Jutla (Saint Mary’s Sobey School of Business and adjunct professor to Dalhousie’s Faculty of Computer Science) and graduate research students in the e-Privacy Lab are working on research that has an international impact on the emerging field of privacy engineering. Their work is helping inform the nascent privacy engineering field from technical, organizational, user, and governance perspectives. Privacy not only enables online commerce, but it protects the human right to be left alone and to control the dissemination of information. Privacy engineering is important in its promise to help shape increasingly online civil societies as well as support economic renewal from digital economies.

In the e-Privacy Lab, research and development is conducted on the architectures, methods, methodologies, and

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management building blocks for privacy and e-commerce. The team is translating their know-how into tools for privacy-enabling start-ups and larger businesses.

Privacy Engineering and Big Data The digitization of vast quantities of information leads to the collaboration of many stakeholders to develop shared

standards-based interoperable platforms. This convergence supports the efficient analysis and flow of information to accelerate new discoveries. But significant discipline-based communication gaps exist among policymakers, businesses and software engineers.

Where policymakers leverage rich textual language, software engineers comSystem

-Dr -Dr -Dr

-Dr

SSL – All communication over secure communication connection

-Dr -Dr

-Dr

-Dr -Dr

Review recommended treatments

-Dr

PII Replacement – Replace PII with codes in program’s input data

Doctor

-Dr

-Dr

Privacy Notice – on storage and usage of obtained data Agreement – Obtain agreement on storage and use of obtained data -Ds

-Ds

-Ds

-Ds

Data Scientist

View alterntive patients treatments

Anonymization - Anonymize data (program’s result) - anonymization method

Figure 1: One software engineer’s visual for embedding privacy into IBM Watson’s Sloan Kettering Cancer Treatment applications.

Privacy Research Personal Context Agent Network (PeCAN) The Personal Context Agent Network (PeCAN) platform provides common privacy services for users and businesses from the client-side. It uses information-providing approaches to enhance user control over personally identifiable information. Building on standardized XML-based vocabulary, PeCAN allows users to manage their own private data online. This includes services to log what data has been given to which organizations, under what terms, and what the user believes to be true about the organization. Users may also customize their privacy preferences according to different situations. The platform provides sophisticated information-providing services intended to help users avoid giving out information that can be used by others to harm their privacy.

and Economies municate cryptically with code snippets or pseudocode and visually with things like flow charts and sketches. Their partially standardized visual diagrams, screenshots and associated metadata are literally worth thousands of words.

These provide rich sources of documentation that can be put to many good uses including closing communication gaps among multidisciplinary stakeholders and facilitating systematic audits.

New standards-based approaches are required to help make the output of big data algorithms more secure and protected. The e-Privacy Lab specifically examines how they can aid software engineers to embed privacy using new tools and services to visualize complex stakeholder interaction with software systems (see figure 1).

Visualizing and Documenting The primary purpose of documentation is to communicate. Organizations of all sizes employ good documentation for rapid onboarding of employees on a project. With high turnover rates, ensuring quality documentation is an essential operational item requiring strict management. Primary software engineering

PeCAN’s common service provides customized privacy preferences according to use context (for example: space, time, country, organization). PeCAN supports multiple privacy services including: services to maintain privacy contexts and user control mechanisms, sixteen enumerated services consisting of comparisons of government privacy regulations, business privacy policies, and user data handling preferences.

methodologies (for example: traditional and agile) recommend frequent quality documentation as a best practice. Agile modeling methodologies cite documenting as late as possible and storing one version of documentation. In practice, software engineers have started using tools such as JIRA to tag, document and store software requirements as security and/or privacy related in one place.

Software engineering documentation may be used to demonstrate proof of compliance at an auditing level.

Moving Privacy R&D into International Standards The vision behind years of R&D in privacy engineering and e-commerce work at Dalhousie University and Saint Mary’s University translates to thought leadership that is currently informing a major emerging international privacy standard from the OASIS Privacy by Design Documentation for Software Engineers Technical Committee (OASIS TC).

