Please take the elevators to the 7th floor to check in and get your name badge. Enjoy breakfast and networking with other conference attendees and speakers.

• David Holtz – Assistant Professor, UC Berkeley Haas School of Business and CODE@MIT Organizer

• Iavor Bojinov – Associate Professor, Harvard Business School
• Emil Palikot – Assistant Professor, Northeastern

Enjoy a 30-minute coffee and networking break.

Session A: Estimating Long-term Treatment Effects (DR 4, 6th Floor)

1. The Bias-Variance Tradeoff in Long Term Experimentation

   • Daniel Ting (Meta), Kenneth Hung (Meta)

2. Estimating Annualized, Population-Level Effects from Short-Term Experiments

   • Tomoya Sasaki (Netflix), Simon Ejdemyr (Netflix), Mihir Tendulkar (Netflix)

3. Many Hands Make Light Work: Long-Term Effect Inference for the Experiment Reservoir

   • Will Hartog (Stanford), Lihua Lei (Stanford), Mandar Rahurkar (DoorDash), Navdeep Sahni (Stanford), Yixin Tang (Galileo Technologies)

4. Beyond the Experiment Window: Prospective Impacts Under Long-Term Ranking Dynamics

   • Lei Shi (UC Berkeley), Lo-Hua Yuan (Airbnb), Peng Ding (UC Berkeley), Navin Sivanandam (Airbnb)

Session B: LLMs and Experimentation (Salon MIT, 7th Floor)

1. Causal Alignment: Augmenting Language Models with A/B Tests

   • Panagiotis Angelopoulos (Persado), Kevin Lee (University of Michigan), Sanjog Misra (University of Chicago)

2. LLM-Driven Causal Inference: A Multi-Agent Bayesian Inference Framework

   • Chen Wang (HKU), Shan Huang (HKU), Shichao Han (Tencent), Yong Wang (Tencent)

3. Causal Inference in Experiments with Mixed-Subjects Designs

   • Austin van Loon (MIT Sloan), Klint Kanopka (New York University)

4. General Social Agents

   • Benjamin S. Manning (MIT), John J. Horton (MIT & NBER)

Session C: Online Platforms (DR 3, 6th Floor)

1. Trolls vs. Allies: The Impact of Comments on Social Media Campaigns

   • Dante Donati (Columbia University), Lena Song (University of Illinois Urbana-Champaign)

2. The Dynamics of Online Participation: Evidence from a Field Experiment with Generative AI Agents

   • Wajeeha Ahmad (Stanford)

3. The Long and the Short of Audio Advertising: Experiments on Ad Length and Frequency

   • Ali Goli (University of Rochester), David Reiley (Independent), Jonas Tungodden (Independent), Wenfeng Qiu (Sirius XM Holdings Inc.)

4. What Is Your AI Agent Buying? Evaluation, Implications, and Emerging Questions for Agentic E-Commerce

   • Amine Allouah (MyCustomAI), Omar Besbes (Columbia Business School), Josue Figueroa (MyCustomAI), Yash Kanoria (Columbia Business School), Akshit Kumar (Yale University)

1. Post Launch Evaluation of Policies in a High-Dimensional Setting

Shima Nassiri (Lyft), Mohsen Bayati (Stanford & Amazon), Joe Cooprider (Amazon)

2. Efficient Statistical Estimation for Sequential Adaptive Experiments with Implications for Adaptive Designs

Wenxin Zhang (Berkeley), Mark van der Laan (Berkeley)

3. Smooth Operators: Measuring the Effect of LLM “Personality” on Negotiation Outcomes

Michelle Vaccaro (MIT), Mohammed Alsobay (MIT), David Fang (Stanford), Abdullah Almaatouq (MIT), Jared Curhan (MIT)

4. Case study: learning robust, long-run surrogate metrics with modeling and instrumental variables

Daniel Day (Meta), Matthew Dalton (Meta), Sam Daulton (Meta), Ben Letham (Meta), Xiao Cai (Meta), Aojia Zhao (Meta), Eytan Bakshy (Meta), Akos Lada (Meta)

