Vikash K. Mansinghka
[my initials] at mit dot edu
I try to make computers smarter and develop computational accounts of
human cognition. My current focus is on
natively probabilistic computation: probabilistic programming
languages and stochastic computing machines built from the ground up
to represent uncertain knowledge, make good guesses, manage ambiguity
and learn from their experience, rather than carry out deterministic
algorithms for arithmetic calculations and logical deduction. My
research builds on ideas from probability theory, Bayesian statistics,
computational statistical mechanics, cognitive psychology, programming
languages and digital design. In particular, I have worked on:
Languages for compositionally specifying stochastic generative
processes that can do useful computational work and represent
uncertain beliefs
Models written in these languages for finding patterns and making
actionable predictions, both commercially and cognitively motivated
Algorithms for efficiently solving the inference and optimization
problems that arise in reasoning, learning and acting according to
these models
Machines, both virtual and physical, for executing these
algorithms naturally, robustly and efficiently, based on distributed,
stochastic circuits
I am a member of the the Computational Cognitive Science
Group at MIT's Brain and Cognitive Sciences
Department and Computer Science
and Artificial Intelligence Laboratory, where I received my PhD in
2009 and was advised by Professor Joshua
Tenenbaum. I am also involved in an ongoing effort to produce a
commercially backed software and hardware platform for natively
probabilistic computing.
My other academic interests include the integration of programming
with pedagogy, the procedural formalization of mathematics and
physical law, and the clear (and hopefully inspiring and empowering)
communication of the methods and products of human knowledge to the
general public.
Some of my publications, in reverse chronological order::
- Sanborn, Mansinghka, Griffiths. A Bayesian Framework for
Modeling Intuitive Dynamics. To appear in COGSCI 2009.
- [PDF] Mansinghka,
Roy, Jonas, Tenenbaum. Exact and Approximate Sampling by Systematic
Stochastic Search. In Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS) 2009.
- [PDF]
Mansinghka, Jonas, Tenenbaum. Stochastic Digital Circuits for
Probabilistic Inference. MIT CSAIL Technical Report 2008-069.
- [PDF] Roy, Mansinghka, Goodman, Tenenbaum. A stochastic programming perspective on nonparametric Bayes. In the Nonparametric Bayes Workshop at ICML 2008.
- [PDF]
Goodman, Mansinghka, Roy, Bonawitz, Tenenbaum. Church: a
language for generative models. In Proceedings of the
Twenty-Fourth Conference on Uncertainty in Artificial Intelligence
(UAI) 2008.
- [PDF]
Goodman, Mansinghka, Tenenbaum. Learning grounded causal
models. In Proceedings of the Twenty-Ninth
Annual
Conference of the Cognitive Science Society (COGSCI) 2007.
(The experiment demo is
here.)
[2007 Cognitive Science Society computational modeling prize
for Perception and Action.]
- [PDF]
Frank, Goldwater, Mansinghka, Griffiths, Tenenbaum. Modeling human
performance in statistical word segmentation. In Proceedings of
the Twenty-Ninth Annual Meeting of the Cognitive Science Society
(COGSCI) 2007.
- [PDF]
Mansinghka, Roy, Rifkin, Tenenbaum. AClass: An online algorithm for
generative classification. In Proceedings of the 11th
International Conference on Artificial Intelligence and Statistics
(AISTATS) 2007.
- [PDF]
Roy, Kemp, Mansinghka, Tenenbaum. Learning annotated hierarchies
from relational data. In Neural Information Processing Systems
(NIPS) 19. December, 2006.
- [PDF]
Mansinghka, Kemp, Tenenbaum, Griffiths. Structured priors for
structure learning. In Proceedings of the Twenty-Second Conference
on Uncertainty in Artificial Intelligence (UAI) 2006.
- [PDF]
Shafto, Kemp, Mansinghka, Gordon, Tenenbaum. Learning cross-cutting
systems of categories. In Proceedings of the Twenty-Eighth Annual
Conference of the Cognitive Science Society (COGSCI) 2006.
- [PDF]
Goodman, Bonawitz, Baker, Mansinghka, Gopnik, Wellman, Schulz,
Tenenbaum. Intuitive theories of mind: a rational approach to false
belief. In Proceedings of the Twenty-Eighth Annual Conference of
the Cognitive Science Society (COGSCI) 2006.
Feel free to read my old homepage.