Sendhil Mullainathan - CS and ORIE Colloquiums

Description

Sendhil Mullainathan
The Peter de Florez Professor, In Electrical Engineering and Computer Science, and Economics at MIT  will be on campus as a Dean of Faculty Messenger Lecturer and a Data Science Distinguished Lecture for the Center for Data Science for Enterprise and Society.  Professor Mullainathan will be giving three talks Monday Nov. 11 - Wednesday, Nov. 13.   

This is the second talk in the Messenger series and the Data Science Distinguished Lecture series and is co-sponsored with the CS Colloquium and the ORIE Colloquium.  

Talk Title:  Incorporating Behavioral Science into Computational Science 
Tuesday, November 12
Time: 11:45 am - 12:45 pm
Location: Gates Hall, G01
Reception following

Description: I will describe several projects that incorporate our understanding of humans into how we build and evaluate algorithmic systems, such as supervised learners or large language models. 

BIO: Professor Mullainathan is a recipient of the MacArthur “Genius Grant,” has been designated a “Young Global Leader” by the World Economic Forum, was labeled a “Top 100 Thinker” by Foreign Policy Magazine, and was named to the “Smart List: 50 people who will change the world” by Wired Magazine (UK).experiments—to study social problems such as discrimination and poverty. He recently co-authored the book Scarcity: Why Having too Little Means so Much and writes regularly for the New York Times. Professor Mullainathan helped co-found a non-profit to apply behavioral science (ideas42), co-founded a center to promote the use of randomized control trials in development (the Abdul Latif Jameel Poverty Action Lab), serves on the board of the MacArthur Foundation, has worked in government in various roles, is affiliated with the NBER and BREAD, and is a member of the American Academy of Arts and Sciences.His current research uses machine learning to understand complex problems in human behavior, social policy, and especially medicine, where computational techniques have the potential to uncover biomedical insights from large-scale health data.