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.
Health and AI Lab
We are primarily motivated by trying to improve human health, through the development of artificial intelligence methods. Most of these problems come back to the question of why things happen or how they change, so we focus on causal inference and time series data. We look at both clinical data as well as data generated outside of hospitals and aim to support both medical providers and patients in their decision making.
Yuxiao Huang (Postdoc), Shah Atiqur Rahman (Postdoc), Christopher Merck (PhD student), Min Zheng (PhD student), Mark Mirtchouk (Undergraduate researcher)
Our mascot is the Honey Badger, for obvious reasons.
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.
- April 2015 New paper: Unintrusive Eating Recognition using Google Glass (at Pervasive Health)
The data are available [here]
- February 2015 New paper: Fast and Accurate Causal Inference from Time Series Data with Yuxiao Huang (at FLAIRS)
We'll present a poster on eating recognition using Google Glass at ATTD 2015
- September 2014 I'll be giving a talk on replication and simulation at PSA
- 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