Vikash K. Mansinghka
(office) MIT Room 46-5169
(lab) MIT Room 46-4066
Our minds accomplish far more than our best machine intelligence
systems, automatically building flexible and remarkably useful
models from our inherently ambiguous sensory experience. At the same time,
our brains are built out of far slower, less reliable parts than even
the earliest electronic computers. I try to identify computational
principles that can help us narrow these capability, efficiency and
robustness gaps, build better computational models of cognition, and
engineer systems that help people infer the probable causes behind
My research is based on an emerging marriage of the abstractions behind software and hardware with stochastic processes, random variables, and Bayesian inference. These randomized building blocks support the engineering of computing systems that stochastically explore alternative explanations for data, implicitly obeying the norms of Bayesian reasoning, rather than carry out deterministic, step-by-step calculations. So far, this approach has yielded new probabilistic programming technology and stochastic hardware designs, as well as practical machine learning systems that analyze high-dimensional tables and automatically produce statistically reliable predictions from raw, messy data.
This work draws heavily on and contributes to several other fields, especially computational Bayesian statistics and nonparametrics, probabilistic programming, machine learning, artificial intelligence, and computational cognitive science. It also builds on ideas from functional programming languages, computational statistical mechanics, and digital design.
I am currently a Fellow of MIT's Intelligence Initiative, where I lead the Probabilistic Computing Project (website coming soon), focused on building, analyzing and deploying a range of probabilistic computing systems. 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 Salesforce.com 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.