Workshop in Biostatistics (BIODS/STATS 260)

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


Medical School Office Building (MSOB)
Rm x303, 1265 Welch Road, Stanford

1:30 pm - 2:50 pm

September 24 -- December 7, 2018


Zhangsheng Yu
Professor of Biostatistics
Shanghai Jiao Tong University and Yale University

A Panel Count Model with Time-Varying Coefficients and a Discussion of Some Collaborative Research in China

(Abstract and Bio)


Mingxiu Hu
Adjunct Professor of Biostatistics, Yale University
Senior Vice President, Data Science and Systems, Nektar Therapeutics

Advance Precision Medicine via Gene Signature Development and Innovative Clinical Trial Designs

(Abstract and Bio)

10/11 Gene Katsevich
Ph.D. Candidate
Department of Statistics, Stanford University

Controlling FDR while highlighting distinct discoveries, with applications to GO enrichment analysis



Deena R. Schmidt
Assistant Professor
Mathematics and Statistics, University of Nevada, Reno

Complexity reduction for stochastic network models in biology



Neil Risch
Professor of Epidemiology & Biostatistics, UC San Francisco
Director, Institute for Human Genetics

Genetic Epidemiology Research Based in Electronic Health Records



Ewan Birney
Associate Faculty, Wellcome Trust Sanger Institute
Associate Director, EMBL-EBI

Note: this seminar will be held at special location, LKSC 120

Big data in biology - the good, the bad, the ugly


11/8 David Knowles
Postdoctoral Researcher
Departments of Genetics and Radiology, Stanford University

Statistical estimation of age-at-death from ancient DNA samples via DNA methylation

(Abstract and Suggested Readings)

11/15 Timothy Thornton
Associate Professor of Biostatistics
University of Washington

Challenges and New Approaches for Whole Genome Association Analysis in Multi-Ethnic Populations

(Abstract and Suggested Readings)

11/29 Katherine Heller
Assistant Professor
Department of Statistical Science and Center for Cognitive Science, Duke University

Machine Learning for Health Care


12/6 Pragya Sur
Ph.D. Candidate
Department of Statistics, Stanford University

A modern maximum-likelihood approach for high-dimensional logistic regression


Contact Katie Kanagawa to be on the e-mail list. Suggestions and self-nominations for seminar speakers and topics are welcome.