Machine Learning in Economics Summer Institute 2022 (MLESI22)

From August 15-20, the Center for Applied AI hosted its annual Machine Learning in Economics Summer Institute (MLESI). During this week, 30 Ph.D. students and junior faculty, all with backgrounds in economics or related disciplines (e.g., computer science, statistics, etc.), joined us at our University campus to interact with leading researchers in machine learning and economics. The program offered a number of opportunities, from lectures to panel discussions, tutorials, office hours, and even social activities around Chicago.

Our organizers this year were Jens Ludwig (Chicago), Sendhil Mullainathan (Chicago), Jann Spiess (Stanford), and Ashesh Rambachan (Harvard).

Guest faculty joining us included Dan Björkegren, Asst Professor of Economics, Brown University; Annie Liang, Asst Professor of Economics, Northwestern University; Pepe Montiel Olea, Asst Professor of Economics, Columbia University; Stefan Nagel, Professor of Finance, Chicago Booth; Manish Raghavan, Asst Professor of Computer Science, MIT Sloan; Chenhao Tan, Asst Professor of Computer Science, UChicago and Stefan Wager, Assc Professor of Operations, Information, and Technology, Stanford GSB. Special thanks to all of our professors who joined us for this conference!

We also had alumni from our MLESI class of 2021 join us on Thursday, August 18. Because last year’s institute was virtual, we wanted to give our past participants a chance to share their current research with our faculty and new class of participants in person. We’re so grateful for their participation and proud of their research thus far! 

Overall, we’re proud to have the Machine Learning Institute be a means for academics and students to find community in their fields. This Institute is an opportunity for the next generation of field-defining scholars to spark inspiration in each other and interest in their respective work. With the help of our organizers, guest faculty and staff, our participants learned more about the role of machine learning in fields like macroeconomics, behavioral science, health, development, public, labor, and applied microeconomics. Through this Institute, our participants have become better equipped with the knowledge and tools to engage in these budding fields more meaningfully and have formed meaningful connections that will last throughout their academic careers. We believe that the future of these fields will be driven by these young scholars, and we are proud to be a part of their journey. 

For those deeply interested in the intersection of machine learning (e.g., supervised and unsupervised learning algorithms, machine vision, text analysis), and economics (e.g., causal inference, experimental design, fairness and bias, and econometrics), we highly encourage you to apply for our Institute next year. For updates on future events and new research, sign up for our newsletter and follow us on Twitter.