Guru Shabadi
PhD student at the University of Pennsylvania
About
I am a PhD student at the University of Pennsylvania where I am advised by Rajeev Alur. I previously graduated from École Polytechnique with bachelor’s and master’s degrees in mathematics and computer science. My current research interests revolve broadly around designing, analyzing, and verifying modern multi-agent systems, using ideas and tools from mechanism design, reinforcement learning, and (probabilistic) model checking. The application areas that I work with include natural language and embodied reasoning.
During my undergrad and master’s, I had the honor and pleasure of working with Alessio Mansutti on optimization in integer linear-exponential programs at the IMDEA Software Institute in Madrid, Spain (SODA’26); Nathanaël Fijalkow on programmatic reinforcement learning (GenPlan’25) at the University of Warsaw; and Caterina Urban on building an abstract-interpretation-based tool to verify data science Jupyter notebooks (SOAP’23) at ENS Ulm, Paris. I started my journey in formal-methods research working with Sergio Mover on handling communication delays arising in cyber-physical systems at École Polytechnique, Paris.
Publications
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Guru Shabadi, Aaditya Naik, Rajeev Alur, Mayur Naik.
Learning Strategies to Break Judges Preprint.
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Guru Shabadi, Kaushik Mallik.
Auction-Based Online Policy Adaptation for Evolving Objectives Conference on Neural Information Processing Systems (NeurIPS), 2026.
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Aaditya Naik*, Guru Shabadi*, Rajeev Alur, Mayur Naik.
Do We Need Frontier Models to Verify Mathematical Proofs? Third Conference on Language Modeling (COLM), 2026.
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Guru Shabadi, Rajeev Alur.
Risk-Sensitive Agent Compositions International Conference on Learning Representations (ICLR), 2026.
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S Hitarth*, Alessio Mansutti*, Guru Shabadi*.
Optimization Modulo Integer Linear-Exponential Programs ACM-SIAM Symposium on Discrete Algorithms (SODA), 2026.
Workshop Papers
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Guru Shabadi, Nathanaël Fijalkow, Théo Matricon.
Programmatic Reinforcement Learning: Navigating Gridworlds Generalization in Planning Workshop, AAAI 2025.
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Luca Negrini, Guru Shabadi, Caterina Urban.
Static Analysis of Data Transformations in Jupyter Notebooks SOAP, ACM SIGPLAN Conference on Programming Language Design and Implementation, 2023.