The R2 Project

R2 is a research project within the Programming Languages and Tools group at Microsoft Research India on probabilistic programming. Our goal is to build a user friendly and scalable probabilistic programming system by employing powerful techniques from language design, program analysis and verification.

R2 is now available! [download]. Please send inquiries about R2 to r2pp AT microsoft DOT com
People
Aditya Nori
Aditya Nori

Sriram Rajamani
Sriram Rajamani

Selva Samuel
Selva Samuel

Deepak Vijaykeerthy
Deepak Vijaykeerthy

Collaborators

  • Johannes Borgström (Uppsala University)
  • Andy Gordon (MSR Cambridge)
  • Chung-Kil Hur (SNU Korea)
  • Samin Ishtiaq (MSR Cambridge)
  • Akash Lal (MSR India)
  • Claudio Russo (MSR Cambridge)

Interns

  • Arun Chaganty (Stanford University)
  • Guillaume Claret (ENS, Paris)
  • Christian von Essen (Verimag)
  • Vijay D'Silva (UC Berkeley)
  • Sulekha Kulkarni (Georgia Tech) 

  • Sherjil Ozair (IIT Delhi)

  • Selva Samuel (Anna University)

  • Robert J. Simmons (CMU)
  • Shayak Sen (CMU) 

Publications

  • Aditya V. Nori, Chung-Kil Hur, Sriram K. Rajamani, and Selva Samuel. R2: An Efficient MCMC Sampler for Probabilistic Programs, In AAAI '14: AAAI Conference on Artificial Intelligence, July 2014
  • Chung-Kil Hur, Aditya V. Nori, Sriram K. Rajamani, and Selva Samuel. Slicing Probabilistic Programs, In PLDI '14: Programming Language Design and Implementation, June 2014
  • Andrew D. Gordon, Thomas A. Henzinger, Aditya V. Nori, and Sriram K. Rajamani. Probabilistic Programming. In ICSE '14: International Conference on Software Engineering (FOSE track), May 2014
  • Aditya V. Nori, Chung-Kil Hur, Sriram K. Rajamani, and Selva Samuel. Semantics Sensitive Sampling for Probabilistic Programs. Microsoft Research Technical Report, MSR-TR-2013-109, October 2013
  • Guillaume Claret, Sriram K. Rajamani, Aditya V. Nori, Andrew D. Gordon, and Johannes Borgström. Bayesian Inference Using Data Flow Analysis. In ESEC-FSE '13: Foundations of Software Engineering, August 2013
  • Arun T. Chaganty, Akash Lal, Aditya V. Nori, and Sriram K. Rajamani. Combining Relational Learning with SMT Solvers using CEGAR. In CAV '13: Computer Aided Verification, July 2013
  • Arun T. Chaganty, Aditya V. Nori, and Sriram K. Rajamani. Efficiently Sampling Probabilistic Programs via Program Analysis. In AISTATS '13: Artificial Intelligence and Statistics, April 2013
  • Andrew D. Gordon, Aditya V. Nori, and Sriram K. Rajamani. Probabilistic Inference using Program Analysis. In OBT '13: Off the Beaten Track Workshop, January 2013
  • Andrew D. Gordon, Mihhail Aizatulin, Johannes Borgström, Guillaume Claret, Thore Graepel, Aditya V. Nori, Sriram K. Rajamani, and Claudio Russo. A Model-Learner Pattern for Bayesian Reasoning. In POPL '13: Principles of Programming Languages, January 2013
  • Arun T. Chaganty, Aditya V. Nori, and Sriram K. Rajamani. Efficiently Sampling Probabilistic Programs via Program Analysis. In NIPS workshop on Probabilistic Programming, December 2012
  • Andrew D. Gordon, Mihhail Aizatulin, Johannes Borgström, Guillaume Claret, Thore Graepel, Aditya V. Nori, Sriram K. Rajamani, and Claudio Russo. A Model-Learner Pattern for Bayesian Reasoning. In NIPS workshop on Probabilistic Programming, December 2012