5. Enhancing Precision in Online HR Experiments: A Comparison of Covariate-Adaptive Randomization Approaches

Wanqun Zhao (Amazon), Marina Dias (Amazon), Shahriar Sadighi (Amazon), Naumaan Nayyar (Amazon)

6. Designing AI-Human Supervision to Improve Worker Performance: A Field Experiment in Service Operations

Xueming Luo (Temple), Bing Bai (McGill), Zheng Fang (Sichuan), He Peng (Fudan)

7. Scalable Optimization for Dunnett Test in Industrial-Scale A/B Testing

Chunkai Wang (ByteDance), Lingyun Li (Columbia), Junpeng Gong (UCAS), Jinyong Ma (ByteDance)

8. Synthetic Control Method under Spatial Interference: Detecting and Correcting Bias

Joao Alipio-Correa (University of Pittsburgh, Departments of Political Science & Statistics)

9. Shrinkage-Based Regressions with Many Related Treatments

Enes Dilber (Wayfair), Colin Gray (Wayfair)

10. The Generative AI – Gender Puzzle

Manuel Hoffmann, Sam Boysel, Frank Nagle, Kevin Xu

11. Regular Balanced Switchback Designs for Robust Multi-Unit Online Experimentation

Lorenzo Masoero, Paul Missault, Christian Delbé, Thomas Richardson, Guido Imbens

12. Experimental Design for Product Launches with Collaborative User Networks

Chiraag Kala (Airbnb), Jeffrey Wong (Airbnb)

13. Voice AI in Firms: A Natural Field Experiment on Automated Job Interviews

Brian Jabarian (Chicago Booth), Luca Henkel (Rotterdam)

14. Covariate Adjustment Cannot Hurt: Treatment Effect Estimation under Interference with Low-Order Outcome Interactions

Xinyi Wang (UC Berkeley), Shuangning Li (Chicago Booth)

15. Beyond Normality: A/B Testing under Skewness and Unequal Allocation

Junpeng Gong (UCAS), Chunkai Wang (ByteDance), Hao Li (ByteDance), Jinyong Ma (ByteDance)

16. Continuous content management through short, repeated, and re-randomized experiments

Merlin Heidemanns (Amazon), Ryan Shyu (Amazon), Mitchell Goodman (Amazon)

17. Introducing AI to an Online Petition Platform Changed Outputs but not Outcomes

Isabel Corpus (Cornell), Eric Gilbert (University of Michigan), Allison Koenecke (Cornell), Mor Naaman (Cornell Tech)

18. Generative AI and Musical Creativity

Jose Oros (CMU), Richard Randall (CMU), Rahul Telang (CMU)

19. Designing Synthetic Control Experiments with Forward Selection

Lawrence R. De Geest (Lyft), Amber Wang (Lyft)

20. Can hate speech be banned online? The effects of shutting down toxic forums on Reddit

Adam Di Lizia (Warwick), Lily Shevchenko (Warwick)

Session D: Evaluation of Algorithms (DR 4, 6th Floor)

1. Algorithmic Auditing Using Revealed Preference and Experimental Variation

   • Andrey Fradkin, Giorgos Zervas, Anonymous 1, Madhav Kumar, Anonymous 2, Anonymous 3

2. Dissecting Temporal Treatment Effects of Recommendation Algorithms via Mediation Analysis

   • Aleksei Opacic (Harvard; work performed during internship at Netflix), Si Cheng (Netflix), Wei Wang (Netflix)

3. Algorithms, Biases, and Belief Polarization

   • Varad Deolankar (National University of Singapore), Jessica Fong (University of Michigan), S. Sriram (University of Michigan)

4. The Missing Lever in Machine Learning Deployment: Threshold Tuning using Regression Discontinuity

   • Lucas Squarize Chagas (Intuit)

Session E: Labor (DR 3, 6th Floor)

1. Perceived AI Use and Hiring Penalties: Evidence from a Field Experiment

   • Sunny Qingyi Wang (UMSI), Jingyi Qiu (UMSI)

2. Algorithm as Boss or Coworker? Randomized Field Experiment on Algorithmic Control and Collaboration in Gig Platform

   • Sojung Yoon (University of Minnesota), Nakyung Kyung (National University of Singapore), Jason Chan (University of Minnesota), Yeonseo Kim (KAIST)

