Dan Roy aka daniel roy, daniel m roy, droy

news

OCT 2011
My PhD dissertation has been selected as a winner of a MIT/EECS Sprowls Award, recognizing it as one of the best dissertations in Computer Science at MIT in 2011.

MAR 2011
I have started my Newton Fellowship at Cambridge.

JAN 2011
I am delighted to report that I have been elected as a Research Fellow at Emmanuel College, University of Cambridge.

SEP 2010
I have accepted a Newton International Fellowship at the University of Cambridge. I will be joining the Machine Learning Group, headed by Zoubin Ghahramani.

research

My research interests lie at the intersection of computer science, statistics and probability theory; I study probabilistic programming languages to develop computational perspectives on fundamental ideas in probability theory and statistics. I am particularly interested in the use of recursion to define nonparametric distributions on data structures; representation theorems that connect computability and probabilistic structures; and the complexity of inference.

academic

Hello, my name is Daniel M. Roy (or simply, Dan) and I am a Newton Fellow at the University of Cambridge. I am a member of the Machine Learning Group, headed by Zoubin Ghahramani, and part of the Computational and Biological Learning Lab in the Department of Engineering.

I am a recent graduate of the EECS PhD program in computer science at the Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory (CSAIL), where I was advised by Leslie Kaelbling. I also collaborated with members of Josh Tenenbaum's Computational Cognitive Science.

You may find my curriculum vitæ online.

personal

I enjoy many outdoor activities and sports such as skiing, hiking, barefoot running, cycling, volleyball, basketball and rowing.

Source: Jesus College Virtual Tour

coauthors

Nate Ackerman, William Beebee, Keith Bonawitz, Cristian Cadar, Hal Daumé III, Brian Demsky, Daniel Dumitran, Cameron Freer, Noah Goodman, Eric Jonas, Leslie Kaelbling, Charles Kemp, Tudor Leu, Vikash Mansinghka, Ryan Rifkin, Martin Rinard, Virginia Savova, Lauren Schmidt, David Sontag, Yee Whye Teh, Josh Tenenbaum, David Wingate

colleagues

Chris Baker, Stephen Cauley, Thomas Kollar, Timothy O'Donnell, Peter Orbanz, Juliet Wagner, Matt Walter

NIPS 2008 Workshop on Probabilistic Programming

I co-organized a workshop on probabilistic programming for statistics and machine learning at NIPS*2008 (with Vikash Mansinghka, John Winn, David McAllester and Josh Tenenbaum).

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preprints (last updated: 05/10)

On the computability of conditional probability
(with Nate Ackerman and Cameron Freer)
[ arXiv:1005.3014, PDF ]

research articlesand publications

Computable de Finetti measures
(with Cameron Freer)
Annals of Pure and Applied Logic, 2012.
[ arXiv:0912.1072, PDF, doi:10.1016/j.apal.2011.06.011 ]

Complexity of Inference in Latent Dirichlet Allocation
David Sontag and Daniel Roy
Adv. Neural Information Processing Systems 24 (NIPS), 2011.
[ PDF ]

On the computability and complexity of Bayesian reasoning
NIPS Philosophy and Machine Learning Workshop, 2011.
[ PDF ]

Noncomputable conditional distributions
(with Nate Ackerman and Cameron Freer)
Proc. Logic in Computer Science (LICS), 2011.
[ Preprint (with proofs) ]

Probabilistic Analysis of Perforated Patterns
Sasa Misailovic, Daniel M. Roy, and Martin C. Rinard
Proc. Int. Static Analysis Symp. (SAS), 2011.

Computability, inference and modeling in probabilistic programming
Daniel M. Roy
Ph.D. thesis, Massachusetts Institute of Technology, 2011.
MIT/EECS George M. Sprowls Doctoral Dissertation Award
[ PDF ]

Bayesian Policy Search with Policy Priors
David Wingate, Noah D. Goodman, Daniel M. Roy, Leslie P. Kaelbling, and Joshua B. Tenenbaum
Proc. Int. Joint Conf. on Artificial Intelligience (IJCAI), 2011.

