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, 10am to 11am
“Eliciting Human Preferences with Language Models”
Belinda Li a PhD candidate at MIT CSAIL, affiliated with the language & intelligence (LINGO) lab @ MIT. Her work focuses on improving the human-interpretability, reliability, and usability of language models: examining and improving representations of both (objective) world states and (subjective) human preferences in language models. 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, obtained her B.S. in Computer Science at the University of Washington.
Friday May 31, 12 to 1 pm
Leveraging Generative AI for Political Persuasion: Experimental Insights into Message Effectiveness and Democratic Reciprocity
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.
Wednesday June 26, 1 to 2 pm
The effects of Facebook and Instagram on the 2020 election: A deactivation experiment
The talk will be based on this paper which studies the effect of Facebook and Instagram access on political beliefs, attitudes, and behavior by randomizing a subset of 19,857 Facebook users and 15,585 Instagram users to deactivate their accounts for 6 weeks before the 2020 U.S. election.
Matthew Gentzkow is the Landau Professor of Technology and the Economy at Stanford University. He studies applied microeconomics with a focus on media and technology industries. He received the 2014 John Bates Clark Medal, given by the American Economic Association to the American economist under the age of forty who has made the most significant contribution to economic thought and knowledge. He is a member of the National Academy of Sciences, a fellow of the American Academy of Arts and Sciences, a fellow of the Econometric Society, a senior fellow at the Stanford Institute for Economic Policy Research, and the Editor of American Economic Review: Insights.
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.
Friday July 5, 12 to 1 pm
Evaluating the persuasive influence of political microtargeting with large language models
Kobi Hackenburg is a PhD candidate in Social Data Science at the Oxford Internet Institute, University of Oxford. His doctoral research, funded by a Clarendon Scholarship and supervised by Helen Margetts and Scott Hale, investigates the persuasive influence of personalized AI systems. More broadly, his work lies at the intersection of computation, language, and society. Alongside his PhD, he works as a Doctoral Researcher in the Public Policy Programme at The Alan Turing Institute, the UK’s national institute for AI and data science.
Thursday July 11, 9 to 10 am
BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions
Terry Yue Zhuo is a PhD candidate in Computer Science at Monash University and the CSIRO’s Data61. He holds a Bachelor of Computer Science (Honours) from Monash University. He is additionally an associate member of the Sea AI Lab, a visiting scholar at Singapore Management University, and a research technician at CSIRO’s Data61. His research has been published at venues including EMNLP, ICLR, EACL, and TMLR.
Friday July 12, 12 to 1 pm
LiveCodeBench: Holistic and contamination free evaluation of large language models for code
Naman Jain is a CS Ph.D. student at UC Berkeley, focusing on using machine learning to enhance developer productivity tools like program analysis, synthesis, and repair. He also explores how synthesis and verification can improve algorithm generalizability and explainability. He holds an undergraduate degree from IIT Bombay, where he researched NLP robustness and computer vision. Before his Ph.D., he was a predoctoral research fellow at Microsoft Research India, working on program repair, improving large language models, and learning decision trees.
Thursday July 18, 12 to 1 pm
What does it mean for AI to be “aligned”
Hannah Rose Kirk is a DPhil candidate in Social Data Science at the Oxford Internet Institute, University of Oxford, with a research focus on the alignment of LLMs through granular and diverse human feedback. Her research has been published in venues including Nature Machine Intelligence, NAACL, EMNLP, and NeurIPs. She has previously worked with researchers at NYU, Google, OpenAI, and The Alan Turing Institute.
Friday July 19, 12 to 1 pm
Improving Steering and Verification in AI-Assisted Data Analysis
Majeed Kazemitabaar is a fourth-year Ph.D. student in Computer Science at the University of Toronto, working with Professor Tovi Grossman. His research in Human-Computer Interaction and Computer Science Education focuses on creating engaging computational learning experiences and has been published in CHI, UIST, and IDC, and earned Best Paper and Best Late-Breaking Work awards.
