jrb at cs.washington.edu
Computer Science & Engineering, Gates Center, Office 231
My resume is available here
About
I am a fourth year PhD student in Computer Science and Engineering at the University of Washington, advised by Kevin Jamieson. My research is in theoretical machine learning; my current project uses the tools from learning mixtures of distributions to improve multiple hypothesis testing. When an experiment involves testing many hypotheses simultaneously, we answer the question ''How many of these treatments are truly different from the control?'' using less data than we would need to identify those treatments.
Preprints
Dynamic Tensor Rematerialization, Marisa Kirisame, Steven Lyubomirsky, Altan Haan, Jennifer Brennan, Mike He, Jared Roesch, Tianqi Chen, Zachary Tatlock, Preprint. Paper
Publications
Estimating the Number and Effect Sizes of Non-null Hypotheses, Jennifer Brennan, Ramya Korlakai Vinayak and Kevin Jamieson, ICML 2020. Paper | Talk | Slides
Reconciliation feasibility in the presence of gene duplication, loss, and coalescence with multiple individuals per species, Jennifer Rogers, Andrew Fishberg, Nora Youngs and Yi-Chieh Wu, BMC Bioinformatics, 2017. 18:292 Paper