Suguman Bansal

About

Suguman Bansal is an Assistant Professor in the School of Computer Science at Georgia Institute of Technology. Her research is focused on formal methods and their applications to artificial intelligence, programming languages, and machine learning. Previously, she was an NSF/CRA Computing Innovation Postdoctoral Fellow at the University of Pennsylvania, mentored by Prof. Rajeev Alur, and completed her Ph.D. at Rice University advised by Prof. Moshe Y. Vardi. She has received the 2020 NSF CI Fellowship, was named a 2021 MIT EECS Rising Star, and served as a keynote speaker at the 29th Static Analysis Symposium (SAS) in 2022.

Research topics

• Artificial Intelligence
• Computer Science
• Mathematics
• Mathematical optimization
• Theoretical computer science

Selected publications

• Hybrid Compositional Reasoning for Reactive Synthesis from Finite-Horizon Specifications

  2020-04-03 · 34 citations

  article1st author

  LTLf synthesis is the automated construction of a reactive system from a high-level description, expressed in LTLf, of its finite-horizon behavior. So far, the conversion of LTLf formulas to deterministic finite-state automata (DFAs) has been identified as the primary bottleneck to the scalabity of synthesis. Recent investigations have also shown that the size of the DFA state space plays a critical role in synthesis as well.Therefore, effective resolution of the bottleneck for synthesis require…

  PublisherDOI

• Model Checking Strategies from Synthesis over Finite Traces

  Lecture notes in computer science · 2023-01-01 · 5 citations

  book-chapter1st authorCorresponding
  PublisherDOI

• Specification-Guided Reinforcement Learning

  Lecture notes in computer science · 2022-01-01 · 3 citations

  book-chapter1st authorCorresponding
  PublisherDOI

• Multi-Agent Systems with Quantitative Satisficing Goals

  2023-08-01 · 2 citations

  articleOpen access

  In the study of reactive systems, qualitative properties are usually easier to model and analyze than quantitative properties. This is especially true in systems where mutually beneficial cooperation between agents is possible, such as multi-agent systems. The large number of possible payoffs available to agents in reactive systems with quantitative properties means that there are many scenarios in which agents deviate from mutually beneficial outcomes in order to gain negligible payoff improvem…

  PublisherOA PDFDOI

• Model Checking Strategies from Synthesis Over Finite Traces

  arXiv (Cornell University) · 2023-05-15 · 2 citations

  preprintOpen access1st authorCorresponding

  The innovations in reactive synthesis from {\em Linear Temporal Logics over finite traces} (LTLf) will be amplified by the ability to verify the correctness of the strategies generated by LTLf synthesis tools. This motivates our work on {\em LTLf model checking}. LTLf model checking, however, is not straightforward. The strategies generated by LTLf synthesis may be represented using {\em terminating} transducers or {\em non-terminating} transducers where executions are of finite-but-unbounded le…

  PublisherOA PDFDOI

Frequent coauthors

• Moshe Y. Vardi

  24 shared

• Rajeev Alur

  University of Pennsylvania

  15 shared

• Kishor Jothimurugan

  University of Pennsylvania

  13 shared

• Osbert Bastani

  California University of Pennsylvania

  13 shared

• Lucas M. Tabajara

  8 shared

• Gal Amram

  Tel Aviv University

  6 shared

• Swarat Chaudhuri

  6 shared

• Krishnendu Chatterjee

  Institute of Science and Technology Austria

  5 shared

Education

• PhD, Computer Science

  Rice University

  2020

• MSc, Computer Science

  Rice University

  2017

• BSc (Hons), Mathematics and Computer Science

  Chennai Mathematical Institute

  2014

Awards & honors

• 2020 NSF CI Fellowship
• 2021 MIT EECS Rising Star
• Keynote speaker at the 29th Static Analysis Symposium (SAS)…

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