Put AI to work
June 26-27, 2017: Training
June 27-29, 2017: Tutorials & Conference
New York, NY
Vikash Mansinghka

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.


9:00am12:30pm Tuesday, June 27, 2017
Implementing AI
Location: Sutton North Level: Advanced
Secondary topics:  Machine Learning
Average rating: *****
(5.00, 4 ratings)
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 surveys the emerging field of probabilistic programming, which aims to make modeling and inference broadly accessible to nonexperts. Read more.
1:30pm5:00pm Tuesday, June 27, 2017
Impact of AI on business and society
Location: Sutton South/Regent Parlor Level: Beginner
Secondary topics:  Machine Learning
Vikash Mansinghka (MIT), Richard Tibbetts (Empirical Systems)
Average rating: ****.
(4.50, 2 ratings)
Businesses have spent decades trying to make better decisions by analyzing structured data. New AI technologies are just beginning to transform this process. Vikash Mansinghka and Richard Tibbetts explore AI that guides business analysts to ask statistically sensible questions and lets junior data scientists answer in minutes questions that previously took hours for trained statisticians. Read more.