Resources

Bohrer Lecture: David Dunson, Duke University

Talk Title: Interpretable AI in Scientific Applications

Talk Abstract: AI is largely based on deep neural networks (DNNs) which tend to be massively parameterized and fitted to immense datasets. In scientific applications, we often have smaller and noisier data than are used in the most successful AI domains and there is a critical need for interpretability, reproducibility, accurate uncertainty quantification in inferences, and ability to reliably fit models to modest sample size but high-dimensional datasets. With this in mind and motivated in particular by applications in ecology and neuroscience, this talk proposes Bayesian methods for unsupervised learning of multilayer latent structures under identifiability guarantees. The proposed methods bridge between DNNs and classical latent class and model-based clustering models, also adding to the literature on stochastic block models for networks.

 

Wijsman Lecture: Kosuke Imai, Harvard University

Talk Title: Does AI help humans make better decisions? 
Talk Abstract: The use of Artificial Intelligence (AI), or more generally data-driven algorithms, has become ubiquitous in today's society. Yet, in many cases and especially when stakes are high, humans still make final decisions. The critical question, therefore, is whether AI helps humans make better decisions compared to a human-alone or AI-alone system. We introduce a new methodological framework to empirically answer this question with a minimal set of assumptions. We measure a decision maker's ability to make correct decisions using standard classification metrics based on the baseline potential outcome. We consider a single-blinded and unconfounded treatment assignment, where the provision of AI-generated recommendations is assumed to be randomized across cases with humans making final decisions. Under this study design, we show how to compare the performance of three alternative decision-making systems --- human-alone, human-with-AI, and AI-alone. Importantly, the AI-alone system includes any individualized treatment assignment, including those that are not used in the original study. We also show when AI recommendations should be provided to a human-decision maker, and when one should follow such recommendations. We apply the proposed methodology to our own randomized controlled trial evaluating a pretrial risk assessment instrument. We find that the risk assessment recommendations do not improve the classification accuracy of a judge's decision to impose cash bail. Furthermore, we find that replacing a human judge with algorithms --- the risk assessment score and a large language model in particular --- leads to a worse classification performance.

 

Department of Statistics 40th Anniversary Lecture: Mikhail Belkin, University of California San Diego

Talk Title: Feature learning in kernal machines and applications to monitoring and steering LLMs
Talk Abstract: Classical kernel machines are a powerful and theoretically grounded method for data analysis. However, the are not adaptive to low-dimensional "features" in the data. In this talk I will discuss feature learning  introducing Recursive Feature Machines—a powerful method  designed for extracting relevant features from tabular data. I will discuss some of its interesting properties and, in particular, will show how this technique enables us to detect and precisely guide LLM behaviors toward almost any desired concept by manipulating a single fixed vector in the LLM activation space.

Past Bohrer Workshop Keynote Speakers

  • 1994 Mark Schervish, Carnegie Mellon University
  • 1995 Dan Naiman, Johns Hopkins University
  • 1997 Ross Leadbetter, University of North Carolina
  • 1998 Dennis Karney, University of Kansas
  • 1999 Erich Lehmann, University of California Berkeley
  • 2000 David Bartholomew, London School of Economics
  • 2001 Gary Koch, University of North Carolina
  • 2002 Robert Serfling, University of Texas Arlington
  • 2003 Peter Bickel, University of California Berkeley
  • 2004 Peter Imrey, Cleveland Clinic Foundation
  • 2005 John Marden, University of Illinois
  • 2006 Raymond Carroll, Texas A&M University
  • 2007 Mary Ellen Bock, Purdue University
  • 2008 Ker-Chau Li, UCLA
  • 2010 Zhiliang Ying, Columbia University
  • 2011 Minge Xie, Rutgers University
  • 2012 Xuming He, University of Michigan
  • 2013 Yuhong Yang, University of Minnesota
  • Sky Andrecheck, Cleveland Indians
  • 2014 Hua-Hua Chang, University of Illinois at Urbana-Champaign
  • 2015 Cun-Hui Zhang, Rutgers University
  • 2016 Lawrence Brown, University of Pennsylvania
  • 2017 Edward George, University of Pennsylvania
  • 2018 Regina Liu, Rutgers University
  • 2019 - 2020 Cancelled
  • 2021 Liza Levina, University of Michigan
  • 2022 Vijay Nair, University of Michigan
  • 2023 Andrew R. Barron, Yale University
  • 2024 Huixia Judy Wang, George Washington University
  • 2025 Dylan Small, University of Pennsylvania

Past Wijsman Lecturers

  • 2014 Zhiliang Ying, Columbia University
  • 2015 John Lafferty, University of Chicago
  • 2016 Xihong Lin, Harvard University
  • 2017 Nancy Reid, University of Toronto
  • 2018 Michael Kosorok, University of North Carolina at Chapel Hill
  • 2019 - 2020 Cancelled
  • 2021 Dean Foster, Amazon
  • 2022 Dawn Woodard, Uber
  • 2023 Jelena Bradic, University of California San Diego
  • 2024 Bodhisattva Sen, Columbia University
  • 2025 Ming Yuan, Columbia University