Artificial Intelligence Conference San Francisco 63/114

Artificial Intelligence Conference San Francisco 63/114

Vikash Mansinghka
Research Scientist, MIT

Vikash Mansinghka is a research scientist at MIT, where he leads the Probabilistic Computing Project, and a cofounder of Empirical Systems, a new venture-backed AI startup aimed at improving the credibility and transparency of statistical inference. Previously, Vikash cofounded a venture-backed startup based on his research that was acquired by Salesforce, was an advisor to Google DeepMind, and held graduate fellowships at the National Science Foundation and MIT’s Lincoln Laboratory. He served on DARPA’s Information Science and Technology advisory board from 2010 to 2012 and currently serves on the editorial boards for the Journal of Machine Learning Research and Statistics and Computation. Vikash holds a PhD in computation, an MEng in computer science, and BS degrees in mathematics and computer science, all from MIT. His PhD dissertation on natively probabilistic computation won the MIT George M. Sprowls dissertation award in computer science, and his research on the Picture probabilistic programming language won an award at CVPR.


9:00am–12:30pm Monday, September 18, 2017
Probabilistic programming
Implementing AI
Location: Yosemite A
Level: Advanced

Secondary topics:  Data science and AI

Vikash Mansinghka (MIT)
Probabilistic inference, a widely used, mathematically rigorous approach for interpreting ambiguous information using models that are uncertain or incomplete, is central to big data analytics to robotics and AI. Vikash Mansinghka and Richard Tibbetts survey the emerging field of probabilistic programming, which aims to make modeling and inference broadly accessible to nonexperts.