Dr. Jutla, who is also Director of the Master of Technology Entrepreneurship and Innovation (MTEI) program at Saint Mary’s University, co-chairs the OASIS TC with Dr. Ann Cavoukian, Executive

Director of the Privacy and Big Data Institute at Ryerson University, and former Information and Privacy Commissioner of Ontario.

“We require more successful university and private sector partnerships like these for a meaningful international impact of privacy R&D on commerce, healthcare, and governments,” says Dr. Bodorik, also a former director of the Master of Electronic Commerce program and Associate Dean at Dalhousie. continued on page 6

Spelling it out: Team members from Dal's e-Privacy Lab (l-r, above) Vikram Sivaraman, Hiral Patel, Ravi Anghan, Dr. Peter Bodorik, Dr. Dawn Jutla, Pradeep Gangireddy, and Naureen Ali.

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Training in Big

Privacy continued

Privacy Research

A collaborative NSERC CREATE Program

Privacy Architecture for Web Services (PAWS) A number of graduate students have been working on the research project, Privacy Architecture for Web Services (PAWS), under the supervision of Dr. Bodorik and Dr. Jutla.

Web services have emerged as a leading technology for exposing information, services, and resources over the web. Web services have become the main technology for integration of software and are employed in most new IT startups. Any business requiring a layering of privacy on their web services can use PAWS (see figure 2) to provide monitoring of their access to —and use of—private data stored in an organization’s database (DB) servers. Web service requests and replies are intercepted by the request and reply monitors who check the data that is provided in a web service request and also check what is returned as a response of executing the service. The monitors consult the knowledge base, which contains information on private data stored in the organization’s DB servers, to ensure that a user who has appropriate rights accesses the private data. Furthermore, the monitors also check that any use of private data is only for purposes that the data subject permits by consent. When the organization obtains private data, it also obtains consent on the use of that private data either directly from the data subject, the person described by the private data, or indirectly from the source of the private data.

The knowledge base contains a wealth of privacy related information. It contains information on which web pages collect or provide private data, which applications and web services access private data and for what purposes, and also what users invoke which applications and web services.

The architecture provides a privacy engineer a toolbox to manage the PAWS’ operation and the content of the knowledge base. For example, it provides the engineer with a tool to inject various privacy services into software, such as a service for obtaining consent from the data subjects when private data is being collected from them or a service for authenticating a user seeking access to private data. A software privacy information agent, which audits the log records of access to DB servers and log records of web service requests and replies in order to ensure that the knowledge base is up to date, assists the engineer.

Currently, these various tools are being integrated together to form a privacy toolbox or a tool suite in order to provide the privacy engineer with a consistent user interface to control the request and reply monitors, guide the privacy agent, and to manage the content of the knowledge base.

Figure 2: The PAWS architecture Toolbox ...

Web Pages Web&App S erver

Privacy Engineer

...

Inject Privacy Services

PE I nterface t o KB

Knowledge Base

Request M onitor

Web Services

Private Data Dictionary Web page KB Web services KB

Reply Monitor

Web Services Requests/Replies Log

Applications KB WS -­‐ DB a ccess KB Private Data in DBs KB

Notice a nd Consent KB Privacy Information PI Agent DB S erver SQL Statements Log

Collected Consent KB State of M onitoring KB Privacy Services KB

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The Institute for Big Data Analytics is pleased to help train Canada’s next generation of highly skilled innovators through Training in Big Text Data (TRIBE).

TRIBE is a Natural Sciences and Engineering Research Council of Canada (NSERC) Collaborative Research and Training Experience (CREATE) Program and is a joint creation between Dalhousie University, Simon Fraser University, and Université de Montréal that offers fully funded, competitive Masters of Computer Science and PhD, Computer Science scholarships. Dr. Stan Matwin, Canada Research Chair in Visual Text Analytics and director of Canada’s only Institute for Big Data Analytics, received the funding to develop training programs which provide young researchers with opportunities to help them make the transition from trainees to productive employees in the Canadian workforce.

