Tuesday, September 28
agenda | registration
This event is free and open to all interested students and faculty, whether currently affiliated with the CMB program or not.
Keynote Speakers:
Daniela Witten | University of Washington
"Selective inference for trees"
Abstract: As datasets grow in size, the focus of data collection has increasingly shifted away from testing pre-specified hypotheses, and towards hypothesis generation. Researchers are often interested in performing an exploratory data analysis to generate hypotheses, and then testing those hypotheses on the same data. Unfortunately, this type of 'double dipping' can lead to highly-inflated Type 1 errors. In this talk, I will consider double-dipping on trees. First, I will focus on trees generated via hierarchical clustering, and will consider testing the null hypothesis of equality of cluster means. I will propose a test for a difference in means between estimated clusters that accounts for the cluster estimation process, using a selective inference framework. Second, I'll consider trees generated using the CART procedure, and will again use selective inference to conduct inference on the means of the terminal nodes. Applications include single-cell RNA-sequencing data and the Box Lunch Study. This is collaborative work with Lucy Gao (U. Waterloo), Anna Neufeld (U. Washington), and Jacob Bien (USC).
Bio: Daniela Witten is a professor of Statistics and Biostatistics at University of Washington, and the Dorothy Gilford Endowed Chair in Mathematical Statistics. She develops statistical machine learning methods for high-dimensional data, with a focus on unsupervised learning. Daniela is the recipient of an NIH Director's Early Independence Award, a Sloan Research Fellowship, an NSF CAREER Award, a Simons Investigator Award in Mathematical Modeling of Living Systems, a David Byar Award, a Gertrude Cox Scholarship, and an NDSEG Research Fellowship. She is also the recipient of the Spiegelman Award, as well as the Leo Breiman Award. She is a Fellow of the American Statistical Association, and an Elected Member of the International Statistical Institute.
James Zou | Stanford University
"AI for clinical trials and clinical trials for AI"
Abstract: Clinical trials are both the gate-keeper and bottleneck of medicine. They can be very costly and challenging to conduct. This talk explores how AI can make trials more efficient and, on the flip side, how to use trials to evaluate medical AI rigorously. I will first discuss Trial Pathfinder, a computational framework that generates synthetic patient cohorts from EHR to optimize cancer trial designs (Liu et al. Nature 2021). Trial Pathfinder enables clinical trials to be more inclusive, benefiting diverse patients and trial sponsors. In the 2nd part, I will discuss insights that we learned from conducting the first trials testing real-time AIs at Stanford and analyzing data from >100 FDA-approved medical AIs (Wu et al. Nature Medicine 2021). These analyses raise new questions about model auditing and human-AI interactions that are critical for responsible deployment of medical AI.
Bio: James Zou is an assistant professor of biomedical data science and, by courtesy, of CS and EE at Stanford University. He is also the faculty director for Stanford AI4Health. James’ group develops novel machine learning models to tackle biomedical and healthcare challenges. Several of their algorithms are deployed at Stanford Medicine, biotech and tech companies. They also work on improving the broader impact of AI by making models more reliable, transparent and fair. James has received a Sloan Fellowship, NSF CAREER Award, Chan-Zuckerberg Investigator award, faculty awards from Google, Tencent and Amazon, and several paper awards at top CS venues including the 2019 RECOMB Best Paper.
Our event also includes talks given by three compelling CMB faculty members -- Georg Seelig (ECE/CSE), Erick Matsen (Fred Hutch), and Armita Nourmohammad (Physics) -- followed by small group discussions to facilitate collaborations. The detailed schedule will be posted shortly.
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