Wednesday, April 21, 202110:00 am - 1:00 pm
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.
Tickets are free for the Columbia University community (faculty, staff, students, and affiliated researchers); DSI Industry Affiliates; and NYC government.
Pat Bajari, Chief Economist and Vice President, Amazon Core AIPat 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.
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
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 NetzerArthur J. Samberg Professor of Business, Columbia Business School
Talk Title: Salespeople automation: A Human-Machine Hybrid Approach
Lydia ChiltonAssistant Professor of Computer Science, Columbia Engineering
Talk Title: AI Tools for Design and Innovation
Sarah RossettiAssistant 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 Associate Professor of Social Work, Columbia School of Social Work
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 KpotufeAssociate Professor, Department of Statistics, Columbia University
Talk Title: Big but Imperfect Data: Fundamental Challenges of Domain Adaptation
Melanie WallProfessor 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 BareinboimAssociate Professor, Department of Computer Science, Columbia University
Talk Title: Causal Data Science
Clifford Stein Professor, Industrial Engineering, Operations Research and Computer Science, Columbia Engineering
Talk Title: Parallel Algorithms for Massive Graphs
Martha KimAssociate Professor, Computer Science, Columbia University
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.
DSI Industry Affiliates Program