3. Algorithm-enabled Decision Support and Worker Learning: A Large-Scale Field Experiment

   • Youngsoo Kim (Loyola University), Benjamin Knight (Instacart), Dmitry Mitrofanov (Boston College), Yuqian Xu (UNC)

Session F: Experimentation Programs and Learning Across Experiments (Salon MIT, 7th Floor)

1. Predicting Website A/B Test Outcomes Using a Supervised Vision Transformer

   • Nitish Prakash (Spiralyze), Gajan Retnasaba (Spiralyze)

2. Compound Selection Decisions: An Almost SURE Approach

   • Jiafeng Chen (Stanford), Lihua Lei (Stanford), Timothy Sudijono (Stanford), Liyang Sun (UCL and CEMFI), Tian Xie (UCL)

3. Optimal Decisions for Repeated A/B Testing Programs

   • Ron Berman (Wharton), Elea Feit (Drexel)

4. Large Language Model–Based Research Synthesis Identifies Paper Collections with Greater Predictive Value

   • Robin Na (MIT), Duncan Watts (University of Pennsylvania), Abdullah Almaatouq (MIT)

Enjoy a 30-minute coffee and networking break.

Session G: Optimizing Sampling and Experiment Duration (Salon MIT, 7th Floor)

1. Towards Optimal Sample Size in Organizational Experimentation

   • Jonah Rockoff (Columbia Business School)

2. Optimizing Duration of Online Experiments via Bayesian Early Termination

   • Lorenzo Masoero (Amazon), Melany Gualavisi (Amazon)

3. Pilot Study Design to Maximize Impact under Significance Testing

   • Isaac Scheinfeld (Columbia Business School), Hannah Li (Columbia Business School), Hongseok Namkoong (Columbia Business School)

4. Stratification Benefits in Experimental Design: Evidence from Multiple Contexts

   • Melany Gualavisi (Amazon), Stefan Hut (Amazon), Mahnaz Islam (Amazon)

Session H: Education & Health (DR 3, 6th Floor)

1. How Effective is Subsidizing Digital Educational Content? Evidence from a Field Experiment

   • Jingcun Cao (HKU), Xiru Pan (University of Miami), Catherine Tucker (MIT), Yifei Wang (University of Pittsburgh)

2. Phones Impede Learning: Evidence from a Massive Phone-Free Classroom Experiment

   • Alp Sungu (Wharton, University of Pennsylvania), Pradeep Choudhury (Jawaharlal Nehru University), Andreas Nielsen (University of Copenhagen)

3. Data Fusion for High-Resolution Estimation

   • Amy Guan (Stanford), Marissa Reitsma (Stanford), Roshni Sahoo (Stanford), Joshua Salomon (Stanford), Stefan Wager (Stanford)

4. Causal Effects of Teachers’ Assignment Report Usage in ITS: A Large-Scale Encouragement Study

   • Wen-Chiang Ivan Lim (WPI), Neil Heffernan (WPI), Adam Sales (WPI)

Session I: From Experimentation to Policy Learning (DR 4, 6th Floor)

1. Policy-Aligned Estimation of Heterogeneous Treatment Effects

   • Artem Timoshenko (Northwestern), Caio Waisman (Northwestern)

2. Multi-Action Policy Learning with an M-ary Decision Tree

   • Shusei Eshima (Netflix)

3. Discretion in the Loop: Human Expertise in Algorithm-Assisted College Advising

   • Kara Schechtman (Princeton University), Benjamin Brandon (Georgia State University), Jenise Stafford (Georgia State University), Hannah Li (Columbia Business School), Lydia T. Liu (Princeton University)

Enjoy a 30-minute coffee and networking break.

Session Moderator:

• David Holtz – Assistant Professor, UC Berkeley Haas School of Business and CODE@MIT Organizer

Panelists:

• Sean Taylor – Technical Staff, OpenAI
• Abhishek Nagaraj – Associate Professor, UC Berkeley Haas
• Julia Neagu – CEO & Co-founder, Quotient AI
• Siddharth Suri – Senior Principal Researcher, Microsoft Research

Coming soon.

