Data Science Day provides a forum for innovators in academia, industry, and government to connect. The April 21, 2021 virtual event will feature two sessions of lightning talks from leading Columbia University faculty members; interactive demonstrations and posters; and a keynote address from Pat Bajari, a Chief Economist at Amazon and Vice President of Amazon’s Core AI team.

Details & Registration

Event Date: April 21, 2021 (10:00 AM – 1:00 PM ET) – Virtual

Tickets are free for the Columbia University community (faculty, staff, students, and affiliated researchers); DSI Industry Affiliates; and NYC government.


Keynote Speaker

Pat Bajari

Pat Bajari, Chief Economist and Vice President, Amazon Core AI
Pat Bajari is a Chief Economist at Amazon and Vice President of Amazon’s Core AI team. His team of software engineers and scientists in machine learning, statistics, operations research, and econometrics has helped to build scalable systems for supply chain, transportation, pricing, automated marketing, robotics, forecasting, human resources, and more. Prior to joining Amazon, he was a full time faculty member in economics at Harvard, Stanford, Duke and Minnesota.


Lightning Talks

Hear from Columbia University’s most innovative researchers. Two sessions of faculty-led lightning talks will shed light on emerging opportunities for data science across many disciplines and use cases.

View abstracts & biographies

Human + Machine: A New Hybrid World

Data science is an ever-evolving and expanding field. Here, we explore ways in which it has become an integral part of the decision-making and optimization of countless fields, including patient care, B2B, and design.

Oded Netzer

Oded Netzer
Arthur J. Samberg Professor of Business, Columbia Business School

Talk Title: Salespeople automation: A Human-Machine Hybrid Approach

Lydia Chilton

Lydia Chilton
Assistant Professor of Computer Science, Columbia Engineering

Talk Title: AI Tools for Design and Innovation

Sarah Rossetti

Sarah Rossetti
Assistant Professor of Biomedical Informatics and Nursing, Columbia University

Talk Title: Exploiting the Signal Gain of Clinician Expertise in a Predictive Early Warning Score and CDS tool using Nursing EHR data

Courtney D. Cogburn

Courtney D. Cogburn
Associate Professor of Social Work, Columbia School of Social Work

(Moderator)


Cause, Learn, Optimize, and Reason

This session will highlight advancements in data science, bringing to light causation as opposed to correlation, the use of transfer learning for improving imperfect data, optimization for the improvement of graph problems, and reason to improve differential prediction.

Samory Kpotufe

Samory Kpotufe
Associate Professor, Department of Statistics, Columbia University

Talk Title: Big but Imperfect Data: Fundamental Challenges of Domain Adaptation

Melanie M. Wall

Melanie Wall
Professor of Biostatistics (in Psychiatry), Department of Biostatistics, Mailman School of Public Health, Columbia University

Talk Title: Data Science as the Engine for a Learning Health Care Service System for First Episode Psychosis in Coordinated Specialty Care

Elias Bareinboim

Elias Bareinboim
Associate Professor, Department of Computer Science, Columbia University

Talk Title: Causal Data Science

Clifford Stein

Clifford Stein
Professor, Industrial Engineering, Operations Research and Computer Science, Columbia Engineering

Talk Title: Parallel Algorithms for Massive Graphs

Martha Kim

Martha Kim
Associate Professor, Computer Science, Columbia University

(Moderator)


Virtual Poster and Demo Session

Meet and network with researchers who are building the next generation of data science methods and applications. Attendees may browse data science posters, demonstrations, videos, and abstracts by the Data Science Institute’s research center and working group topic areas. Join breakout groups to ask questions and get to know the participating teams.

Want to exhibit? See details below.


Thank You

DSI Industry Affiliates Program

Industry Affiliate Logos
Current DSI Industry Affiliates