World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
53
Citations
12054
World Ranking
4804
National Ranking
2235

Research.com Recognitions

  • 2014 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to the field of machine learning including pioneering work in robot learning and representation discovery.

Overview

Sridhar Mahadevan is affiliated with the University of Massachusetts Amherst in the United States. Their research primarily spans the field of Computer Science, with a significant focus on Artificial Intelligence, Computational Theory and Mathematics, and Management Science and Operations Research. Additional subfields include Signal Processing and Computer Vision and Pattern Recognition.

Their work addresses various advanced topics, encompassing Bayesian Modeling and Causal Inference, Rough Sets and Fuzzy Logic, Topological and Geometric Data Analysis, Advanced Bandit Algorithms Research, Auction Theory and Applications, Non-Destructive Testing Techniques, and Infrastructure Maintenance and Monitoring.

Some recent papers authored by Sridhar Mahadevan include:

  • Finite-Sample Analysis of Proximal Gradient TD Algorithms, 2020, published in arXiv (Cornell University)
  • Manifold Warping: Manifold Alignment over Time, 2021, published in Proceedings of the AAAI Conference on Artificial Intelligence
  • Multi-fidelity physics-informed machine learning for probabilistic damage diagnosis, 2023, published in Reliability Engineering & System Safety
  • Regularized Off-Policy TD-Learning, 2020, published in arXiv (Cornell University)
  • Optimizing for the Future in Non-Stationary MDPs, 2020, published in arXiv (Cornell University)

Sridhar Mahadevan has frequently published in venues such as arXiv (Cornell University), Proceedings of the AAAI Conference on Artificial Intelligence, Entropy, Reliability Engineering & System Safety, and the 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).

Frequent co-authors collaborating with Sridhar Mahadevan include:

  • Zhao Song
  • Georgios Theocharous
  • Sarah Miele
  • Pranav Karve
  • Ritwik Sinha

In 2014, Sridhar Mahadevan was recognized as a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) for contributions to machine learning research, including work in robot learning and representation discovery.

Best Publications

  • Recent Advances in Hierarchical Reinforcement Learning

    Andrew G. Barto;Sridhar Mahadevan

  • Automatic programming of behavior-based robots using reinforcement learning

    Sridhar Mahadevan;Jonathan Connell

  • LEAP: a learning apprentice for VLSI design

    Tom M. Mitchell;Sridbar Mahadevan;Louis I. Steinberg

  • Average reward reinforcement learning: foundations, algorithms, and empirical results

    Sridhar Mahadevan

  • Recent Advances in Hierarchical Reinforcement Learning

    Unknown

  • Heterogeneous domain adaptation using manifold alignment

    Chang Wang;Sridhar Mahadevan

  • Proto-value Functions: A Laplacian Framework for Learning Representation and Control in Markov Decision Processes

    Sridhar Mahadevan;Mauro Maggioni

  • Manifold alignment using Procrustes analysis

    Chang Wang;Sridhar Mahadevan

  • Generative Multi-Adversarial Networks

    Ishan P. Durugkar;Ian Gemp;Sridhar Mahadevan

  • Solving Semi-Markov Decision Problems Using Average Reward Reinforcement Learning

    Tapas K. Das;Abhijit Gosavi;Sridhar Mahadevan;Nicholas Marchalleck

  • Robot Learning

    Jonathan H. Connell;Sridhar Mahadevan

  • Manifold alignment without correspondence

    Chang Wang;Sridhar Mahadevan

  • Hierarchical multi-agent reinforcement learning

    Rajbala Makar;Sridhar Mahadevan;Mohammad Ghavamzadeh

  • Hierarchical multi-agent reinforcement learning

    Mohammad Ghavamzadeh;Sridhar Mahadevan;Rajbala Makar

  • Self-Improving Factory Simulation using Continuous-time Average-Reward Reinforcement Learning

    Sridhar Mahadevan;Tapas K. Das;Abhijit Gosavi

  • Robot Learning

    Unknown

  • A study of machine learning regression methods for major elemental analysis of rocks using laser-induced breakdown spectroscopy

    Thomas F. Boucher;Marie V. Ozanne;Marco L. Carmosino;M. Darby Dyar

  • Repairing Disengagement With Non-Invasive Interventions

    Ivon Arroyo;Kimberly Ferguson;Jeff Johns;Toby Dragon

  • Proto-value functions: developmental reinforcement learning

    Sridhar Mahadevan

  • Value Function Approximation with Diffusion Wavelets and Laplacian Eigenfunctions

    Sridhar Mahadevan;Mauro Maggioni

  • Finite-sample analysis of proximal gradient TD algorithms

    Bo Liu;Ji Liu;Mohammad Ghavamzadeh;Sridhar Mahadevan

  • Hierarchical Memory-Based Reinforcement Learning

    Natalia Hernandez-Gardiol;Sridhar Mahadevan

Frequent Co-Authors

Mohammad Ghavamzadeh
Mohammad Ghavamzadeh Amazon (United States)
Ji Liu
Ji Liu Facebook (United States)
M. Darby Dyar
M. Darby Dyar Mount Holyoke College
Jonathan H. Connell
Jonathan H. Connell IBM (United States)
Beverly Park Woolf
Beverly Park Woolf University of Massachusetts Amherst
Tom M. Mitchell
Tom M. Mitchell Carnegie Mellon University
Mauro Maggioni
Mauro Maggioni Johns Hopkins University
Andrew G. Barto
Andrew G. Barto University of Massachusetts Amherst
Samuel M. Clegg
Samuel M. Clegg Los Alamos National Laboratory
John M. Henderson
John M. Henderson University of California, Davis

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

Exploring online degree options can be a smart move for those interested in Computer Science or related fields. Many universities now offer best colleges for low gpa applicants, giving a second chance to students who want to pursue higher education without a perfect academic record.

For learners eager to finish their studies quickly, there are fast track computer science degree programs available online. These accelerated paths allow motivated students to enter the workforce sooner by compressing coursework into a shorter timeframe.

If you are interested in environmental science and its intersection with technology, the cheapest online environmental science degree programs can make your education more affordable. After graduation, there are many options for what can you do with an environmental science degree, including roles in research, policy, consulting, and technology.

Whether you seek flexible scheduling, affordable tuition, or a broad range of career opportunities, choosing the right online degree can be a crucial first step toward your goals.

Best Scientists Citing Sridhar Mahadevan

Trending Scientists

Recently Published Articles