Enjoy a networking reception featuring the work of our poster presenters on display and poster presenter authors available for questions.

See you tomorrow!

Please take the elevators to the 7th floor to check in and get your name badge. Enjoy breakfast and networking with other conference attendees and speakers.

• David Holtz – Assistant Professor, UC Berkeley Haas School of Business and CODE@MIT Organizer

• Ramesh Johari – Professor, Management Science and Engineering, Stanford University
• Hongseok Namkoong – Assistant Professor, Columbia University

Please enjoy a 15-minute coffee and networking break.

Session J: Politics & Government (DR 3, 6th Floor)

1. Algorithmic Amplification is Value-Aligned for Republicans but Misaligned for Democrats on X/Twitter

   • Ziv Epstein (MIT), Farnaz Jahanbakhsh (University of Michigan), Tiziano Piccardi (Stanford University), Axel Peytavin (Stanford University), Shardul Sapkota (Stanford University), Johan Ugander (Yale University), Michael S. Bernstein (Stanford University)

2. Auditing Government Ad Delivery Skew in Practice

   • Isabel Corpus (Cornell), Allison Koenecke (Cornell)

3. LLMs in Public Services: How Does Chatbot Accuracy Affect Human Accuracy?

   • Jennah Gosciak (Cornell University), Eric Giannella (Georgetown University), Zhaowen Guo (Georgetown University), Michael Chen (Nava Labs), Allison Koenecke (Cornell University)

4. Do Brief How-To Trainings Improve Human Detection of AI-Generated Images? Evidence from a Randomized Study with Government Analysts

   • Negar Kamali (Northwestern), Matt Groh (Northwestern)

Session K: Privacy (DR 4, 6th Floor)

1. Blind Targeting: Personalization under Third-Party Privacy Constraints

   • Anya Shchetkina (Wharton)

2. Can Privacy Technologies Replace Cookies? Ad Revenue in a Field Experiment

   • Zhengrong Gu (Boston University), Garrett Johnson (Boston University), Shunto Kobayashi (Boston University)

3. Beyond ATE — Multi-Criteria Design for Adaptive A/B Testing with Privacy

   • Jiachun Li (MIT), Kaining Shi (University of Chicago), David Simchi-Levi (MIT)

4. Measuring Advertising Effects in Privacy-Constrained Geo Experiments

   • Malika Korganbekova (University of Chicago), Jerry Chen (Wayfair), Faith Shin (Wayfair), Yusheng Jiao (Wayfair)

Session L: Bayesian Inference (Salon MIT, 7th Floor)

1. Trustworthy Bayesian Inference in Batch-Adaptive Experimentation

   • Yicheng Li (Airbnb), Yan Wang (Airbnb), Jiayun Dong (Airbnb)

2. Breaking the Winner’s Curse with Bayesian Hybrid Shrinkage

   • Richard Mudd (Meta), Rina Friedberg (Meta), Ilya Gorbachev (Meta), Houssam Nassif (Meta), Abbas Zaidi (Meta)

3. Bayesian Predictive Probabilities for Online Experimentation

   • Abbas Zaidi (Meta Inc.), Rina Friedberg (Meta Inc.), Houssam Nassif (Meta Inc.), Samir Khan (Meta Inc.), Yao-Yang Leow (Meta Inc.), Maulik Soneji (Meta Inc.), Richard Mudd (Meta Inc.)

4. Unifying Statistical Power and Bayesian Inference in Online Experiments

   • Melany Gualavisi (Amazon), Ryan Kessler (Amazon), Lorenzo Masoero (Amazon)

Session M: Off-Policy Evaluation, Heterogeneous Treatment Effects, and Targeting (DR 5+6, 6th Floor)

1. Off-Policy Evaluation in Markov Decision Processes under Weak Distributional Overlap

   • Mohammad Mehrabi (Stanford University), Stefan Wager (Stanford University)

2. Minimax Regret Estimation for Generalizing Heterogeneous Treatment Effects with Multisite Data

   • Yi Zhang (Netflix), Melody Huang (Yale), Kosuke Imai (Harvard)

