Principal Research Scientist, Department of Brain and Cognitive Sciences
Vikash Mansinghka is a principal research scientist at MIT, where he leads the Probabilistic Computing Project. His group is building a new generation of probabilistic computing systems that integrate probability and randomness into the basic building blocks of software and hardware. They have discovered that this approach leads to surprising new AI capabilities, and are exploring them via a combination of academic research and entrepreneurship. They also carry out basic research on the mathematical foundations of probabilistic computation. They make our work as freely available as possible via open-source software, workshops, and online educational materials. Additionally, they collaborate with industry and non-profit partners on applications in the public interest. Mansinghka earned an SB in mathematics and computer science, MEng in computer science, and a PhD in computation, from MIT. Mansinghka co-founded two startups: Prior Knowledge and Empirical Systems, later acquired by Salesforce and Tableau, respectively.
- Gothoskar, N., Zhi-Xuan, T., Pollok, F., Gutfreund, D., Tenenbaum, J., Mansinghka, V. (2022). Solving the Baby Intuitions Benchmark with a Hierarchically Bayesian Theory of Mind. Robotics: Science and Systems (RSS) Workshop on Social Intelligence in Humans and Robots.
- Pollok, F., Garrett, A., Cusumano-Towner, M., Ghavamizadeh, M., Zinberg, B., Gothoskar, N., Gutfreund, D., Mansinghka, V., Tenenbaum, J.(2021). 3DP3: 3D Scene Perception via Probabilistic Programming.
- Zhi-Xuan, T., Mann, J. Silver, T., Tenenbaum, J., Mansinghka, V. (2020). Online Bayesian Goal Inference for Boundedly-Rational Planning Agents.
- Proceedings of the ACM on Programming Languages, 4(POPL): 36:1–36:31.
- Lew, A. K., Cusumano-Towner, M. F., Sherman, B., Carbin, M., Mansinghka, V. K. (2020). Trace types and denotational semantics for sound programmable inference in probabilistic languages. Proceedings of the ACM on Programming Languages, 4(POPL): 19:1–19:32.
- Proceedings of the ACM on Programming Languages, 3(POPL): 37:1–37:32.
- Jun. 27, 2019: VentureBeat, MIT’s Gen programming system flattens the learning curve for AI projects.
- Jun. 26, 2019: MIT News, New AI programming language goes beyond deep learning.
- Jan. 15, 2019: MIT News, Democratizing data science.