Machine Learning in Economics Summer Conference (MLESC25)

天美传媒

NECESSARY COOKIES These cookies are essential to enable the services to provide the requested feature, such as remembering you have logged in.	ALWAYS ACTIVE
	Reject \| Accept
PERFORMANCE AND ANALYTIC COOKIES These cookies are used to collect information on how users interact with Chicago Booth websites allowing us to improve the user experience and optimize our site where needed based on these interactions. All information these cookies collect is aggregated and therefore anonymous.
FUNCTIONAL COOKIES These cookies enable the website to provide enhanced functionality and personalization. They may be set by third-party providers whose services we have added to our pages or by us.
TARGETING OR ADVERTISING COOKIES These cookies collect information about your browsing habits to make advertising relevant to you and your interests. The cookies will remember the website you have visited, and this information is shared with other parties such as advertising technology service providers and advertisers.
SOCIAL MEDIA COOKIES These cookies are used when you share information using a social media sharing button or 鈥渓ike鈥� button on our websites, or you link your account or engage with our content on or through a social media site. The social network will record that you have done this. This information may be linked to targeting/advertising activities.

John Zich

Machine Learning in Economics Summer Conference (MLESC25)

August 11-12, 2025

The Machine Learning in Economics Summer Conference will be held at the University of Chicago on August 11–12, 2025.

MLESC brings together researchers working at the intersection of machine learning and economics. We are looking for submissions on research studying how machine learning methods (e.g., supervised and unsupervised learning algorithms, machine vision, text analysis) may be used to tackle existing questions and open new directions in fields like behavioral economics, applied microeconomics, development, and macroeconomics. Both empirical and theoretical papers are welcome.

We invite scholars, researchers, and students to register and attend the 2025 Machine Learning in Economics Summer Conference. Join us for a rich program of research presentations and discussions at the intersection of machine learning and economics.

Deadline to register for the conference is Tuesday, August 5th.

Keynote Speakers

Massachusetts Institute of Technology

University of California- Berkeley

Agenda

Monday, August 11, 2025
8:00 - 8:30 am	Coffee and Breakfast
8:30 - 9:45 am	Suproteem Sarkar, Booth School of Business \| Economic Representations , ETH Zurich \| Deep Latent Variable Models for Unstructured Data
9:45 - 10:15 am	Coffee Break
10:15 - 11:30 am	Giovanni Compiani, Booth School of Business \| Demand Estimation with Text and Image Data , University of Bristol \| Copyright and Competition: Estimating Supply and Demand with Unstructured Data
11:30 - 1:00 pm	Lunch
1:00 - 2:15 pm	, Stanford University \| The Political Content of College Courses , Stanford Graduate School of Business \| Admissibility of Completely Randomized Trials: A Large-Deviation Approach , Booth School of Business \| (Deep) Learning Analyst Memory , Stanford University \| AI Supply Chains: An Emerging Ecosystem of AI Actors, Products, and Services , UC Berkeley \| Sparse Autoencoders for Hypothesis Generation
2:15 - 2:30 pm	Break
2:30 - 3:45 pm	, MIT \| AI Expropriation , Indiana University- Kelley School of Business \| AI Personality Extraction from Faces: Labor Market Implications
3:45 - 4:00 pm	Break
4:00 - 4:45 pm	, MIT \| Sufficient Statistics
Tuesday, August 12, 2025
8:00 - 8:30 am	Breakfast and Coffee
8:30 - 9:45 am	, Stanford University \| Optimal Conditional Inference in Adaptive Experiments , University of Pennsylvania \| Generative AI Can Harm Learning
9:45 - 10:15 am	Coffee Break
10:15 - 11:30 am	, Stanford University \| Simulating the Survey of Professional Forecasters , University of Tokyo \| Using Big Data and Machine Learning to Uncover How Players Choose Mixed Strategies
11:30 - 1:00 pm	Lunch
1:00 - 2:15 pm	, Harvard University \| Calibrated Coarsening in Human-AI Interaction: Theory and Experiments , Brown University \| Hate in the Time of Algorithms: Evidence on Online Behavior from a Large-Scale Experiment , Princeton University \| The Price of Engagement: Estimating Preferences and Welfare Through Recommendation Algorithm Audits , MIT \| AI Agents Can Enable Superior Market Designs , Harvard University \| Generative AI and Organizational Structure
2:15 - 2:30 pm	Break
2:30 - 3:45 pm	, University of Illinois at Urbana-Champaign \| The Costs of Housing Regulation: Evidence From Generative Regulatory Measurement
3:45 - 4:00 pm	Break
4:00 - 4:45 pm	, UC Berkeley \| Thinking versus Doing: Cognitive Capacity, Decision Making and Medical Diagnosis

Conference Organizers

Sanjog Misra

Charles H. Kellstadt Distinguished Service Professor of Marketing and Applied AI

Sendhil Mullainathan

Distinguished Service Fellow

Assistant Professor of Economics, Northwestern University

Assistant Professor of Operations, Information, and Technology, Stanford Graduate School of Business

Assistant Professor of Economics, Massachusetts Institute of Technology