David L. DeMets Lecture Series - November 2016

David Heckerman

The second annual David L. DeMets Lectures Series presented by the Department of Biostatistics & Medical Informatics will be held on November 3rd and 4th of 2016. Click here for the 2016 announcement flyer

November 2016 Speaker

David Heckerman, MD, PhD

Distinguished Scientist and Director of Genomics Group, Microsoft


David Heckerman is a Distinguished Scientist and Director of Microsoft Genomics at Microsoft.  In his current scientific work, he is developing machine-learning and statistical approaches for biological and medical applications including genomics and HIV vaccine design.  In his early work, he demonstrated the importance of probability theory in Artificial Intelligence, and developed methods to learn graphical models from data, including methods for causal discovery.  At Microsoft, he has developed numerous applications including the junk-mail filters in Outlook, Exchange, and Hotmail, machine-learning tools in SQL Server and Commerce Server, handwriting recognition in the Tablet PC, text mining software in Sharepoint Portal Server, troubleshooters in Windows, and the Answer Wizard in Office.  David received his Ph.D. (1990) and M.D. (1992) from Stanford University, and is an ACM and AAAI Fellow.


Embracing Big Data in Genomics

Thursday, November 3 - 3:30 to 4:45 pm
1306 HSLC

Download the lecture video here
In the last decade, genomics has seen an explosion in the production of data due to the decreasing costs and processing times associated with DNA sequencing. I will discuss how the cloud as well as techniques from mathematics and computer science help take advantage of this big data.


Surprises in the Statistical Analysis of Associations

Friday, November 4 - 12:00 to 1:00 pm
Biotechnology Center Auditorium

Download the lecture video here
In recent years, linear mixed models have emerged as the model of choice for association studies, including GWAS and PheWAS. In this talk, I will discuss some of the theory underlying these models. Along the way, there will be some surprises as to why they work so well in some cases and not so well in other cases.

Publications of Interest

Computational and statistical issues in personalized medicine XRDS: Crossroads, Volume 21 Issue 4, July 2015 (doi: 10.1145/2788502). Christoph Lippert, David Heckerman
FaST linear mixed models for genome-wide association studies Nature Methods, 8: 833-835, Oct 2011 (doi:10.1038/nmeth.1681). C. Lippert, J. Listgarten, Y. Liu, C.M. Kadie, R.I. Davidson, and D. Heckerman.
Further Improvements to Linear Mixed Models for Genome-Wide Association Studies Scientific Reports 4, 6874, Nov 2014 (doi:10.1038/srep06874). C. Widmer, C. Lippert, O. Weissbrod, N. Fusi, C.M. Kadie, R.I. Davidson, J. Listgarten, and D. Heckerman.