World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
78
Citations
36772
World Ranking
1169
National Ranking
71

Research.com Recognitions

  • 2012 - Fellow of Alfred P. Sloan Foundation

Overview

Satinder Singh is affiliated with DeepMind in the United Kingdom. Their research spans the intersections of computer science and medicine, with a focus on artificial intelligence and related subfields. The main areas of study include artificial intelligence, computer vision and pattern recognition, genetics, oncology, and surgery.

The scientist's work covers several topics, including reinforcement learning in robotics, domain adaptation and few-shot learning, adversarial robustness in machine learning, COVID-19 and healthcare impacts, nasal surgery and airway studies, evolutionary algorithms and applications, and cleft lip and palate research.

Among their recent papers are:

  • Reward is enough, 2021, published in Artificial Intelligence
  • Discovering Reinforcement Learning Algorithms, 2020, published on arXiv (Cornell University)
  • Meta-Gradient Reinforcement Learning with an Objective Discovered Online, 2020, published on arXiv (Cornell University)
  • A Self-Tuning Actor-Critic Algorithm, 2020, published on arXiv (Cornell University)
  • Comparative Assessment of Autogenous Cancellous Bone Graft and Bovine-Derived Demineralized Bone Matrix for Secondary Alveolar Bone Grafting in Patients With Unilateral Cleft Lip and Palate, 2021, published in The Cleft Palate-Craniofacial Journal

Frequent coauthors include:

  • Tom Zahavy
  • David Silver
  • Hado van Hasselt
  • Sebastian Flennerhag
  • Vidya Rattan

The scientist commonly publishes in venues such as arXiv (Cornell University), Indian Journal of Otolaryngology and Head & Neck Surgery, Journal of Maxillofacial and Oral Surgery, Journal of Oral Biology and Craniofacial Research, and Scholarly Journal of Otolaryngology.

Satinder Singh received recognition as a Fellow of the Alfred P. Sloan Foundation in 2012.

Best Publications

  • Policy Gradient Methods for Reinforcement Learning with Function Approximation

    Richard S Sutton;David A. McAllester;Satinder P. Singh;Yishay Mansour

  • Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning

    Richard S. Sutton;Doina Precup;Satinder Singh

  • Learning to act using real-time dynamic programming

    Andrew G. Barto;Steven J. Bradtke;Satinder P. Singh

  • Near-Optimal Reinforcement Learning in Polynomial Time

    Michael Kearns;Satinder Singh

  • Convergence of Stochastic Iterative Dynamic Programming Algorithms

    Tommi Jaakkola;Michael I. Jordan;Satinder P. Singh

  • Reinforcement learning with replacing eligibility traces

    Satinder P. Singh;Richard S. Sutton

  • Convergence Results for Single-Step On-PolicyReinforcement-Learning Algorithms

    Satinder Singh;Tommi Jaakkola;Michael L. Littman;Csaba Szepesvári

  • Intrinsically Motivated Reinforcement Learning

    Nuttapong Chentanez;Andrew G. Barto;Satinder P. Singh

  • Action-conditional video prediction using deep networks in Atari games

    Junhyuk Oh;Xiaoxiao Guo;Honglak Lee;Richard Lewis

  • Eligibility Traces for Off-Policy Policy Evaluation

    Doina Precup;Richard S. Sutton;Satinder P. Singh

  • Graphical models for game theory

    Michael J. Kearns;Michael L. Littman;Satinder P. Singh

  • Learning Without State-Estimation in Partially Observable Markovian Decision Processes

    Satinder P. Singh;Tommi S. Jaakkola;Michael I. Jordan

  • Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems

    Tommi Jaakkola;Satinder P. Singh;Michael I. Jordan

  • Intrinsically Motivated Learning of Hierarchical Collections of Skills

    Andrew G. Barto;Satinder Singh;Nuttapong Chentanez

  • Intrinsically Motivated Reinforcement Learning: An Evolutionary Perspective

    Satinder Singh;Richard L Lewis;Andrew G Barto;Jonathan Sorg

  • Transfer of Learning by Composing Solutions of Elemental Sequential Tasks

    Satinder Pal Singh

  • Optimizing dialogue management with reinforcement learning: experiments with the NJFun system

    Satinder Singh;Diane Litman;Michael Kearns;Marilyn Walker

  • Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning

    Xiaoxiao Guo;Satinder Singh;Honglak Lee;Richard L Lewis

  • Reinforcement Learning with Soft State Aggregation

    Satinder P. Singh;Tommi Jaakkola;Michael I. Jordan

  • Reward is enough

    David Silver;Satinder P. Singh;Doina Precup;Richard S. Sutton

  • Near-Optimal Reinforcement Learning in Polynominal Time

    Michael J. Kearns;Satinder P. Singh

Frequent Co-Authors

Richard L. Lewis
Richard L. Lewis University of Michigan–Ann Arbor
Edmund H. Durfee
Edmund H. Durfee University of Michigan–Ann Arbor
Michael Kearns
Michael Kearns University of Pennsylvania
Michael P. Wellman
Michael P. Wellman University of Michigan–Ann Arbor
Honglak Lee
Honglak Lee University of Michigan–Ann Arbor
Yevgeniy Vorobeychik
Yevgeniy Vorobeychik Washington University in St. Louis
Andrew G. Barto
Andrew G. Barto University of Massachusetts Amherst
Doina Precup
Doina Precup McGill University
Michael L. Littman
Michael L. Littman Brown University
David Silver
David Silver DeepMind (United Kingdom)

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Pursuing Computer Science is just one of many online degree options for students interested in tech, business, or engineering fields. For those looking to fast-track their education, 6 month degree course options provide a quick way to enter the workforce, covering essential skills in a condensed timeline.

Affordability is a common concern, especially for online business or technology learners. Choosing the right program often means balancing quality with cost. Many students seek the online business degree cost that best matches their budget. For undergraduate studies, it's wise to compare the cheapest online college bachelor degree programs to minimize student debt.

If you’re interested in expanding your technical skills beyond computer science, an online bachelor's in engineering can open doors to diverse and high-demand career pathways. Exploring related online degrees not only supports career growth but also helps tailor your education to your personal and professional goals.

Best Scientists Citing Satinder Singh

Trending Scientists