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), Maryam Vatankhah (PhD student)
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.
- 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
- July 2013 "Lessons Learned in Replicating Data-Driven Experiments in Multiple Medical Systems and Patient Populations" was accepted by AMIA 2013
- June 2013 I received an R01 through the NSF/NIH BIGDATA program for Causal Inference in Large-Scale Time Series with Rare and Latent Events