3. Optimal Targeting in Dynamic Systems

   • Yuchen Hu (MIT), Shuangning Li (University of Chicago), Stefan Wager (Stanford University)

4. Detecting and Mitigating Group Bias in Heterogeneous Treatment Effects with Experiments: Theory, Methods, and Implications

   • Joel Persson (Spotify), Jurriën Bakker (Booking.com), Dennis Bohle (Booking.com), Stefan Feuerriegel (Munich Center for Machine Learning & LMU Munich), Florian von Wangenheim (ETH Zurich)

Please enjoy a 15-minute coffee and networking break.

Moderator:

• Dean Eckles – Professor, MIT Sloan and CODE@MIT Organizer

Panelists:

• Brent Cohn – Data Science and Machine Learning Manager, Stripe
• Thu Le – Senior Staff Data Scientist, Lyft Inc
• Guillaume Saint-Jacques – Director, Roblox
• Bilal Zia –  Head of Data Science & Analytics, Duolingo

20. Shrinkage with Binomials

• Yan Chen (Duke), Lihua Lei (Stanford)

21. Designing Synthetic Control Experiments with Forward Selection

• Lawrence R. De Geest (Lyft), Amber Wang (Lyft)

22. Dynamic Personalization with Multiple Customer Signals: Multi-Response State Representation in Reinforcement Learning

• Liangzong Ma (Harvard Business School), Ta-Wei Huang (National University of Singapore), Eva Ascarza (Harvard Business School), Ayelet Israeli (Harvard Business School)

23. Computationally Efficient On-Demand Experiment Platform

• Thu Le (Lyft), Lawrence De Geest (Lyft)

24. Can Hate Speech Be Banned Online? The Effects of Shutting Down Toxic Forums on Reddit

• Adam Di Lizia (Warwick), Lily Shevchenko (Warwick)

25. Dot Plots and Posterior Density Plots Improve User Inferences About Experimental Results

• Joni Rustulka (LaunchDarkly), Zachary Horne (University of Edinburgh)

26. Generalized Geo-Based Causal Framework for Strategy Evaluation in Spatially Distributed Systems

• Bhavnish Walia (Amazon), Banani Mohapatra (Walmart), Manisha Arora (Google)

27. Improving Lifecycle Regret with Tie-Breaker Designs

• Apoorva Lal (Amazon), Kyle Woodward (Amazon)

28. Diagnosing Time-Varying Treatment Effects in Online Control Experiments

• Lu Deng (Tencent), Jingjing Zhang (Tencent), Yilin Li (Tencent), Yong Wang (Tencent), Chuan Chen (Tencent)

29. Panel Experiment Design for Two-Way Fixed-Effects Regression

• Dmitry Arkhangelsky (CEMFI), Guido Imbens (Stanford), Lihua Lei (Stanford), Xiaoman Luo (Roblox)

30. Echoes of the Ping: How Push Notifications Shape App Engagement and Its Implications for Targeting

• Moritz von Zahn, Arda Güler, Kevin Bauer, Oliver Hinz

31. Sponsored Search Incrementality Testing in Online Retail Marketplaces: Enhance Precision from Promoted Items

• Joel Barajas (Walmart Global Tech), Kuang-Chih Lee (Walmart Global Tech)

32. Directed Search with AI: Evidence from an Experiment with Entry-Level Job Seekers

• Achyuta Adhvaryu (UCSD, NBER, and BREAD), Sofía Calderón (University of Notre Dame), Jean-François Gauthier (HEC Montréal), Parker Howell (University of Michigan), Anant Nyshadham (University of Michigan and NBER), Jorge Tamayo (Harvard Business School)

33. A Simulation Framework for Off-Policy Evaluation of Ranking Models

• Dan Qiao (UCSB), Mohsen Malmir (Meta), Houssam Nassif (Meta), Murat Bayir (Meta)

34. Treatment Effect Learning Under Sequential Randomization

• Rina Friedberg (Meta), Richard Mudd (Meta), Patrick Johnstone (Meta), Melissa Pothen (Meta), Vishal Vaingankar (Meta), Abbas Zaidi (Meta)

