Rohit Singh
Rohit is an AI researcher with a PhD from MIT Computer Science and Artificial Intelligence Lab (CSAIL). He is currently working on applications of various AI techniques with the Pyro programming language across product teams at Uber. His previous work has involved applications of Machine Learning, Quantitative Game Theory and Program Synthesis in multiple domains from the fields of Compilers and Databases. Rohit has worked as an intern at Google where he used the Google Brain deep-learning framework for an application with the YouTube team and as a PM intern at Yelp where he worked on a Machine Learning application on Ad CTR prediction.
Research Papers
Pyro: Deep Universal Probabilistic Programming
E. Bingham, J. Chen, M. Jankowiak, F. Obermeyer, N. Pradhan, T. Karaletsos, R. Singh, P. Szerlip, P. Horsfall, N. Goodman
Pyro is a probabilistic programming language built on Python as a platform for developing advanced probabilistic models in AI research. [...] [PDF at arXiv]
Journal of Machine Learning Research (JMLR), 2018
Pyro is a probabilistic programming language built on Python as a platform for developing advanced probabilistic models in AI research. [...] [PDF at arXiv]
Journal of Machine Learning Research (JMLR), 2018
Synthesizing Entity Matching Rules by Examples
R. Singh, V. Vamsikrishna Meduri, A. Elmagarmid, S. Madden, P. Papotti, Jo. Quiané-Ruiz, A. Solar-Lezama, N. Tang
Entity matching (EM) is a critical part of data integration. We study how to synthesize entity matching rules from positive-negative matching examples. The core of our solution is program synthesis, a powerful tool to automatically generate rules (or programs) that satisfy a given highlevel specification, via a predefined grammar. [...] [PDF at Very Large Data Base Endowment Inc.]
Proceedings of the VLDB Endowment (PVLDB) 11(2): 189-202, 2017
Entity matching (EM) is a critical part of data integration. We study how to synthesize entity matching rules from positive-negative matching examples. The core of our solution is program synthesis, a powerful tool to automatically generate rules (or programs) that satisfy a given highlevel specification, via a predefined grammar. [...] [PDF at Very Large Data Base Endowment Inc.]
Proceedings of the VLDB Endowment (PVLDB) 11(2): 189-202, 2017