CREATE program provides resources The CREATE program provides leading research teams in natural sciences and engineering with the resources to implement an applied training environment that combines research knowledge and experience with the personal and professional skills needed in industry or government workplaces. Dalhousie has been very successful in competing for CREATE grants, receiving almost one each year there has been a competition.

Dr. Matwin and his research team will receive $1.65 million in CREATE funding over the next six years. The TRIBE program is based on five pillars: a structured curriculum combining the know-how, including “soft skills” in demand by industry; hands-on training in the form of an industrial internship; national and international student mobility and exchange; a multi-lingual application and training environment; respect for privacy as a value instilled in students and in

Text Data

Scholarship profile

Lulu Huang is a Masters of Computer Science student and one of the first to receive a scholarship to study big text data analytics as part of TRIBE, a program funded by NSERC‘s CREATE Program.

the approach to big data. The combination of these pillars will provide unique, industrially relevant graduate training in an area of unmet high demand in Canada and globally. Students who fulfill all the course and internship requirements will obtain a Graduate Certificate in Big Text Data.

Industry-Relevant training “This training will be achieved by students participating in focused, industrially-relevant research projects,” says Dr. Matwin.

“In TRIBE, all students will do an industry internship, and most of them will spend some time doing research in one of our partner universities.”

The amount of data managed by organizations in North America and around the world has exploded in recent years.

Dr. Matwin says analyzing large data sets will become a key area of priority in many different domains. A recent McKinley Report predicts a shortage of 140,000 data analysts in the US alone in the next four years. “TRIBE will provide advanced training in areas of high demand, producing graduates that will be in high demand,” says Dr. Matwin.

The CREATE grant will also help the Institute of Big Data Analytics attract topnotch students, and provide the institute with the resources to work on state-ofthe-art research projects. “The partnership with TRIBE will increase the footprint of the Institute, as text data analytics is one of the main focuses of the institute and training personnel is one of its main priorities.”

“As only 15 projects were selected from the initial 120 applications, the CREATE grant is also a recognition of excellence towards the team we’ve put together, both at Dal and at our partner universities,” says Dr. Matwin.

Big Text Data Analytics and Health Care Lulu Huang started her undergraduate degree in China. After listening to a presentation

by Dr. Phil Cox on joint degree opportunities, she decided to finish her degree at Dal. Although language presented a challenge and it took a little while to get caught up with classwork, the courses were very appealing to her. During Lulu’s last year of her undergraduate degree, she particularly enjoyed data mining, natural language processing, and completed a research project on text mining and visualization.

Originally, Lulu’s idea was to get a job after graduating. After strong encouragement and sound advice from Dr. Evangelos Milios and Dr. Stan Matwin, she became inspired to further her studies. When the opportunity arose to complete a master’s degree in big text data under the supervision of Dr. Matwin, she took it with enthusiasm and started her graduate studies in September. In collaboration with a major care provider company, Lulu currently works on analyzing data from geriatric care patients, with the goal of understanding how care can be better delivered. For example, how can the fall of a patient be prevented or how can we determine who are most prone to falls in geriatric care?

Lulu has quickly gotten used to living in Canada. “Halifax is very similar to the city I come from in south east China, except for the winters!” If, after graduating, a great opportunity came up she would probably stay in Canada. She says she might have to look very closely, though, at her location choice. “I couldn't live anywhere that was colder than Halifax!”

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Alumni around the globe

Status Update Building a work relationship with facebook

Peter O'Hearn (BSc’85) lives in London, England, and has landed a job at Facebook building and deploying static analysis tools – notably using Infer, a static analyzer acquired when facebook took over O'Hearn's startup.

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Every 60 seconds, 510 comments are posted on Facebook, 293,000 statuses are updated and 136,000 photos are uploaded.

Last February the social media site celebrated its tenth anniversary, and today, Facebook has over 1.32 billion monthly active users, and over 7,185 employees worldwide.

Dalhousie University Computer Science alumnus Peter O’Hearn is among the billions of users who have a Facebook profile page. He, like many others, regularly posts updates to his page.

