So far, this work has yielded new general-purpose probabilistic programming technology and intentionally stochastic (but still digital) hardware for real-time Bayesian inference. It has also yielded academic and commercial Bayesian database systems that automate the analysis of high-dimensional data tables.

- Venture --- an interactive, Turing-complete probabilistic programming platform descended from Church
- BayesDB --- a Bayesian database that lets users query the probable implications of their data, and solve basic data science problems without training in statistics
- Picture --- an imperative probabilistic programming language for scene perception, building on the GPGP framework
- CrossCat --- a nonparametric Bayesian machine learning method for analyzing high-dimensional data tables

- Taylor Campbell (Research Engineer)
- Baxter Eaves (Research Engineer)
- Vlad Firoiu (PhD Student, EECS; formerly undergraduate)
- Tejas Kulkarni (PhD Student, BCS)
- Vikash Mansinghka (Research Scientist, PI)
- Alexey Radul (Research Engineer)
- David Wadden (Research Assistant)
- Benjamin Zinberg (MEng Student, EECS)

- Dan Lovell (Research Engineer) -> software engineer at Continuum Analytics (2013)
- Daniel Selsam (Research Assistant) -> graduate student at Stanford (2014)
- Jay Baxter (MEng Student, EECS) - data scientist at Twitter
- Yura Perov (Visiting Undergraduate) -> visiting undergraduate at Oxford (2013)
- Jeff Wu (MEng Student, EECS) -> software engineer #1 at Terminal.com (2012)
- Owain Evans (PhD Student, Philosophy) -> postdoctoral fellow at Oxford (2015)

- Querying the probable implications of tabular data with BQL and BayesDB. Mansinghka, V.,
Baxter, J., Eaves, B. [extended abstract presented at 3rd NIPS Workshop on
Probabilistic Programming], 2014.
- Venture: a general purpose probabilistic programming platform with efficient stochastic
inference. Mansinghka, V., Radul, A. [preprint available as arXiv, number 1404:0099]
[pdf]
- Discrete Particle Variational Inference. Saeedi, A., Kulkarni, T., Mansinghka, V.,
Gershman, [extended abstract delivered as oral presentation at 4th NIPS Workshop on
Advances in Variational Inference], 2014.
- CoreVenture: a highlevel, reflective machine language for probabilistic programming. Mansinghka,
V., Radul, A. [extended abstract presented at 3rd NIPS Workshop on Probabilistic
Programming], 2014.
- VentureScript: a language for probabilistic modeling and interactive inference.
Mansinghka, V., Radul, A. [extended abstract presented at 3rd NIPS Workshop on
Probabilistic Programming], 2014.
- Picture: an imperative probabilistic programming language for scene perception. Kulkarni,
T., Kohli, P., Tenenbaum, J., Mansinghka, V. [Under review at CVPR.]
[pdf]
[supplemental]
- Sublinear approximate inference for probabilistic programs, Chen, Y., Mansinghka, V.,
Ghahramani, Z. [Under review at AISTATS; preprint available as arXiv, number 1411.1690, 2014.]
[pdf]
- Particle Gibbs with Ancestor Resampling for Probabilistic Programs. Willem, J., Yeung,
H., Wood, F., and Mansinghka, V. [Accepted to AISTATS 2015; preprint available as arXiv, number 1501.06769]
[pdf]
- JUMP Means: Small Variance Asymptotics for Markov Jump Processes. Huggins, J.,
Narasimhan, K., Saeedi, A., and Mansinghka, V. [Under review at ICML.]
- Building fast Bayesian computing machines out of intentionally stochastic parts. Mansinghka, V., Jonas, E. [preprint available as arXiv, number 1402:4914], 2014. [pdf]

- Composable gradientbased inference for higherorder probabilistic programming
languages. Mansinghka, V., Radul, A. [Presented at 3rd NIPS Workshop on Probabilistic
Programming.], 2014.
- GPGP3D: Generative probabilistic graphics programming for 3D shape perception.
Kulkarni, T., Kohl, P., Tenenbaum, J., Mansinghka, V. [Presented at 3rd NIPS Workshop on
Probabilistic Programming.], 2014.
- plotf: a candidate printf for probabilistic programs. Mansinghka, V., Radul, A.
[Presented at 3rd NIPS Workshop on Probabilistic Programming.], 2014.
- Identifying speed and convergence bottlenecks in probabilistic programs. Firoiu, V.,
Zinberg, B., Mansinghka, V. [3rd NIPS Workshop on Probabilistic Programming.], 2014.
- Writing custom proposals for probabilistic programs as probabilistic programs. Zhang, H.,
Mansinghka, V. [Presented at 3rd NIPS Workshop on Probabilistic Programming.], 2014.
- A new approach to probabilistic programming inference. Wood, F., van den Meent, J.,
Mansinghka, V., AISTATS, 2014. [Oral].
[pdf]
- Automatic inference for inverting software simulators via probabilistic programming. Saeedi, A., Firoiu, V., Mansinghka, V. [ICML Workshop on Automatic Machine Learning.], 2014. [pdf]

