Email: jereeves [at] cs [dot] cmu [dot] edu

About

Hello! My name is Joseph Reeves and I am a PhD student (entered Fall 2020) at Carnegie Mellon University advised by Marijn Heule and Randy Bryant.

My research interests revolve broadly around satisfiablity (SAT) solving. I have worked on finding short and explainable proofs within the propagation redudnancy (PR) proof system, and developed a PR preprocessing algorithm that appeared in several solvers in the 2023 SAT Competition. I have also explored proof summarization in my proof skeletons work, and am investigating ways to lift the core ideas to SMT solving.

For my PhD thesis, I am researching the ways in which we can leverage a cardinality-based input to improve SAT solvers. The introductory paper "From Clauses to Klauses" was presented at CAV 2024 (link below).

Research Publications and Talks

Software

Cardinality-CaDiCaL [repo] - the repository provides an extraction engine and solver configurations. The extractor transforms a formula in Conjunctive Normal Form (CNF) into a formula in Cardinality Conjunctive Normal Form (KNF). The solver is a modified version of CaDiCaL that takes as input a formula in KNF and applies one of three solving configurations: reencoding the cardinality constraints, propagating natively on the cardinality constraints, or a combination of both.

PReLearn [repo] - a preprocessor that extracts binary and ternary PR clauses from a CNF formula. These clauses can be added to the formula then passed to a SAT solver, with a general improvement in performance.

BiPartGen [repo] - generates a CNF for the perfect matching problem on bipartite graphs with random constructions and various at-most-one encodings. Additionally provides tools to produce symmetry-breaking clauses. A selection of formulas were submitted to the SAT competition 2021.