For example, according to Facebook, O’Hearn caught a number of squid in St. Margaret’s Bay, N.S. this past summer.

The Halifax native is well versed in Facebook. But his social media expertise spans beyond the occasional status update.

Also noted on his profile page is his place of employment: Facebook.

Based in the company’s London, England office, O’Hearn was hired to work for the popular social media site following the acquisition of his startup company, Monoidics, in 2013 by Facebook. Monoidics was created in 2009 by O’Hearn and two colleagues, Cristiano Calcagno and Dino Distefano. Their company marketed Infer, the separation logic-based static analysis tool.

Separation logic is a theory which facilitates scalable reasoning about programs, particularly concerning the way they access and mutate memory and other dynamic resources.

Separation logic was developed jointly by O’Hearn and the late Prof John Reynolds from Carnegie Mellon University. O’Hearn says the creation remains his greatest achievement.

“With separation logic a range of programs that previously only had messy, complicated specifications and proofs became easy to deal with. This opened up new possibilities both in theoretical and practical work,” says O’Hearn.

“In addition to its significance, the work on separation logic was (to my mind) pretty. It is a compact theory based on a few primitives, that nonetheless provides the power to go much further on difficult problems than previous more-complicated approaches.”

Today O’Hearn works with a team at Facebook building and deploying static analysis tools. These are software tools that crawl over code searching for bugs and attempting to prove properties of the code. The team uses separation logic

“When I first arrived, I thought it felt like Disneyland. Only later did I find out that Facebook actually hired Disney consultants to give it its amazing look and feel.” in their work, particularly with the Infer static analyzer.

For O’Hearn, landing a job at Facebook has been the icing on the cake. He remembers his first visit to the company’s main headquarters in Hacker Way, California.

“When I first arrived it was a jawdropping experience,” he recalls. “There were restaurants, cafes, people driving around on bicycles and skateboards, there was graffiti and other art on the walls of the offices, there was funky music playing before seminars and in lunch areas. I thought it felt like Disneyland.

Only later did I find out that Facebook actually hired Disney consultants to help give it its amazing look and feel.”

He says most importantly, he was inspired by the people he met: hundreds of employees working enthusiastically to make an impact on the company.

“It was amazing,” he says. “It’s an extremely positive atmosphere and there are a lot of intelligent people to

engage with and challenging problems (to solve).”

The work ethic within the company is one O’Hearn has always instilled within himself. He says he likes a challenge and sets his standards very high.

“We purposely choose difficult goals so it’s hard to achieve them,” he says.

“We challenge ourselves. It feels great when you actually achieve those goals.”

He says working at Facebook has taken him one step closer to achieving his next goal.

“A career goal of mine has been for program verification, based on logics of programs, to have broad realworld impact,” he says. “It should help programmers write more reliable code and therefore impact the people that use this code. Over a billion people use Facebook, and there is a great programming culture here, making this an ideal situation for me.”

O’Hearn attained a BSc degree in Computer Science from Dal in 1985, followed by MSc and PhD degrees from Queen’s University. He went on to hold professional positions at Syracuse University, Queen Mary, University of London and University College London before joining Facebook in 2013.

Since graduating, he admits he’s certainly had a few bumps along the way, but going the extra mile has always paid off.

He says it’s important for students today to try and go beyond the textbook.

He says students need to debate with one another about computer science problems and understand why an approach might or might not work.

“I’ve never seen a better time for being a graduating computer science student,” he says. “There are many amazing technology companies with challenging problems and they need talented computer scientists. “

TULIP DEvelopers Marek Lipczak and Arash Koushkestani

Tulip Takes First Prize in Data Challenge When the research labs of Google, Microsoft and Yahoo team up to organize a challenge around building a better system for entity linking, you know that researchers around the world are going to take note.