- Markov chain algorithms: A template for building future robust low power systems. Deka,
B., Birklykke, A., Duwe, H., Mansinghka, V., Kumar, R. Asilomar Conference on Signals,
Systems, and Computers, 2013.
[pdf]
- Approximate Bayesian image interpretation using generative probabilistic graphics
programs. Mansinghka, V., Kulkarni, T., Perov, Y., Tenenbaum, J. NIPS, 2013. [Oral]
[pdf]
- Parallel Markov chain Monte Carlo for Dirichlet process mixtures. Lovell, D., Malmaud, J.,
Adams, R. P., Mansinghka, V. [Oral presentation at NIPS Workshop on Big Learning.],
2012. Extended version available on arXiv, number 1304.2302, 2013.
[pdf]
- Reconciling intuitive physics and Newtonian mechanics for colliding objects. Sanborn, A., Mansinghka, V., and Griffiths, T.L. [Psychological Review], 2013. [pdf]

- A probabilistic model of crosscategorization. Shafto, P., Kemp, C., Mansinghka, V.,
Tenenbaum, J. B. [Cognition], 2011.
[pdf]
- Beyond calculation: probabilistic computing machines and universal stochastic inference.
Mansinghka, V. [NIPS Workshop on Philosophy and Machine Learning.], 2011. [Oral]
- When are probabilistic programs probably computationally tractable? Freer, C.,
Mansinghka, V., Roy, D. [NIPS Workshop on Monte Carlo Methods for Modern
Applications.], 2010.
[pdf]
- Cross categorization: a method for discovering multiple overlapping clusterings.
Mansinghka, V., Jonas, E., Petschulat, C., Cronin, B., Shafto, P. and Tenenbaum, J.
[NIPS Workshop on Nonparametric Bayes.], 2009.
[pdf]
- A stochastic programming perspective on nonparametric Bayes. Roy, Mansinghka,
Goodman and Tenenbaum. [ICML Nonparametric Bayes Workshop.], 2008. [Oral]
[pdf]
- CrossCat: a fully Bayesian, nonparametric method for analyzing heterogeneous, high-
dimensional data. Mansinghka, V., Shafto, P. Jonas, E., Petschulat, C., Gasner, M. and
Tenenbaum, J. [Journal of Machine Learning Research, accepted pending revision.]
- A Bayesian framework for modeling intuitive dynamics. Sanborn, A., Mansinghka, V.,
Griffiths, T. L. COGSCI, 2009.
[pdf]
- Exact and approximate sampling by systematic stochastic search. Mansinghka, V., Roy,
D., Jonas, E., Tenenbaum, J. B. AISTATS, 2009. [Oral].
[pdf]
- Natively Probabilistic Computation. Mansinghka, V. MIT Doctoral dissertation. 2009.
Winner, George M. Sprowls dissertation award.
[pdf]
- Nonparametric Bayesian Models for Supervised and Unsupervised Learning.
Mansinghka, V. MIT Masters thesis. 2009.
[link]
- Stochastic Digital Circuits for Probabilistic Inference. Mansinghka, V., Jonas, E.,
Tenenbaum, J. B. MIT CSAIL Technical Report, 2008.
[pdf]
- Church: a universal language for generative models. Goodman, N. and Mansinghka, V.,
Roy, D., Bonawitz, K., Tenenbaum, J. B. UAI, 2008. [Oral, available as arXiv, number 1206.3255].
[pdf]
- Learning grounded causal models. Goodman, N., Mansinghka, V., Tenenbaum, J. B.
COGSCI, 2007. [Oral; winner of Cognitive Science Society Computational Modeling Prize
for Perception and Action] [pdf]
- Modeling human performance in statistical word segmentation. Frank, M. C., Goldwater,
S., Mansinghka, V., Griffiths, T., Tenenbaum, J. B. COGSCI, 2007. [pdf]
- AClass: an online algorithm for generative classification. Mansinghka, V. Roy, D. Rifkin,
R. Tenenbaum, J. B. AISTATS, 2007.
[pdf]
- Learning annotated hierarchies from relational data. Roy, D., Kemp, C., Mansinghka, V.
K., and Tenenbaum, J. B. NIPS, 2006. [Oral]
[pdf]
- Structured priors for structure learning. Mansinghka, V. K., Kemp, C., Tenenbaum, J. B.,
and Griffiths, T. L. UAI, 2006. [Oral]
[pdf]
- Learning crosscutting systems of categories. Shafto, P. Kemp, C., Mansinghka, V.,
Gordon, M., and Tenenbaum, J. B. COGSCI, 2006. [Poster].
- Intuitive theories of mind: A rational approach to false belief. Goodman, N., Baker, C., BonawitzBaraff, E., Mansinghka, V., Gopnik, A., Wellman, H., Shulz, L., Tenenbaum, J. B. COGSCI, 2006. [pdf]