Meel Group

Department of Computer Science
University of Toronto

Welcome to the Meel Group’s web page. We are situated at the University of Toronto.

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Interests

  • Artificial Intelligence
  • Constrained Sampling and Counting
  • Knowledge Representation and Reasoning
  • Formal Methods
  • Interpretable Models

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.

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.

Publications

Quickly discover relevant content by filtering publications.
(2024). Equivalence Testing: The Power of Bounded Adaptivity. In International Conference on Artificial Intelligence and Statistics (AISTATS).

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(2024). Conjunctive Queries on Probabilistic Graphs: The Limits of Approximability. In Proceedings of The International Conference on Database Theoryn (ICDT).

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(2023). An Approximate Skolem Function Counter. In Proceedings of AAAI Conference on Artificial Intelligence (AAAI).

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(2023). Engineering an Exact Pseudo-Boolean Model Counter. In Proceedings of AAAI Conference on Artificial Intelligence (AAAI).

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(2023). Exact ASP Counting with Compact Encodings. In Proceedings of AAAI Conference on Artificial Intelligence (AAAI).

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News

Five Papers accepted to AAAI 2024.
1. The first paper is Auditable Algorithms for Approximate Model Counting
    Authors: S. Akshay, Supratik Chakraborty and Kuldeep S. Meel
2. The second paper is An Approximate Skolem Function Counter
    Authors: Arijit Shaw, Brendan Juba and Kuldeep S. Meel
3. The third paper is Exact ASP Counting with Compact Encodings
    Authors: Mohimenul Kabir, Supratik Chakraborty and Kuldeep S. Meel
4. The fourth paper is Testing Self-Reducible Samplers
    Authors: Rishiraj Bhattacharyya, Sourav Chakraborty, Yash Pote, Uddalok Sarkar and Sayantan Sen
5. The fifth paper is Engineering an Exact Pseudo-Boolean Model Counter
    Authors: Suwei Yang and Kuldeep S. Meel
We have presented a tutorial on auditing bias in machine learning in IJCAI 2023. Presenters: Bishwamittra Ghosh and Debabrota Basu.
In this tutorial, we address three questions on bias in machine learning: (i) Choosing a compatible fairness metric based on application context, (ii) Formally quantifying fairness with respect to the compatible metric, and (iii) Explaining the sources of unfairness corresponding to the metric.
We will present our paper Solving the Identifying Code Set Problem with Grouped Independent Support this month at IJCAI 2023.
We show how reducing an NP-hard problem to a problem in the second order of the polynomial hierarchy helps us to exponentially decrease the encoding size. By leveraging modern solvers that solve problems beyond NP, we can solve much larger problem instances than the former state of the art.
If you are attending IJCAI in Macau, please come to our talk on Wednesday 23rd August, at 11:45am in the CSO: Constraint Programming session, or join us for the poster session afterwards, from 5pm until 6:30pm. You can also check out our preprint or watch this short video, which summarises our contribution.
Authors: Anna L.D. Latour, Arunabha Sen, Kuldeep S. Meel
Our work on Rounding Meets Approximate Model Counting has been accepted to CAV 2023 and received Distinguished Paper Award.
We round the approximate count of ApproxMC, which allows us to achieve 4$\times$ speedup over the state of the art.
Authors: Jiong Yang and Kuldeep S. Meel

Software


Crane

A weighted model counter for first-order logic

Manthan

Manthan: A Data-Driven Approach for Boolean Function Synthesis

NPAQ

NPAQ: Neural Property Approximate Quantifier

ApproxMC

A hashing-based algorithm for discrete integration over finite domains.

CrystalBall

A framework to provide white-box access to the execution of SAT solver.

GANAK

GANAK: A Scalable Probabilistic Exact Model Counter

UniGen

An algorithm to generate uniform samples subject to given set of constraints.

Barbarik

On Testing of Uniform Samplers

WAPS

WAPS: Weighted and Projected Sampling

Bosphorus

An ANF and CNF simplifier and converter.

KUS

Knowledge Compilation meets Uniform Sampling

MIS

An algorithm to compute minimal independent support for a given CNF formula.

SMTApproxMC

An approximate model counter for Bitvector theory.

WeightGen

A hashing-based approximate sampler for weighted CNF formulas.

WeightMC

A weighted model counter over Boolean domains.

1-CARD-XOR

Phase Transition Behavior of Cardinality and XOR Constraints

Meet the Team

Faculty

Postdoctoral Researchers

PhD Students

Alumni

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Yong Lai

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Rémi Delannoy

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Alexis de Colnet

Ananth Krishna Kidambi

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Biswadeep

Guramrit Singh

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Lawqueen Kanesh

Past Visitors

2023

Dror Fried

Open University of Israel

S Akshay

IIT Bombay

2019

Pavan Aduri

Iowa State

Roland Jie-Hong

National Taiwan University

2018

Vijay Ganesh

Waterloo

Openings

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.

Application Procedure:

  • If you are a student at the University of Toronto, feel free to drop by Kuldeep's office or schedule a meeting with him. (See his calendar).
  • Post-doc position: We have multiple post-doc positions available. Interested candidates should email meel+postdoc@cs.toronto.edu with a PDF of CV, which must contain information of at least two references. Furthermore, a short write up indicating your interest in a particular theme is required. The initial term of appointment will be one year extensible for another year, upon review of satisfactory performance. The selected candidates will be offered competitive salaries and benefits including generous travel funding to top-tier conferences.
  • Internship (>=6 months): We strongly prefer candidates who want to use their internship as a way to apply for PhD programs (at UofT or elsewhere; of course, if you are good, we would like you to remain at UofT). Email meel+interns@cs.toronto.edu with a PDF of your CV. Make sure your subject contains the word "olleh" and you should include reviews of two of the papers published in the previous 3 years at AAAI/IJCAI/CP/SAT/CAV conferences. The reviews should be in the body of the email (and not as pdf). Furthermore, the body of your email should contain the phrase: "Here are two papers that I have reviewed". You should also provide reason for your choice of the papers.
  • Short term internship (=3 months): We may offer short term internship to exceptional undergraduates. Same process as above.
  • PhD positions: Admissions to the Department of Computer Science at UofT are handled via a central admission procedure.
  • UofT and Toronto

    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.

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