In April of this year, an announcement for the 2014 Entity Recognition and Disambiguation Challenge went out and 36 teams from North America, Europe and Asia signed up. There were two strands of the competition: one in search queries and the other in web documents. Alumnus and current research associate for Dr. Evangelos Milios, Marek Lipczak PhD’13, and MCS student, Arash Koushkestani (also supervised by Dr. Milios) entered the challenge with Tulip, a lightweight entity recognition and disambiguation system they developed together.

Their Tulip system beat out the competition and was awarded first prize in the web documents long track at the Special Interest Group on Information Retrieval (SIGIR) conference in July.

Entity recognition and disambiguation is also known as “entity linking”, the goal of which is to find mentions of entities (like Barack Obama) in text and link them to an external knowledge base, typically Wikipedia. Although the goal is straightforward, the problem is not a simple one for either human beings or machines, and has been addressed by many other researchers in the past. Human beings are limited by both the amount of data they can process and the tendency to skim-read and, therefore, miss entities. Machines can cope with large data sets but struggle with language ambiguity and the overwhelming number of options.

To overcome this problem, the challenge then is to use context in a smart way.

Rather than bringing an existing system to this problem, Marek and Arash —with fresh eyes and fresh ideas —started from scratch and created a system which is both simple and fast (200 news articles per minute) and achieves an accuracy of between 0.7 and 0.8 (F1 score). Up against other teams who were using existing systems, Marek and Arash felt that the fresh approach was one of the main sources of their competitive advantage. Their Tulip system represents a new stream in this field of research, solving the problem in a new way and creating more opportunities for future development.

At the start of the challenge, Tulip was scoring near the bottom of the pack in its performance but their modifications and developments propelled the system rapidly up the leaderboard, overtaking its competitors in just a few months.

Although the prize money was not huge – a $500 cheque from Google – it is their success in developing a new idea to outperform teams from around the world that satisfies both Marek and Arash. They have presented a paper on their work at the workshop following the challenge and have plans for further publications. Their intentions are to develop their system as open-source technology.

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FCS News & Notes

ICT Sandbox We have been working hard over the last few months to spread the word about the new ICT Sandbox and to get students from across partner institutions at Dal, SMU, NSCAD, and NSCC excited. From entrepreneurship and technology courses, to mentorship, to workshops and material support, this program includes all the necessary ingredients to support students as they develop their ideas into successful commercial or social enterprises. Several teams have already expressed interest in entering the sandbox with projects for a January start. ictsandbox.ca

Beach Pong at Nocturne Dr. Derek Reilly and his team of students in the Human-Computer Interaction Lab have been working over the past year on a new mixed reality game. Beach Pong was unveiled during Halifax’s Nocturne: Art at Night festival in October. Beach Pong is a mash-up of beach volleyball and the classic video game Pong and was a popular exhibit at Sands at Salter on the waterfront.

CSGS New kid on the block The Faculty of Computer Science officially has a new society – one which represents all FCS graduate students. The new Dalhousie University Computer Science Graduate Society (CSGS) began earlier this year and grew from the need for a separate society at the Faculty to cater to the specific needs of graduate students.

CSGS was ratified as a society in March and has been actively organizing events since then. Summer events have included board game nights, trivia, tutorials on making balloon animals, and pizza socials. The CSGS Cup was particularly exciting, where an intense soccer competition took place between grad students from the Goldberg Computer Science Building and the Mona Campbell Building, with over 40 people participating.

Collaboration and partnership is a key part of CSGS culture. CSGS has collaborated with sister societies Computer Science Society (CSS), Women in Technology

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Society (WiTS) and Web Development Society (WDS) in a variety of ways.

All the societies worked together during new student orientation activities this fall and through the Organizing Committee of the 2014 Dalhousie Computer Science In-house conference (DCSI).

It’s not just about social events: CSGS just introduced Discover Mondays, a series of talks open to graduate or 4th

year undergrad students with a goal of providing the opportunity to practice presentations: for thesis, project or conference presentations, to demonstrate a how-to, or to present on any other interesting topic.

