Vikash K. Mansinghka
(office) MIT Room 46-4094A
(lab) MIT Rooms 46-4066 and 46-5089
I build probabilistic computing systems that exploit uncertain knowledge to learn from data, infer its probable causes, make calibrated predictions and choose effective actions. I also study the computational principles and building blocks needed to design, implement and analyze these systems, drawing on and contributing to an emerging integration of key ideas from probability theory and computer science. This research includes work on machine learning and artificial intelligence fundamentals, as well as applications to modeling human cognition and to intelligent data analysis.

So far, this work has yielded new general-purpose probabilistic programming technology and intentionally stochastic (but still digital) hardware for real-time Bayesian inference. It has also yielded academic and commercial Bayesian database systems that automate the analysis of high-dimensional data tables.

I am currently a Research Scientist with MIT's Intelligence Initiative, where I lead the Probabilistic Computing Project. We are supported by MIT's Computer Science and Artificial Intelligence Laboratory (especially Google's "Rethinking AI" project), the Department of Brain & Cognitive Sciences, and Harvard's School of Engineering and Applied Sciences (where I am a Visiting Fellow). I am also involved in consulting and advisory relationships with industry, and co-founded a startup that was ultimately acquired by in 2012. I have served on DARPA's Information Science and Technology (ISAT) advisory board, and currently serve on the Editorial Board for the Journal of Machine Learning Research. I received my PhD in 2009 from MIT, advised by Professor Joshua Tenenbaum.

Some of my publications, in reverse chronological order (last updated early 2011):