After completing my PhD in Computer Science in 2010 at NYU, I spent two years as a postdoctoral Computing Innovation Fellow at Columbia University, in the Department of Biomedical Informatics. Before that I was an undergraduate at NYU in Computer Science and Physics.
My book, Causality, Probability, and Time, is now available in print and electronically.
My broad interests are causality, inference from complex data, and biomedical informatics. At the core of my work is a fascination with time and temporal data. My current work combines these areas, uniting temporal logic and tools from computer science with philosophical theories of causality to solve biomedical problems. I've previously applied these methods to stock return time series as well as to political speeches and popularity ratings, but my most recent work aims involves applications to electronic health record and intensive care data.
Yuxiao Huang (Postdoc), Shah Atiqur Rahman (Postdoc), Christopher Merck (PhD student), Mark Mirtchouk (Undergraduate researcher), Jason Gardella (Undergraduate researcher)
NSF CAREER: Learning from Observational Data with Knowledge
NIH R01 BIGDATA: Causal Inference in Large-Scale Time Series with Rare and Latent Events
531 Hudson Street, room 306
Hoboken, NJ 07030
(email is my preferred contact method)
Current OpeningsI am looking for creative and motivated PhD students and undergrads.
- May 2014 I received an NSF CAREER Award for research on Learning from Observational Data with Knowledge
- January 2014 My second book (a guide to thinking about and using causes for a popular audience) is now under contract with O'Reilly
- November 2013 We'll present "Causal Inference with Uncertainty Identifies Features of Physical Activity as Significant Predictors of Hyperglycemia in Type 1 Diabetes" at ATTD 2014
- October 2013 I'll be giving talks at the Machine Learning for Clinical Data Analysis workshop at NIPS 2013 in December and at Johns Hopkins in November