35. Measuring What Matters: A Forward-Engineered Approach to Causal Inference with Endogenous Staggered Adoption

• Tom Hackenberg (TU/e)

36. Admissibility of Completely Randomized Trials: A Large-Deviation Approach

• Guido Imbens (Stanford), Chao Qin (Stanford), Stefan Wager (Stanford)

37. Wrong but Better: When Pretrained Models Win in AI-Assisted Inference

• Ginnie Ma (Stanford), Lihua Lei (Stanford)

38. Evaluating Variance Estimates with Relative Efficiency

• Kedar Karhadkar (UCLA), Jack Klys (Meta), Daniel Ting (Meta), Artem Vorozhtsov (Meta), Houssam Nassif (Meta)

39. From Meta-Encouragement to MTE: Estimating ATE, ATT and LATE from Hundreds of Business-as-Usual Randomized Controlled Trials

• Nadia Tomova (Booking.com)

Session N: Adaptive Experimentation (DR 4, 6th Floor)

1. The Power of Adaptivity in Experimental Design

   • David Simchi-Levi (MIT), Yunxiao Zhao (University of Michigan)

2. Simulation-Based Inference for Adaptive Experiments

   • Brian Cho (Cornell), Aurélien Bibaut (Netflix), Nathan Kallus (Cornell, Netflix)

3. Anytime-Valid Inference in Adaptive Experiments: Covariate Adjustment and Balanced Power

   • Daniel Molitor (Cornell), Samantha Gold (Cornell)

4. Adaptive Multi-Objective Optimization for Primary and Guardrail Criteria

   • Blake Mason (Amazon), James McQueen (Amazon)

5. Choosing the Better Bandit Algorithm under Data Sharing: When Do A/B Experiments Work?
   • Shuangning Li (University of Chicago), Chonghuan Wang (UT Dallas), Jingyan Wang (TTIC)

Session O: Consumer Behavior (DR 3, 6th Floor)

1. Experiment Design for Choice Estimation and Nest Identification

   • Will Ma (Columbia), Xintong Yu (Columbia), Michael Zhao (Dream11)

2. The Internet Behavior Experiment (IBE): An Experimental Infrastructure to Study Online Behavior and Consumer Welfare

   • Cristobal Cheyre (Cornell), Li Jiang (George Washington University), Florian Schaub (University of Michigan), Zijun Ding (Carnegie Mellon University), Cristiana Firullo (Cornell), Yucheng Li (Carnegie Mellon University), Alessandro Acquisti (MIT)

3. Optimal Exploration of New Products under Assortment Decisions

   • Jackie Baek (NYU), Atanas Dinev (MIT), Thodoris Lykouris (MIT)

4. Experimentally Guided Recommendations for Assortment Optimization

   • Jia Wan (MIT), Guy Aridor (Northwestern), Nathan Kallus (Cornell & Netflix), Aurélien Bibaut (Netflix)

Session P: Human-AI Interaction (Salon MIT, 7th Floor)

1. Personality Pairing Improves Human-AI Collaboration: A Large-Scale Experiment

   • Harang Ju (Johns Hopkins University), Sinan Aral (MIT)

2. Biased AI Enhances Human Decision-Making but Reduces Trust

   • Shiyang Lai (University of Chicago), Junsol Kim (University of Chicago), Nadav Kunievsky (University of Chicago), Yujin Potter (UC Berkeley), James Evans (University of Chicago)

3. Human Learning about AI

   • Bnaya Dreyfuss (Harvard), Raphaël Raux (MIT Sloan)

4. Calibrated Coarsening: Designing Information for AI-Assisted Decisions

   • Ruru Hoong (Harvard), Bnaya Dreyfuss (Harvard)

5. Boosting Empathic Communication Skills Using LLM Role-Playing Games

   • Aakriti Kumar (Northwestern), Nalin Poungpeth (Northwestern), Diyi Yang (Stanford), Bruce Lambert (Northwestern), Matthew Groh (Northwestern)

Session Q: Interference (DR 5+6, 6th Floor)

