Vikash Mansinghka | Principal Investigator
Massachusetts Institute of Technology

Vikash Mansinghka, Principal Investigator, Massachusetts Institute of Technology

Vikash Mansinghka is a research scientist at MIT, where he leads the Probabilistic Computing Project. Vikash holds S.B. degrees in Mathematics and in Computer Science from MIT, as well as an M.Eng. in Computer Science and a PhD in Computation. He also held graduate fellowships from the National Science Foundation and MIT's Lincoln Laboratory. 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. He co-founded a venture-backed startup based on this research that was acquired by Salesforce.com, was an advisor to Google DeepMind, and is a co-founder of Empirical Systems, a new venture-backed AI startup aimed at improving the credibility and transparency of statistical inference. He served on DARPA's Information Science and Technology advisory board from 2010-2012, and currently serves on the editorial boards for the Journal of Machine Learning Research and the journal Statistics and Computation.

Appearances:



BioData West: Day 2 @ 15:40

ROUND TABLES

Round Table 1
Augmenting Human Intelligence through AI
Prasun Mishra, Founder and CEO Agility Pharmaceuticals, CEO, American Association for Precision Medicine  

Round Table 2
Tensor Flow - Developing a greater understanding of the chip and its possibilities

Round Table 3 
The Power of Epigenomics- From Age Prediction to Precision Medicine
Jeffrey Bhasin, Director of Informatics, Zymo

Round Table 4 
Bayes DB- Understanding systems for implementing AI for the layman
Vikash Mansinghka, Principal Investigator, MIT Probabilistic Computing Project, MIT
Veronica Weiner, CEO and Co-Founder, Resiliance Therapeutics, Director of Special Projects, MIT Probabilistic Computing Project

Round Table 5
AI & Pharmacovigilance - A short introduction

Round Table 6
Sequencing Populations- How to make it effective and fast

Round Table 7 
AI in Drug Discovery- Partnering with new AI and tech companies

Round Table 8
Policy and Governance- Update on current legal implications 

Round Table 9- 
Aging and Drug Development- How investment in aging is becoming an essential topic

Round Table 10- 
Policy and Governance

Round Table 11- 
Cancer Diagnostics- Liquid biopsies 

Round Table 12- 
Multimodel Integration- New AI methods
 

Bayes DB- Understanding systems for implementing AI for the layman

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