“It is our first year and we are learning as we do various things, but it has been a very exciting experience for us all,” says Raghav Sampangi, PhD student and President of CSGS. csgs.cs.dal.ca

Celebrating Women in Computing Attending the Grace Hopper Conference

Fall Lectures Deep Learning: The Path to Artificial Intelligence

We welcomed Dr. Yoshua Bengio of the Department of Computer Science and Operations Research and Canada Research Chair in Statistical Learning Algorithms at Université de Montréal to Dalhousie as part of our Distinguished Lecture Series in Computer Science. Welcoming over 150 people, Dr. Bengio hosted two talks on how deep learning has influenced technology markets. Its importance can be particularly observed in computer vision and speech recognition. Dr. Bengio’s talk also explored the potential for expanding the level of competence of deep learning systems.

Killam Lecture Series

In partnership with the Dalhousie Art Gallery, and supported by the Killam Trusts, We hosted a three-part series on Computer Science, New Media, and Space. This series was broken out into an exhibition featuring artists Edith Flückiger and Germaine Koh and two lectures showcasing artists David Rokeby and Nell Tenhaaf.

BIG DATA: OPPORTUNITIES & CHALLENGES Dr. Stan Matwin welcomed over 60 people to this year's FCS Homecoming Lecture which was followed by our Student and Alumni Social in partnership with the CS Society's Geek Beer.

In October, 8,000 women and men from Bachelor of Computer Science (BCS) students, Sage Franch and Sarah Morash, traveled to Phoenix to ataround the world gathered in Phoenix, tend the 2014 Grace Hopper Celebration for Women in Arizona for the largest Grace Hopper Computing, a conference produced by the Anita Borg Celebration of Women in Computing Institute and presented in partnership with ACM. in the organization’s history. It was my first time attending this, or any, conference specifically geared to women in tech, and I was blown away by the energy and power that the attendees exuded. With keynotes and talks from high-powered speakers including US CTO Megan Smith, Microsoft CEO Satya Nadella, and head of DARPA Arati Prabhakar, it was impossible to leave the conference without a renewed drive and excitement for this industry.

The conference was rife with opportunities for students, from on-site interviews with the 100+ companies showcasing at the Career Fair to mentoring sessions and networking events specifically designed to introduce students to women at more advanced stages of their careers. It was incredible to meet so many women with such varying careers, all sharing a passion for technology and for building a better future.

One of my favourite moments was when I had the opportunity to talk to Bonnie Ross, head of 343 Industries — the woman that built Halo into the franchise it is today.

When the three jam-packed days of talks and technical sessions were over, I left Phoenix with a whole new perspective on what it means to be a part of this amazing technology movement, both as a woman and an advocate for equality.

As current tech students, we are entering the industry at an extraordinary time when anything is possible. Technology has advanced to the point where it is starting to become part of the environment we live in and we as programmers have the power to shape that world.

I feel truly fortunate to have been able to take part in the Grace Hopper Celebration this year, and highly recommend it to anyone who is passionate about the future of the tech industry.

–sage Franch

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cs.dal.ca/csday

Spread the word

Computer Science Day 2015

Stay connected with us TWITTER Twitter.com/dalfcs and FACEBOOK Facebook.com/dalfcs ONLINE cs.dal.ca FCS Class Notes Please send your news, updates, announcements, events or ‘things you’d like to see’ to allison.kincade@ dal.ca. We’d love to hear from you!

Contributors: Allison Kincade, Theresa Anne Salah, David Langstroth, Michael Shepherd, Sage Franch, Raghav Sampangi, Thomas Trappenberg, Peter Bodorik, Dawn Jutla, Grant Wells, Nick Pearce, Danny Abriel, Jane Lombard

Do you know a high school student who is exploring their university options?

They can test drive a degree in computer science or informatics at Computer Science Day. High school students, their parents, teachers, and friends are invited to this free full day.

Saturday, February 28, 2015 Goldberg Computer Science Building csday@cs.dal.ca

42101015 Return undeliverable Canadian addresses to: Alumni & Communications Officer Faculty of Computer Science Dalhousie University PO Box 15000 Halifax NS B3H 4R2 Canada