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Columbia’s Institute for Data Sciences and Engineering An Applied Sciences Innovation Hub

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“Friday’s inaugural symposium for Columbia University’s new Institute for Data Science and Engineering was a celebration of an idea and an ambition. The idea: there is a deep, historic movement taking place across disciplines, which is that data science as a field is increasingly core to the way we understand the world in all fields of endeavor. Columbia University’s ambition is to build a leading program in research and education in this emerging field.”

NYC Media Lab/ The Lab Report 2


A Broad Institute  Nine Schools • SEAS (School of Engineering and Applied • • • • • • • •

Science) (lead) Arts and Science Journalism Business Architecture, Planning and Preservation International and Public Affairs Medical School Public Health Law

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Institute Plans  Initial plans to hire 30 new faculty in data science in 5 years  recruit 150 doctoral students;

 45 additional faculty will be hired, at 5 a year, over the next 15 years

 44,000 sq. feet of new academic space will be ready by 2016 4


Institute Status  48 founding Institute faculty  9 Executive Committee Members  Organizing committees for each Center

 ~150 affiliated faculty members, University-wide

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The Centers of the Columbia Institute for Data Sciences and Engineering SMART CITIES NEW MEDIA HEALTH ANALYTICS CYBERSECURITY FINANCIAL ANALYTICS FOUNDATIONS OF DATA SCIENCE

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Center for Smart Cities Co-chairs from Civil Engineering and Electrical Engineering

7 committee members, 23 affiliates

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Research in Smart Cities Integrating the digital city with the physical city • Monitoring building energy consumption in New York • Improve the power supply through smart grid technology • Deploy sensing devices to facilitate everyday activities in a crowded urban environment

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Infrastructure Monitoring Monitoring large suspension bridge vibrations

Fixed Reference

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Developing Green Infrastructure

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Urban visualization Visualizing and interacting in 3D with georeferenced urban data

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Center for New Media Cc-chairs from Journalism and the Center from Computational Learning Systems 10 committee members, 19 affiliates

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Research in New Media New forms of digital media Analyzing and creating social media • Creating visualizations •

Acquiring Information • From language – speech analysis, machine translations, identifying emotions • From images and video – extracting information from images

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Center for Health Analytics Chair from Biomedical Informatics

10 committee members, 15 affiliates

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Research in Health Analytics Analyzing big data for:

• Patient data • Genomic databases • Public health records

Using electronic health records

• To discover patterns of diseases, effective drugs, treatments, and therapies

Sequencing genomics • Showing associations with single mutations and geneticallyassociated diseases DNA Sequencing on a chip

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Health Analytics Center

Individual, Population

Clinical, Healthcare

Molecular, Cellular

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EHR and time series analysis – Glucose predictability Glucose

0.45 0.4 0.35

0.4-0.45 0.35-0.4

0.3

0.3-0.35

0.25 MI

0.25-0.3 0.2-0.25

0.2

0.15-0.2

0.15

0.1-0.15 0.05-0.1

0.1

0-0.05

0.05

450

0

-0.1-0

50 7 1 2 3 4 5 6 7 8 9 10 20 30 40 50 60 70 80 90 100

-0.05

tau

(Albers et al., 2009)

2

delta-t (days)

0.83 0.17

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Center for Financial Analytics Chair from Industrial Engineering and Operations Research 6 committee members, 24 affiliates

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Research in Financial Analytics Big data for better financial services and solutions • Use predictive analytics to optimize financial decisions • Understand and regulate highfrequency trading • Predict and manage systemic risks • Real-time analysis of unstructured data/information, e.g., corporate and government actions, commentary, social media

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Systemic risk  “Social” network of financial institutions  Complex  Very high dimensional  “Edges”  Lending  Assets  Derivatives Minoui and Reyes (2011 IMF Report)

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The Contagion Effect

 A vicious cycle during crisis time, leading to contagion

 Approach: stochastic network using publicly available data 25


Foundations of Data Science Co-chairs from Computer Science and Statistics 6 committee members, 42 affiliates

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Foundations of Data Science  Machine learning  Computational learning theory

 Statistical prediction  Algorithms and optimization

 Software and hardware infrastructure for computation with big data 27


Graph & Network Algorithms • Matching nodes into a network • New students show up to school • Have a matrix of their profile vectors • At graduation, observe formed network • Predict network for next year’s freshmen?

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New York, color coded by inferred similar social behavior

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Center for Cybersecurity Chair from Computer Science

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Research in Cybersecurity Essential to critical infrastructure • Government, financial transactions, electronic commerce, and personal computing

Security and survivability of large-scale, heterogeneous cybersystems • • • • •

Threat mitigation Threat detection and analysis Cyberattack reaction and recovery Cyberattack tolerance Large-scale distributed (re)action

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Breaking commodity devices to learn how to fix them using Symbiotes  CISCO Phone IP vulnerability

 HP printer firmware update vulnerability

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Collaborations  Industrial Affiliates  Foundations  International

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Industrial Affiliates  Different levels of access  Reduction in tuition and ICR for members

 Project work  Capstone project course participation  Events, recruiting 34


Initial Industrial Affiliates Partners  Bloomberg  Mediaocean  Microsoft Research  Google 35


Degree Programs  Certification of Achievement in Data Science  Fall 2013  Four courses

 MS in Data Science  Fall 2014  Core in fundamentals of data science  Tracks in application areas corresponding to centers 36


Certification in Data Science Joint between SEAS and GSAS All courses specifically designed for Certification    

Probability & Statistics (STATS) Algorithms for Data Science (CS/ IEOR) Machine Learning for Data Science (CS) Exploratory Data Analysis and Visualization (STATS)

Aiming for ~ 25 on-campus students in Fall 2013 37


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Kathleen Mckeown (Columbia Univ.): Columbia’s Institute for Data Sciences and Engineering  

Presentation from Big Data & Language Technology Conference May 2013

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