World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
40
Citations
8165
World Ranking
9190
National Ranking
3912

Overview

Rishabh Singh is affiliated with Google in the United States, focusing primarily on research in computer science. Their scholarly work spans multiple subfields including artificial intelligence, information systems, software, computational theory and mathematics, as well as electrical and electronic engineering.

The main topics of Rishabh Singh's research include software testing and debugging techniques, software engineering research, adversarial robustness in machine learning, parallel computing and optimization techniques, explainable artificial intelligence (XAI), topic modeling, and machine learning and data classification.

Frequent coauthors collaborating with Singh include Prathamesh Churi, Charles Sutton, Vidhi Kapoor, Rishabh Reddy, and José C. Prı́ncipe. These collaborations reflect ongoing research partnerships across various subjects within computer science.

Singh has published extensively across a range of academic venues. Common publication platforms are arXiv (Cornell University), SSRN Electronic Journal, ACM Transactions on Programming Languages and Systems, Computer Science and Information Technologies, and the International Journal for Research in Applied Science and Engineering Technology.

Selected recent papers by Rishabh Singh include:

  • "Neurosymbolic Programming," 2021, Foundations and Trends® in Programming Languages
  • "Privacy Issues in Wearable Technology: An Intrinsic Review," 2020, SSRN Electronic Journal
  • "Joy of Learning Through Internet Memes," 2020, International Journal of Engineering Pedagogy (iJEP)
  • "Music Genre Classification," 2021, 2021 5th International Conference on Information Systems and Computer Networks (ISCON)
  • "Question paper generation through progressive model and difficulty calculation on the Promexa Mobile Application," 2021, Education and Information Technologies

Best Publications

  • Syntax-guided synthesis

    Rajeev Alur;Rastislav Bodik;Garvit Juniwal;Milo M. K. Martin

  • Syntax-Guided Synthesis.

    Rajeev Alur;Rastislav Bodík;Eric Dallal;Dana Fisman

  • Automated feedback generation for introductory programming assignments

    Rishabh Singh;Sumit Gulwani;Armando Solar-Lezama

  • Learn&Fuzz: machine learning for input fuzzing

    Patrice Godefroid;Hila Peleg;Rishabh Singh

  • Spreadsheet data manipulation using examples

    Sumit Gulwani;William R. Harris;Rishabh Singh

  • Program Synthesis

    Oleksandr Polozov;Sumit Gulwani;Rishabh Singh

  • Syntax-guided synthesis

    Rajeev Alur;Rastislav Bodik;Garvit Juniwal;Milo M. K. Martin

  • RobustFill: neural program learning under noisy I/O

    Jacob Devlin;Jonathan Uesato;Surya Bhupatiraju;Rishabh Singh

  • Neuro-Symbolic Program Synthesis

    Emilio Parisotto;Abdel-rahman Mohamed;Rishabh Singh;Lihong Li

  • OverCode: Visualizing Variation in Student Solutions to Programming Problems at Scale

    Elena L. Glassman;Jeremy Scott;Rishabh Singh;Philip J. Guo

  • Programmatically Interpretable Reinforcement Learning

    Abhinav Verma;Vijayaraghavan Murali;Rishabh Singh;Pushmeet Kohli

  • Synthesizing data structure manipulations from storyboards

    Rishabh Singh;Armando Solar-Lezama

  • TerpreT: A Probabilistic Programming Language for Program Induction

    Alexander L. Gaunt;Marc Brockschmidt;Rishabh Singh;Nate Kushman

  • Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis.

    Rudy Bunel;Matthew J. Hausknecht;Jacob Devlin;Rishabh Singh

  • Synthesizing number transformations from input-output examples

    Rishabh Singh;Sumit Gulwani

  • Qlose: Program Repair with Quantitative Objectives

    Loris D’Antoni;Roopsha Samanta;Rishabh Singh

  • BlinkFill: semi-supervised programming by example for syntactic string transformations

    Rishabh Singh

  • Learning semantic string transformations from examples

    Rishabh Singh;Sumit Gulwani

  • Natural Language to Structured Query Generation via Meta-Learning

    Po-Sen Huang;Chenglong Wang;Rishabh Singh;Wen-tau Yih

  • Automated Correction for Syntax Errors in Programming Assignments using Recurrent Neural Networks

    Sahil Bhatia;Rishabh Singh

  • Not all bytes are equal: Neural byte sieve for fuzzing

    Mohit Rajpal;William Blum;Rishabh Singh

  • Dynamic Neural Program Embedding for Program Repair

    Ke Wang;Rishabh Singh;Zhendong Su

  • Summary - TerpreT: A Probabilistic Programming Language for Program Induction

    Alexander L. Gaunt;Marc Brockschmidt;Rishabh Singh;Nate Kushman

Frequent Co-Authors

Pushmeet Kohli
Pushmeet Kohli DeepMind (United Kingdom)
Sumit Gulwani
Sumit Gulwani Microsoft (United States)
Charles Sutton
Charles Sutton Google (United States)
Rajeev Alur
Rajeev Alur University of Pennsylvania
Abdel-rahman Mohamed
Abdel-rahman Mohamed Facebook (United States)
Marc Brockschmidt
Marc Brockschmidt Google (United States)
Isil Dillig
Isil Dillig The University of Texas at Austin
Petros Maniatis
Petros Maniatis Google (United States)
Patrice Godefroid
Patrice Godefroid Microsoft (United States)

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