Kuldeep Singh MEEL

Sung Kah Kay Assistant Professor
Ph.D. (Computer Science, Rice University, Sep 2017)
M.S. (Computer Science, Rice University, May 2014)
B.Tech. (Computer Science & Engineering, Indian Institute of Technology, Bombay, Aug 2012)
COM2-03-41
651 61422

Research Areas

  • Artificial Intelligence
  • Algorithms & Theory
  • Programming Languages & Software Engineering

Research Interests

  • Constrained Counting and Sampling
  • Verification of AI Systems
  • Interpretable Decision Making
  • Probabilistic Reasoning
  • SAT
  • Knowledge Representation and Reasoning

Profile

Dr. Kuldeep Meel is an Assistant Professor of Computer Science in School of Computing at the National University of Singapore. He received his Ph.D. (2017) and M.S. (2014) degree in Computer Science (Artificial Intelligence and Formal Methods) from Rice University. He holds B. Tech. (with Honors) degree (2012) in Computer Science and Engineering from Indian Institute of Technology, Bombay. Dr. Meel's research interests lie at the intersection of Artificial Intelligence and Formal Methods. The broader goal of his research is to advance artificial intelligence techniques, which utilize ubiquity of data and formal methods, to enable computing to deal with increasingly uncertain real-world environments. Besides his primary research program, Meel has worked on other applications of data science and constrained reasoning including interpretable classification rules, hardware verification, validation of distributed synchronization protocol, automatic data layout generation, program synthesis, and probabilistic programming. Dr. Meel has co-presented tutorials at top-tier AI conferences, UAI 2016 and AAAI 2017. His work received the 2014 Outstanding Masters Thesis Award from Vienna Center of Logic and Algorithms and Best Student Paper Award at CP 2015. He received the IBM Ph.D. Fellowship and the Lodieska Stockbridge Vaughn Fellowship for his work on constrained sampling and counting.

Current Projects

  • Constrained Counting and Sampling: Pushing the Scalability Envelope
  • Interpretable Rule Learning
  • Verification of Probabilistic Systems
  • Demystifying CNF-XOR Formulas

Selected Publications

  • The Hard Problems Are Almost Everywhere For Random CNF-XOR Formulas 
    Jeffrey Dudek, Kuldeep S. Meel, and Moshe Y. Vardi 
    Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), 2017.

  • Counting-Based Reliability Estimation for Power-Transmission Grids 
    Leonardo Duenas-Osorio, Kuldeep S. Meel, Roger Paredes, and Moshe Y. Vardi 
    Proceedings of AAAI Conference on Artificial Intelligence (AAAI), 2017.

  • Algorithmic Improvements in Approximate Counting for Probabilistic Inference: From Linear 
    to Logarithmic SAT Calls 
    Supratik Chakraborty, Kuldeep S. Meel, and Moshe Y. Vardi 
    Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), 2016.

  • On Computing Minimal Independent Support and Its Applications to Sampling and Counting 
    Alexander Ivrii, Sharad Malik, Kuldeep S. Meel, and Moshe Y. Vardi 
    Proceedings of International Conference on Constraint Programming (CP), 2015.
    Best Student Paper Award 

  • Distribution-Aware Sampling and Weighted Model Counting for SAT 
    Supratik Chakraborty, Daniel J. Fremont, Kuldeep S. Meel, Sanjit A. Seshia, and Moshe Y. Vardi 
    Proceedings of AAAI Conference on Artificial Intelligence (AAAI), 2014.

Awards & Honours

  • Lodieska Stockbridge Vaughn Fellowship (2016-17), awarded to up to five students university wide whose record at Rice shows evidence of outstanding achievement and promise.
  • IBM PhD Fellowship (2016-17)
  • 2014 Outstanding Masters Thesis Award from the Vienna Center for Logic and Algorithms
  • Best Student Paper Award, 21st International Conference on Principles and Practice of Constraint Programming (CP-2015)
  • Andrew Ladd Memorial Excellence in Computer Science Fellowship (2013-14)
  • Heidelberg Laureate Forum 2015 Invitee

Teaching (2019/2020)

  • CS4269: Fundamentals of Logic in Computer Science
  • CS5469: Fundamentals of Logic in Computer Science
  • CS4244: Knowledge-Based Systems