Abhinav Bhatia

CV

Updated: Nov 26, 2025

Download PDF

Interests

Artificial Intelligence, Reinforcement Learning, Inverse Reinforcement Learning, Meta Reinforcement Learning, Real-time Planning, Robotics

Education

• Ph.D in Computer Science, University of Massachusetts, Amherst, 2026 (expected).
  • Advisor: Prof. Shlomo Zilberstein
  • Coursework: Artificial Intelligence, Reinforcement Learning, Robotics, Advanced Robot Dynamics & Control, Machine Learning, Neural Networks, Advanced Algorithms, Empirical Research Methods, Advanced Information Assurance
• M.S. in Computer Science, University of Massachusetts, Amherst, 2022. GPA 3.95/4.
• B.E. (Hons.) in Computer Science, Birla Institute of Technology and Science, Pilani, 2015. GPA 9.27/10.

Work experience

• Jun 2024 - Aug 2024: Applied Sciences Intern at Microsoft Xbox Game Studios, Redmond
  • Worked on multi-task inverse reinforcement learning and offline reinforcement learning solutions for automated game playing.
• Jun 2017 - Jul 2019: Research Engineer at School of Computing and Information Systems, Singapore Management University
  • Supervised by Prof. Pradeep Varakantham and Prof. Akshat Kumar
  • Worked on optimizing constrained resource allocation at city scale using deep reinforcement learning, which led to a publication.
• Aug 2015 - Jun 2017: Software Engineer at Walmart Labs, Bengaluru
  • Was part of Operations, Analytics & Research team for supply-chain division of Walmart’s eCommerce.
  • Developed an Elasticsearch based distributed database for data analysis.
  • Developed a deep-learning based system for anomaly-detection in large live incoming data streams.
• Jan 2015 - Jun 2015: Software Development Engineer Intern at Amazon, Bengaluru
  • Worked on offline experience for Prime Video.
  • Worked on optimizing content load time for Prime Video on Kindle tablets.

Publications

• RL$^3$: Boosting Meta Reinforcement Learning via RL inside RL$^2$
  Bhatia, A., Nashed, SB., & Zilberstein, S. In NeurIPS Workshop on Generalization in Planning (2023), In Reinforcement Learning Conference (2025). PDF

• Selecting the Partial State Abstractions of MDPs: A Metareasoning Approach with Deep Reinforcement Learning
  Nashed, S.B., Svegliato, J., Bhatia, A., Russell S., Zilberstein, S. (2022). In IEEE/RSJ International Conference on Intelligent Robots and Systems. PDF

• Adaptive Rollout Length for Model-Based RL Using Model-Free Deep RL
  Bhatia, A., Thomas, PS., & Zilberstein, S. (2022). In arXiv preprint arXiv:2206.02380. PDF

• Tuning the Hyperparameters of Anytime Planning: A Metareasoning Approach with Deep Reinforcement Learning
  Bhatia, A., Svegliato, J., Nashed, S. B., & Zilberstein, S. (2022). In Proceedings of the International Conference on Automated Planning and Scheduling. PDF

• Tuning the Hyperparameters of Anytime Planning: A Deep Reinforcement Learning Approach
  Bhatia, A., Svegliato, J., & Zilberstein, S. (2021). In ICAPS Workshop on Heuristics and Search for Domain-independent Planning. PDF

• On the Benefits of Randomly Adjusting Anytime Weighted A*
  Bhatia, A., Svegliato, J., & Zilberstein, S. (2021). In Proceedings of the International Symposium on Combinatorial Search. PDF

• Resource Constrained Deep Reinforcement Learning
  Bhatia, A., Varakantham, P., & Kumar, A. (2019). In Proceedings of the International Conference on Automated Planning and Scheduling. PDF

Teaching Experience

• Teaching Assistant | CS383 Artificial Intelligence | UMass Amherst, Fall 2022
  College of Information & Computer Sciences, University of Massachusetts Amherst
  Responsible for designing quizzes, clarifying students’ doubts and holding office hours.

Misc.

• Dissertation Writing Fellowship Awardee, UMass Amherst, Spring 2026.
• Program Committee member, IJCAI 2025.
• Organizing Committee member, AAAI 2025 GenPlan workshop.
• Program Committee member, IJCAI 2024.
• Program Committee member, NeurIPS 2023 GenPlan workshop.
• Paper reviewer, JMLR, 2023.
• Program Committee member, AAAI 2023.
• Paper reviewer, AIJ, 2021.
• As a member of IEEE BITS-Pilani chapter, conceptualized, developed and organized an AI bot making competition for a video game at college tech festival 2014.
• Won 1st prize for project PC 3D Gesture Interface using Kinect in design appliances category at BITS-Pilani tech festival 2014.
• Offered Kishore Vaigyanik Protsahan Yojana fellowship, which is an initiative by govt. of India to encourage young students to pursue a career science research, 2010.

Programming Skills

• Languages: Experienced in Python, C, Julia, C++, Java. Familiar with C#, SQL.
• Frameworks: OpenAI Gym, PyTorch, FluxML, Tensorflow, CPLEX, Elasticsearch, Unity3D

See Github profile

Relevant Ph.D. Coursework

Artificial Intelligence, Reinforcement Learning, Robotics, Advanced Robot Dynamics & Control, Machine Learning, Neural Networks, Advanced Algorithms, Empirical Research Methods, Advanced Information Assurance