Department of Computer Science
University of Toronto
Welcome to the Meel Group’s web page. We are situated at the University of Toronto.
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.
Given the broad nature of the field of automated reasoning, our research group's work spans multiple traditional subfields of computer science, reflected by publication record as well as recognition in artificial intelligence (AAAI: 17×, IJCAI: 13×, NeurIPS: 6×), formal methods (CAV: 7×, CP: 8×, SAT: 6×, TACAS: 3×), design automation (ICCAD: 2×, DATE: 2×, DAC: 1×), and logic/databases (PODS: 4×, ICALP: 1×, LPAR: 4×, LICS: 2×). In short, a research group that is not bound by (traditional) borders.
A weighted model counter for first-order logic
Manthan: A Data-Driven Approach for Boolean Function Synthesis
NPAQ: Neural Property Approximate Quantifier
A hashing-based algorithm for discrete integration over finite domains.
A framework to provide white-box access to the execution of SAT solver.
GANAK: A Scalable Probabilistic Exact Model Counter
An algorithm to generate uniform samples subject to given set of constraints.
On Testing of Uniform Samplers
WAPS: Weighted and Projected Sampling
An ANF and CNF simplifier and converter.
Knowledge Compilation meets Uniform Sampling
An algorithm to compute minimal independent support for a given CNF formula.
An approximate model counter for Bitvector theory.
A hashing-based approximate sampler for weighted CNF formulas.
A weighted model counter over Boolean domains.
Phase Transition Behavior of Cardinality and XOR Constraints
We are always looking for highly motivated Ph.D. students, research assistants and summer internship for exceptional undergraduate interns in our group. We work at the intersection of algorithmic design and systems; therefore, an ideal candidate should have deeper expertise in one area and willingness to learn the other. A strong background in statistics, algorithms/formal methods and prior experience in coding is crucial to make a significant contribution to our research.
UofT is a world-class university that provides an outstanding and supportive research environment. Its Department of Computer Science is highly ranked (within the top 15) among the computer science departments in the world. Toronto is a vibrant, well-connected city and a research hub in North America.