1. Validating Counterfactual Estimations under General Interference

   • Sadegh Shirani (Stanford GSB), Yuwei Luo (Stanford GSB), William Overman (Stanford GSB), Ruoxuan Xiong (Emory University), Mohsen Bayati (Stanford GSB)

2. Double Machine Learning for Causal Inference under Shared-State Interference

   • Chris Hays (MIT), Manish Raghavan (MIT)

3. Estimating Total Effects in Bipartite Experiments with Spillovers and Partial Eligibility

   • Albert Tan (Amazon), Mohsen Bayati (Stanford), James Nordlund (Amazon), Roman Istomin (Amazon)

4. Policy Relevance of Causal Quantities in Networks

   • Sahil Loomba (MIT), Dean Eckles (MIT)

5. When Does Interference Matter? Decision-Making in Platform Experiments

   • Ramesh Johari (Stanford MS&E), Hannah Li (Columbia Business School), Anushka Murthy (Stanford MS&E), Gabriel Weintraub (Stanford GSB)

Enjoy a 10-minute networking and coffee break.

• Yan Chen – Daniel Kahneman Collegiate Professor of Information, University of Michigan
• Sherry Jueyu Wu –  Assistant Professor, UCLA

Coming soon.

Enjoy a networking reception featuring the work of our poster presenters on display and poster presenter authors available for questions.

Thank you for attending – see you in 2026!

Iavor Bojinov is an Associate Professor of Business Administration at Harvard Business School. He is the co-PI of the AI and Data Science Operations Lab and a faculty affiliate in the Department of Statistics at Harvard University and the Harvard Data Science Initiative. His research focuses on developing novel statistical methodologies to make business experimentation more rigorous, safer, and efficient, specifically homing in on the application of experimentation to the operationalization of artificial intelligence (AI), the process by which AI products are developed and integrated into real-world applications. His work has been published in top academic journals such as Annals of Applied Statistics, Biometrika, The Journal of the American Statistical Association, The Journal of Econometrics, Quantitative Economics, Management Science, and Science, and has been cited in Forbes, The New York Times, The Washington Post, and Reuters, among other outlets. More broadly, as one of the few scholars who work at the intersection of data science and business, he was the first author to have spotlight featured articles in both the Harvard Business Review and the Harvard Data Science Review.

Yan Chen is the Daniel Kahneman Collegiate Professor in the School of Information at the University of Michigan. Her research interests are in behavioral and experimental economics, market design, and digital economics. She was the president (2015–17) of the Economic Science Association and the recipient of the 2019 Carolyn Shaw Bell Award. She is a Fellow of the Economic Science Association.

Brent is a data scientist and machine learning engineering manager at Stripe. He leads teams focused on optimizing payments.

Ramesh Johari is a Professor at Stanford University, with a full-time appointment in the Department of Management Science and Engineering (MS&E), and a courtesy appointment in the Department of Electrical Engineering (EE). He is also an Associate Director of Stanford Data Science and Co-Director of the Stanford Causal Science Center. His research focuses on data science methods and practice, as well as the design and operation of online markets and platforms. He has an extensive track record of collaboration with industry on these topics, ranging from full-time data science leadership to advising, consulting, and research collaboration. Past and present partners include Airbnb, Boam, Bumble, oDesk/Upwork, Optimizely, Stitch Fix, Stripe, and Uber.

Thu is a Senior Staff Data Scientist leading Lyft’s Experimentation Platform, where she develops causal inference systems that drive better decisions for riders and drivers alike. She also heads the Pricing Artificial Intelligence at Lyft (PAIL) initiative—a holistic platform for scenario-based pricing that ensures a balanced and healthy marketplace. Previously, Thu led smart pricing modeling and inference at Airbnb and designed large-scale experiments at Facebook, giving her a 360-degree perspective on data-driven growth and the strategic role of experimentation in business decision-making.

