CV
Education
- Ph.D., Mechanical and Aerospace Engineering, Arizona State University, Aug 2025 – present
- Advisor: Prof. Kunal Garg; GPA 4.25/4.00
- M.Tech., Electrical Engineering, Indian Institute of Technology Gandhinagar, 2018 – 2020
- Advisor: Prof. Naran Pindoriya; GPA 9.43/10
- Thesis: Peer-to-Peer Energy Trading Platform: Hardware and Software Integration
- B.E., Electrical Engineering, L. D. College of Engineering, Gujarat Technological University, 2014 – 2018
- GPA 9.05/10
Experience
- Graduate Research Assistant, Safe Robotics Lab (SRG), Arizona State University, Aug 2025 – present
- Dissertation: learning-based predictive safety for autonomous systems: enforcing barrier constraints over predicted horizons and diagnosing failures in learned controllers, scaling from single-agent control to distributed multi-robot teams under communication delay.
- Researcher, Data and Decision Sciences, TCS Research, Mumbai, Sept 2020 – July 2025
- Mentors: Dr. Mayank Baranwal, Dr. Harshad Khadilkar.
- RL for safety-critical networked infrastructure under uncertainty and adversarial attack: multi-robot coordination, power grids, railway scheduling, and supply chain. NASSCOM AI Gamechangers Award, top 10 AI projects in India (2024).
- Adjunct Faculty, B.Sc. Data Science, S.P. Jain School of Global Management, Mumbai, Dec 2023 – July 2025
- Taught Introduction to Data Science and Introduction to Programming; supervised undergraduate capstone projects.
- Teaching Assistant, Electrical Systems Lab and Electrical & Electronics Lab, IIT Gandhinagar, Aug 2018 – Apr 2020
Selected projects
- BarrierFormer: transformer-guided predictive barrier enforcement (CoRL 2026, main track; project page)
- Encoded horizon-level discrete-time CBF constraints into policy parameters using an SQP-based safety teacher and a barrier critic over transformer-predicted rollouts, removing online optimization and model knowledge at inference.
- Raised safety rate to 96% across 2D/3D and linear/nonlinear dynamics against 65–76% for diffusion- and transformer-based baselines; matched model-based MPC on safety while cutting per-step latency by three orders of magnitude (5 µs vs 8.3 ms); generalized zero-shot to larger, denser workspaces.
- FIND-R: failure identification and directed repair (under review)
- Localized safety-encoding failures in GNN controllers (GCBF+, InforMARL) by probing layer-wise representations, tracing the bottleneck to an unsupervised attention mechanism and repairing it with a supervised radial-attention head, cutting false modes by 95% and 65% respectively.
- Ran end-to-end in under 9 s where SMT and CROWN formal-verification baselines exceeded a 4-hour budget.
- MRTAgent: safe multi-robot task allocation (ECAI 2024; AAMAS 2025; patents)
- Dual-agent RL framework inspired by self-play for real-time fleet task allocation, coupled with a modified LQR controller for collision-free navigation; safety enforced at the control layer while the learned policy handled assignment.
- PowRL: constraint-safe RL for power grids under adversarial attack (AAAI 2023; U.S. patent granted)
- Heuristic-guided safety filter masking the action space to a verified-feasible subset, cutting 72,000 actions to 240 on the IEEE 118-bus network without violating operational limits.
- Ranked 1st on L2RPN NeurIPS 2020 (Robustness), top-performing agent in L2RPN WCCI 2020, and 3rd of 90 teams in L2RPN 2023 (TU Delft / RTE-France).
- Prosumer-driven integrated smart grid (IIT Gandhinagar; patent)
- Linear-programming model for peer-to-peer energy trading and a physical testbed integrating solar PV, battery storage, V2G, and an IoT-based smart energy management device, with a blockchain-backed trading interface.
Publications
Full list on the publications page.
- BarrierFormer: Transformer-Guided Predictive Barrier Enforcement for Safe Robot Control, Conference on Robot Learning (CoRL), main track, 2026
- PRIORITY-Q: Certifiable Event-Queue Scheduling for Real-Time Railway Resource Allocation, International Conference on Neural Information Processing (ICONIP), main track, 2026
- Together We Rise: Optimizing Real-Time Multi-Robot Task Allocation using Coordinated Heterogeneous Plays, AAMAS, AAAI Track, 2025
- Optimizing Multi-Robot Task Allocation in Dynamic Environments via Heuristic-Guided Reinforcement Learning, European Conference on Artificial Intelligence (ECAI), 23% acceptance, 2024
- An Experimental Evaluation of a Blockchain-based Peer-to-Peer Energy Trading Framework, 13th IEEE PES ISGT Asia, 2024
- A Learning Approach for Discovering Cost-Efficient Integrated Sourcing and Routing Strategies in E-Commerce, CODS-COMAD, 2024
- Multi-Agent Learning of Efficient Fulfilment and Routing Strategies in E-Commerce, NeurIPS Workshop on Generalization in Planning, 2023
- PowRL: A Reinforcement Learning Framework for Robust Management of Power Networks, AAAI Conference on Artificial Intelligence, 19.2% acceptance, 2023
- Real-time Simulation of V2G Operation for EV Battery, 21st National Power Systems Conference (NPSC), 2020
Patents
Five filings across three inventions (three India, two U.S.), including granted U.S. Patent 12,476,460 B2. See the patents page.
Awards
- NASSCOM AI Gamechangers Award, 2024: top 10 AI research projects in India
- Learning to Run a Power Network (L2RPN) Challenge: 1st place, NeurIPS 2020 Robustness track; top-performing agent, WCCI 2020; 3rd of 90 teams, 2023 edition (TU Delft / RTE-France)
- India Smart Grid Forum (ISGF): Certificate of Merit, 2024 (AI-based control and smart grid management); Platinum Award, 2023 (smart agent for peer-to-peer energy trading and demand response); Gold Award, 2023 (power grid topological reconfiguration for congestion management)
- POSOCO Power System Award, 2021: one of 15 recipients in India for an M.Tech thesis in power systems (Grid-India and FITT, IIT Delhi)
Invited talks and service
- Invited talk, Learning and Control Colloquium, Systems & Control Engineering, IIT Bombay, 2024
- Invited talk, Applying Reinforcement Learning in the Real World, TCS iON, 2024
- Reviewer: CoRL, AAAI, ICONIP, Indian Control Conference (ICC), 2023–2026
Technical skills
- Languages: Python, MATLAB, SQL
- Learning: PyTorch, JAX/Flax, TensorFlow, Gymnasium, Hugging Face Transformers
- Optimization: CasADi, IPOPT, sequential quadratic programming, Z3, dReal
- Simulation: Grid2Op, Simulink, OPAL-RT, PSS/E, Raspberry Pi
- Tools: Git, Linux, AWS, LaTeX