Big Data and Precision Medicine Event

Thursday, November 20, 2014 - 4:00pm to 6:30pm
8th floor of Herbert Irving Cancer Center (inside NewYork-Presbyterian)
1130 St Nicholas Ave
New York, NY 10032
United States

As part of CU's Personalized Medicine Initiative

Big Data and Precision Medicine

November 20, 4pm - 6:30pm

8th floor of Irving Cancer Center

Faculty facilitators: David Madigan, George Hripcsak and Noemie Elhadad

 

Speaker(s): 

David Madigan

Columbia University
Arts and Sciences
EVP and Dean of the Faculty
Statistics
Professor
david.madigan@columbia.edu

David Madigan received a bachelor’s degree in Mathematical Sciences and a Ph.D. in Statistics, both from Trinity College Dublin. He has previously worked for AT&T Inc., Soliloquy Inc., the University of Washington, Rutgers University, and SkillSoft, Inc. He has over 100 publications in such areas as Bayesian statistics, text mining, Monte Carlo methods, pharmacovigilance and probabilistic graphical models. He is an elected Fellow of the American Statistical Association and of the Institute of Mathematical Statistics.

George Hripcsak

George Hripcsak
Columbia University
Biomedical Informatics
Vivian Beaumont Allen Professor and Chair
gh13@columbia.edu

George Hripcsak is Vivian Beaumont Allen Professor, chair of Columbia’s Department of Biomedical Informatics, and director of medical informatics services for New York-Presbyterian Hospital. Hripcsak is a board certified internist with degrees in chemistry, medicine, and biostatistics. He led the effort to create the Arden Syntax, a language for representing health knowledge that has become a national standard. His current research is on the clinical information stored in electronic health records.

Noémie Elhadad

Columbia University
Biomedical Informatics
Associate Professor
noemie.elhadad@columbia.edu

My research is in biomedical informatics, natural language processing, and data mining. I develop techniques that aim to support clinicians, patients, and health researchers in their information workflow by automatically extracting and making accessible information from unstructured, large clinical datasets (e.g., the electronic patient record) and patient platforms (e.g., online health communities).

Back to Top