Hongseok Namkoong is an Assistant Professor in the Decision, Risk, and Operations division at Columbia Business School and a member of the Columbia Data Science Institute. His research builds trustworthy AI systems for real-world decision-making problems. As an interdisciplinary researcher, he draws upon tools from machine learning and operations research. Hong’s research has been recognized at leading AI and OR research outlets, including paper awards at NeurIPS, ICML, CVPR, ICLR, and INFORMS Applied Probability Society. He received his Ph.D. from Stanford University and worked as a research scientist at Facebook Core Data Science before joining Columbia. Outside of academia, he served as a LinkedIn Scholar at LinkedIn’s Core AI team.

Abhishek Nagaraj is an Associate Professor in the Management and Organizations (MORS) and the Entrepreneurship and Innovation group at the Haas School of Business, UC Berkeley. He is also a Faculty Research Fellow at the National Bureau of Economic Research (NBER) and the Director of the Data Innovation and AI Lab (DIAL). He earned his Ph.D. in Management at the MIT Sloan School of Management, in the Technological Innovation, Entrepreneurship and Strategic Management (TIES) group. Professor Nagaraj’s research is in the area of innovation economics, entrepreneurship, and the economics of AI.

Julia is the co-founder and CEO of Quotient AI, building the critical monitoring layer for AI agents in production. Before founding Quotient, Julia was the Director of Data for GitHub Copilot, where her team built the systems behind one of the world’s most widely used AI coding assistants. She previously served as Director of Analytics at Tamr and built quantitative models for Aon’s Intellectual Property Solutions group. Julia holds a Ph.D. and M.A. in Physics from Harvard and an A.B. in Physics from Princeton.

Emil Palikot is an Assistant Professor of Marketing at Northeastern University and a research affiliate at the Golub Capital Social Impact Lab at Stanford University. His research examines how AI and digital platforms reshape labor markets, with a focus on recruitment systems, algorithmic hiring, and the economics of digitization in work. His work combines large-scale field experiments with causal inference and machine learning methods to understand how technology interventions affect hiring decisions, career development, and economic opportunity.

Guillaume is the Director of Engine and Creator Data Science at Roblox. Prior to this, he led data science and engineering teams at Apple and LinkedIn, with a focus on causal inference and experimentation. He holds a Ph.D. from MIT Sloan.

Siddharth Suri is a computational social scientist at Microsoft Research. His early work analyzed the relationship between network topology and human behavior. Since then, he became one of the leaders in designing, building, and conducting “virtual lab” experiments using Amazon’s Mechanical Turk. This research stream turned into a study of the on-demand workers who power many modern apps, websites, and AI systems, which culminated in Ghost Work (coauthored with Mary L. Gray). During and after the COVID-19 pandemic, he explored the future of work. Most recently, he has been analyzing the effects of AI on society.

Sean J. Taylor is a data scientist at OpenAI focused on model evaluation. Previously, he was a founder of Motif Analytics, where he developed data visualization and analysis software, a research scientist at Lyft, and a member of Facebook’s Core Data Science team. He holds a Ph.D. in Information Systems from NYU and a B.S. in Economics from the University of Pennsylvania.

Sherry Jueyu Wu is an Assistant Professor of Management and Organizations at the Anderson School of Management, University of California, Los Angeles (UCLA). Dr. Wu received her Ph.D. in Psychology at Princeton University with a doctoral joint degree in Social Policy and her B.A. in Psychology and Economics with highest honors from the University of Virginia. Dr. Wu’s research is centered around the complementary themes of interconnection (i.e., what motivates us for group participation and collective change) and inequality (i.e., what separates us into “us” versus “them” in organizational and social hierarchies). Her work is deeply grounded in real-world consequential issues surrounding democratic participation and inequality, leading to theory-driven applications in organizations and society at large. To do this, she employs a variety of research methods including large-scale field experiments, cross-cultural laboratory experiments, ethnography, and time-series designs. These methods advance both theoretical understanding of and pragmatic intervention in behavior and attitudes.

Bilal Zia leads the Data Science & Analytics team at Duolingo, where he blends data, AI, and strategy to help millions of people around the world learn more effectively. Before joining Duolingo, he helped build Amazon’s HR Central Science team and spent over 15 years at the World Bank working on financial inclusion and economic development. A Ph.D. economist trained at MIT, Bilal is passionate about using data to solve real-world problems—especially ones that sit at the crossroads of technology, policy, and impact.