Research

Our primary research interest is in automated reasoning. The long term vision of our research program is to advance automated reasoning techniques to enable computing to deal with increasingly uncertain real-world environments. The core theme of our research program is the quest for scalability. Accordingly, our work straddles theory and practice, and draws upon ideas from randomized algorithms, statistical inference, formal methods, distribution testing, and software engineering.

Our research spans multiple areas of computer science, as reflected in our publication record through 2026: artificial intelligence and machine learning (AAAI: 27×, IJCAI: 14×, NeurIPS: 6×, KR: 4×, AISTATS: 3×, ICML: 1×, ICLR: 1×), formal methods and constraint solving (CAV: 12×, SAT: 10×, CP: 9×, LPAR: 4×, TACAS: 3×), databases (PODS: 6×, ICDT: 2×), design automation (DAC: 3×, DATE: 3×, ICCAD: 2×), and theoretical computer science (LICS: 2×, SODA: 1×, ICALP: 1×, ESA: 1×). These connections allow us to bring advances in algorithms and reasoning systems to problems across disciplines.

Openings

Our group is based at the University of Toronto and the Georgia Institute of Technology. We work at the intersection of algorithm design and systems, with an emphasis on scalable automated reasoning. We welcome applicants with a strong background in algorithms, statistics, or formal methods and experience building research software.

PhD positions

Kuldeep plans to recruit 1–2 PhD students for Fall 2027. Please mention his name in your application and follow the application procedure of the institution you are applying to:

• University of Toronto, Department of Computer Science
• Georgia Institute of Technology, School of Computer Science

Current students

If you are a student at the University of Toronto or Georgia Tech and would like to discuss research opportunities, email Kuldeep to arrange a meeting.