🧑‍🔬 Sanjit Arunkumar Seshia

🏛️ Affiliations

🎓University of California, BerkeleyBerkeley · United States2006–2026 · 322 paperscurrent🎓Carnegie Mellon UniversityPittsburgh · United States2002–2018 · 23 papers🎓Tata Institute of Fundamental ResearchMumbai · India1999 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 10 more institutions on single bylines are not listed.

🧭 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📅 Works by year

2010s

📄 Papers

The most cited first (OpenAlex and our library).

1. Syntax-Guided Synthesis (2013 · 633 citations)
   Many recent synthesis tools, including program sketching, superoptimization, loop-free program synthesis and example-driven spreadsheet macros, let users restrict candidate programs syntactically as well as specifying them logically.
2. Learning Heuristics for Quantified Boolean Formulas through Deep Reinforcement Learning (2018 · 12 citations)
   The paper asks whether deep reinforcement learning can produce better branching heuristics for solvers of quantified Boolean formulas (QBF), specifically 2QBF of the form ∀X.∃Y.ϕ with ϕ in CNF.

All works

7 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

1. Syntax-Guided SynthesisFMCAD 2013Rajeev Alur, Rastislav Bodik, Garvit Juniwal, et al.2013 · 633 citations
2. Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems ↗arXiv (Cornell University) · no summary yetDavid Dalrymple et al.2024 · 24 citations
3. Cerberus ↗ACM SIGSAC Conference on Computer and Communications Security (CCS) · no summary yetDayeol Lee et al.2022 · 14 citations
4. Learning Heuristics for Quantified Boolean Formulas through Deep Reinforcement LearningarXiv:1807.08058Gil Lederman, Markus N. Rabe, Edward A. Lee, et al.2018 · 12 citations
5. Programmatic Modeling and Generation of Real-Time Strategic Soccer Environments for Reinforcement Learning ↗Proceedings of the AAAI Conference on Artificial Intelligence · no summary yetAbdus Salam Azad et al.2022 · 5 citations
6. Scenic4RL: Programmatic Modeling and Generation of Reinforcement Learning Environments ↗arXiv (Cornell University) · no summary yetAzad, Abdus Salam et al.2021 · 3 citations
7. optimize_anything: A Universal API for Optimizing any Text Parameter ↗arXiv:2605.19633 · Proceedings of the ACM Conference on AI and Agentic Systems (CAIS 26), May 26-29, 2026, San Jose, CA, USA · no summary yetLakshya A Agrawal et al.2026 · 1 citation

🤝 Co-authors here

3 co-authors with a page, the most papers together first.

• Armando Solar-Lezama
• Markus N. Rabe
• Rishabh Singh

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• ACM SIGSAC Conference on Computer and Communications Security (CCS)
• FMCAD 2013
• Proceedings of the AAAI Conference on Artificial Intelligence

🔗 Cited by

Papers of our library cite their works 48 times (of the 149,439 papers whose bibliographies we read); 18 citing papers with a page here →.

Updated 3 November 2026

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