Speaker: Jake Soloff, Department of Statistics, University of Michigan

Time: Wednesday, April 8, from 2:30 PM to 3:30 PM

Location for in-person participants: 1146 FAB 

Zoom link for online audience: https://wayne-edu.zoom.us/j/92845590121?pwd=CpRA5Wa5gzSMn2xiVkR2abD83O5nrH.1

Title: Calibrated hypothesis testing

Abstract: Among error criteria for large-scale hypothesis testing, the local false discovery rate (lfdr) is the one most directly tied to the reliability of an individual finding. Its interpretation, however, traditionally relies on a Bayesian two-groups model. This talk develops new perspectives on empirical Bayes and compound-decision inference in this context. The guiding idea is to seek procedures that, without prior knowledge, attain in expectation the same local guarantees that an oracle Bayes rule attains almost surely. First, I introduce a simple, nonparametric procedure that exactly controls the expected maximum lfdr among the discoveries; equivalently, the probability that the least promising discovery is false. I will then discuss recent and ongoing work connecting multiple testing to probabilistic forecasting, giving a prior-free interpretation of lfdr as a calibrated conditional error probability and suggesting a broader framework for interpretation of compound decisions. 

Bio: Dr. Soloff is an Assistant Professor of Statistics at the University of Michigan. His research focuses on the theoretical foundations of data science and machine learning, with the goal of creating and understanding off-the-shelf tools that are both principled and broadly applicable. He received his Ph.D. in Statistics from UC Berkeley in 2022, advised by Aditya Guntuboyina and Michael I. Jordan, and completed a postdoc at the University of Chicago with Rina Foygel Barber and Rebecca Willett.

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AIDaS: CAD Seminar Series

Advancing Knowledge, Innovation, and Collaboration in Computation, AI, and Data Science (CAD)

The CAD Seminar Series is a primary seminar series at Wayne State University’s Institute for AI and Data Science (AIDaS). It is a dedicated platform for advancing knowledge, fostering innovation, and promoting collaboration across the fields of Computation, Artificial Intelligence, and Data Science. This series brings together leading experts, researchers, and professionals to explore the latest developments, tackle emerging challenges, and drive forward-thinking solutions at the convergence of these critical disciplines.

Objectives:

• Advance Knowledge: Share cutting-edge research and insights that push the boundaries of what is known in CAD.

• Foster Innovation: Encourage the development of novel ideas and solutions through interdisciplinary dialogue and creative thinking.

• Promote Collaboration: Unite expertise across disciplines and build bridges between academia, industry, and government to address complex problems and create opportunities for joint ventures.

Target Audience: The CAD Seminar Series is designed for a diverse audience, including faculty, researchers, students, and professionals in Computation, AI, Data Science, and related fields. It serves as a forum for exchanging ideas, networking, and contributing to the growth of these rapidly evolving areas. We highly recommend in-person attendance to enhance engagement and networking opportunities with speakers and fellow participants.

Call for Participation: We welcome contributions from researchers, practitioners, and students. Whether presenting your work, participating in discussions, or attending as a learner, your involvement is crucial to the success of this collaborative initiative.