Summer 2024 Line Up
Friday April 26, 12 to 1 pm
Mass Reproducibility and Replicability: A New Hope
Abel Brodeur is an Associate Professor in the Department of Economics at the University of Ottawa. He earned a Ph.D. from the Paris School of Economics in 2015, and participated in the European Doctoral Program at the London School of Economics. His research interests include applied microeconomics, with a focus on reproductions and replications in economic research. He has served as a guest editor for special issues dedicated to these topics in Economic Inquiry and Research & Politics. Brodeur is also the founder and chair of the Institute for Replication (I4R) and co-directs the Ottawa Applied Microeconomics Lab. The talk will be based on a recent paper available here.
Friday May 3, 12 to 1 pm
Does AI help humans make better decisions? A methodological framework for experimental evaluation
Kosuke Imai is Professor in the Department of Government and the Department of Statistics at Harvard University. He is also an affiliate of the Institute for Quantitative Social Science where his office is located. Before moving to Harvard in 2018, Imai taught at Princeton University for 15 years where he was the founding director of the Program in Statistics and Machine Learning. Imai specializes in the development of statistical methods and machine learning algorithms and their applications to social science research. His areas of expertise include causal inference, computational social science, and survey methodology. Imai leads the Algorithm-Assisted Redistricting Methodology Project (ALARM) and served as an expert witness for several high-profile legislative redistricting cases. In addition, he is the author of Quantitative Social Science: An Introduction (Princeton University Press, 2017). Outside of Harvard, Imai served as the President of the Society for Political Methodology from 2017 to 2019. His current research interests include: data-driven policy learning and evaluation, causal inference with high-dimensional and unstructured treatments (e.g., texts, images, videos, and maps), fairness and racial disparity analysis, algorithmic redistricting analysis, data fusion and record linkage, census and privacy.
Friday May 10, 12 to 1 pm
TBD
Amanda Coston is a Postdoc at Microsoft Research in the Machine Learning and Statistics Team. In fall 2024 she will join the Department of Statistics at UC Berkeley as an Assistant Professor. Her work considers how – and when – machine learning and causal inference can improve decision-making in societally high-stakes settings. Her research addresses real-world data problems that challenge the validity, equity, and reliability of algorithmic decision support systems and data-driven policy-making. A central focus of her research is identifying when algorithms, data used for policy-making, and human decisions disproportionately impact marginalized groups. Amanda earned her PhD in Machine Learning and Public Policy at Carnegie Mellon University (CMU) where she was advised by Alexandra Chouldechova and Edward H. Kennedy. Amanda is a Rising Star in EECS, Machine Learning and Data Science, Meta Research PhD Fellow, NSF GRFP Fellow, K & L Gates Presidential Fellow in Ethics and Computational Technologies, and Tata Consultancy Services Presidential Fellow. Her work has been recognized by best paper awards and featured in The Wall Street Journal and VentureBeat.
Friday May 17, 12 to 1 pm
Algorithmic Recommendations and Human Discretion
Victoria Angelova is a fourth-year PhD student in Economics at Harvard University interested in Applied Microeconomics. She received a AB in Economics from Wellesley College in 2018. Prior to starting her PhD, she was a Research Assistant at the Industrial Relations Section at Princeton University.
Friday May 24, 12 to 1 pm
TBD
Belinda Li is a third-year PhD student at MIT CSAIL, affiliated with the language & intelligence (LINGO) lab @ MIT. Her advisor is Jacob Andreas. She has broad interests within natural language processing, but some high-level themes of recent work include: 1) Probing for representations of meaning in language models 2) Using language models for various natural-language tasks and grounded tasks. (What characterizes the space of tasks humans may be interested in? How do we transfer linguistic knowledge to grounded tasks? How do we best specify human intent to a language model?) 3) Improving the coherence and faithfulness of machine generations. She is funded by an NDSEG Fellowship and Clare Boothe Luce Graduate Fellowship. Previously, she spent a year at Facebook AI Applied Research, and before that, she obtained a B.S. in Computer Science at the University of Washington, where she worked with Luke Zettlemoyer.
Friday May 31, 12 to 1 pm
TBD
Ethan Busby is an Assistant Professor of Political Science at Brigham Young University, specializing in political psychology, extremism, artificial intelligence, and computational social science. His research relies on various methods, using lab experiments, quasi-experiments, survey experiments, text-as-data, surveys, artificial intelligence, and large-language models. He studies extremism in democracies, including what extremism is, who people blame for extremism, and what encourages and discourages extremism.
Friday June 7, 12 to 1 pm
Privacy protection in RCTs: The challenge of privacy protection in the field
Lars Vilhuber holds a Ph.D. in Economics from Université de Montréal, Canada, and is currently on the faculty of the Cornell University Economics Department. He has interests in labor economics, statistical disclosure limitation and data dissemination, and reproducibility and replicability in the social sciences. He is the Data Editor of the American Economic Association, and Managing Editor of the Journal of Privacy and Confidentiality.
Friday June 14, 12 to 1 pm
Field experimentation in the U.S. safety net
Jae Yeon Kim is a senior data scientist at the Safety Net Innovations Lab at Code for America, and a research fellow at the SNF Agora Institute and P3 Lab at Johns Hopkins University, as well as the Center for Public Leadership and Civic Power Lab at Harvard Kennedy School. He holds a Ph.D. in political science from UC Berkeley.
Friday June 21, 12 to 1 pm
Can Generative AI Improve Social Science
Chris Bail is Professor of Sociology, Political Science, and Public Policy at Duke University, where he founded the Polarization Lab. He studies how artificial intelligence shapes human behavior in a range of different settings—and social media platforms in particular.
Friday June 28, 12 to 1 pm
The Lie Detectives: In Search of a Playbook for Winning Elections in the Disinformation Age
Sasha Issenberg is a journalist and author of “The Lie Detectives: In Search of a Playbook for Winning Elections in the Disinformation Age” and four previous books, most recently “The Engagement: America’s Quarter-Century Struggle Over Same-Sex Marriage.” He teaches in the UCLA Department of Political Science and is a correspondent for Monocle. His work has also appeared in New York, The New York Times Magazine and George, where he was a contributing editor.
