Workshop in Biostatistics (BIODS/STATS 260)


Open to enrolled students and all members of the Stanford community.

Receiving Credit for Attending a Workshop Seminar

Students who wish to receive two credits must write an essay summarizing one of the seminars and discussing it critically in the context of the background readings.

Contact program manager Katie M. Kanagawa with questions. Suggestions and self-nominations for seminar speakers and topics are welcome.

The Workshop is held from 1:30-2:50pm in Medical School Office Building (MSOB), Rm x303, 1265 Welch Road, Stanford, unless otherwise specified on the calendar below.

Fall 2019 Calendar

DATE INVESTIGATOR PRESENTATION TITLE
9/26 No workshop.   
10/3 Gilmer Valdes (Assistant Professor of Radiation Oncology at UCSF)

Breaking the tradeoff between interpretability and accuracy of machine learning algorithms

Abstract and Suggested Readings (PDF)

10/10 Eric Jorgenson, PhD (Research Scientist at the Division of Research (DOR), Kaiser Permanente Northern California (KPNC))

Genetic variation in the SIM1 locus is associated with erectile dysfunction

Abstract and Suggested Reading (PDF)

10/17 Jingshen Wang (Assistant Professor in Biostatistics at UC Berkeley)


Inference on Treatment Effects after Model Selection with application to subgroup analysis

Abstract and Suggested Readings (PDF)

10/24 Scott Linderman (Assistant Professor of Statistics at Stanford)

Models and Algorithms for Understanding Neural and Behavioral Data

10/31 Erick Matsen (Associate Professor of Genome Sciences and of Statistics at University of Washington) Phylogenetic Variational Bayes
11/7 Sonia Petrone (Professor and PhD Director of Statistics at Bocconi University, Milano) TBD
11/14 Emma Pierson (Ph.D. Candidate in Computer Science at Stanford)

Using machine learning to explain socioeconomic and racial disparities in pain

11/21 Serena Yeung (Assistant Professor of Biomedical Data Science at Stanford)

TBD

12/5 Babak Shababba (Professor of Statistics and Computer Science at UC Irvine) Neural Data Analysis— From Stochastic Process Modeling to Deep Learning