معرفی
Kazem Cheshmi is an Assistant Professor in the Department of Electrical and Computer Engineering at McMaster University. His research focuses on compiler optimization techniques for accelerating scientific computing and machine learning applications on parallel architectures. He leads the SwiftWare Lab and teaches courses such as High-Performance Programming (COMPENG 4SP4/ECE 6SP4) and Special Topics in Computation (ECE 718).
Education: B.Eng. (Ferdowsi University of Mashhad), M.A.Sc. (University of Tehran), Ph.D. (University of Toronto). He has held research positions at Microsoft Research, Adobe Research, Concordia University, and Rutgers University.
Research Interests: High-performance computing, compiler design, sparse matrix computations, and their applications in machine learning and scientific computing. His work emphasizes optimizing sparse codes for parallel architectures and developing efficient QP solvers like NASOQ.
Key Contributions: Developed Sympiler (a domain-specific compiler for sparse matrix codes) and NASOQ (a scalable QP solver). His awards include the ACM-IEEE CS George Michael Memorial HPC Fellowship (2020) and recognition for contributions to compiler-driven sparse computation optimization.
Teaching and Service: Organizes SONAD’25, serves on program committees for PPoPP, Supercomputing, and IPDPS. Supervises students in compiler design, parallel programming, and high-performance computing.
Labs/Teams: Leads the SwiftWare Lab focusing on compiler optimization and high-performance systems. Collaborates on open-source projects like Sympiler and NASOQ.