When are probabilistic programs probably computationally tractable?
(with Cameron Freer and Vikash Mansinghka)
NIPS Workshop on Monte Carlo Methods for Modern Applications, 2010.
[ PDF bibtex ]

Posterior distributions are computable from predictive distributions
(with Cameron Freer)
Proc. Artificial Intelligence and Statistics (AISTATS), 2010.
[ PDF bibtex ]

Complexity of Inference in Topic Models
David Sontag and Daniel Roy
NIPS Workshop on Applications for Topic Models: Text and Beyond, 2009.
[ PDF bibtex ]

The Infinite Latent Events Model
David Wingate, Noah D. Goodman, Daniel M. Roy, and Joshua B. Tenenbaum
Proc. Uncertainty in Artificial Intelligence (UAI), 2009.
[ PDF bibtex ]

Computable exchangeable sequences have computable de Finetti measures
(with Cameron Freer)
Proc. Computability in Europe (CiE), 2009.
[ PDF bibtex ]

Exact and Approximate Sampling by Systematic Stochastic Search
Vikash Mansinghka, Daniel M. Roy, Eric Jonas, and Joshua Tenenbaum
Proc. Artificial Intelligence and Statistics (AISTATS), 2009.
[ PDF bibtex ]

The Mondrian Process
(with Yee Whye Teh)
Adv. Neural Information Processing Systems 21 (NIPS), 2009.
[ PDF bibtex ]

Video animation of the Mondrian process as one zooms into the origin (under a beta Levy rate measure at time t=1.0). See also the time evolution of a Mondrian process on the plane as we zoom in with rate proportional to time. In both cases, the colors are chosen at random from a palette. These animations were produced by Yee Whye in Matlab. For now, we reserve copyright, but please email me and we'll be more than likely happy to let you use them.

A stochastic programming perspective on nonparametric Bayes
Daniel M. Roy, Vikash Mansinghka, Noah Goodman, and Joshua Tenenbaum
ICML Workshop on Nonparametric Bayesian, 2008.
[ PDF ]

Church: a language for generative models
Noah Goodman, Vikash Mansinghka, Daniel M. Roy, Keith Bonawitz, and Joshua Tenenbaum
Proc. Uncertainty in Artificial Intelligence (UAI), 2008.
[ PDF bibtex ]

Bayesian Agglomerative Clustering with Coalescents
Yee Whye Teh, Hal Daumé III, and Daniel M. Roy
Adv. Neural Information Processing Systems 20 (NIPS), 2008.
[ arXiv:0907.0781, PDF bibtex ]

Discovering Syntactic Hierarchies
Virginia Savova, Daniel M. Roy, Lauren Schmidt, and Joshua B. Tenenbaum
Proc. Cognitive Science (COGSCI), 2007.
[ PDF bibtex ]

AClass: An online algorithm for generative classification
Vikash K. Mansinghka, Daniel M. Roy, Ryan Rifkin, and Joshua B. Tenenbaum
Proc. Artificial Intelligence and Statistics (AISTATS), 2007.
[ PDF bibtex ]

Efficient Bayesian Task-level Transfer Learning
Daniel M. Roy and Leslie P. Kaelbling
Proc. Int. Joint Conf. on Artificial Intelligience (IJCAI), 2007.
[ PDF bibtex ]

Learning Annotated Hierarchies from Relational Data
Daniel M. Roy, Charles Kemp, Vikash Mansinghka, and Joshua B. Tenenbaum
Adv. Neural Information Processing Systems 19 (NIPS), 2007.
[ PDF bibtex ]

Clustered Naive Bayes
MEng thesis, Massachusetts Institute of Technology, 2006.
[ PDF bibtex ]

Enhancing Server Availability and Security Through Failure-Oblivious Computing
Martin Rinard, Cristian Cadar, Daniel Dumitran, Daniel M. Roy, Tudor Leu, and William S. Beebee, Jr.
Proc. Operating Systems Design and Implementation (OSDI), 2004.
[ PDF bibtex ]

A Dynamic Technique for Eliminating Buffer Overflow Vulnerabilities (and Other Memory Errors)
Martin Rinard, Cristian Cadar, Daniel Dumitran, Daniel M. Roy, and Tudor Leu
Proc. Annual Computer Security Applications Conference (ACSAC), 2004.
[ PDF bibtex ]

Efficient Specification-Assisted Error Localization
Brian Demsky, Cristian Cadar, Daniel M. Roy, and Martin C. Rinard
Proc. Workshop on Dynamic Analysis (WODA), 2004.
[ PDF bibtex ]

Efficient Specification-Assisted Error Localization and Correction
Brian Demsky, Cristian Cadar, Daniel M. Roy, and Martin C. Rinard
MIT CSAIL Technical Report 927. November, 2003.
[ PDF bibtex ]

Implementation of Constraint Systems for Useless Variable Elimination
(advised by Mitchell Wand)
Research Science Institute. August, 1998.
[ PDF bibtex ]

Note: Some articles, distinguished by "(with ...)", have alphabetical author lists, as is the convention in mathematics and theoretical computer science.