Multiple MSc, PhD, and Postdoctoral positions available: Positions (PDF)
Research: My research group, ParaMathics, works on various aspects of cloud computing, machine learning, numerical analysis, compilers, programming languages, and high-performance computing. We develop scalable numerical methods, high-performance libraries, and domain-specific languages and compilers for high-performance and cloud computing platforms. CV (PDF)
- "ParSy: Inspection and Transformation of Sparse Matrix Computations for Parallelism" accepted at SC18. Paper
- I am the co-chair of the Machine Learning & HPC track at Supercomputing 2019.
- Our research on "Communication-Efficient Algorithms for Machine learning" receives an NSF award.
- "Reducing Communication in Proximal Newton Methods" accepted at ICPP18. Paper
- "CSTF: Large-Scale Sparse Tensor Factorizations on Distributed Platforms" accepted at ICPP18. Paper
- Our work on "Sparsity-Aware Storage Format Selection" receives the outstanding poster paper award at HPCS 2018.
- Kazem Cheshmi receives the 2018 Adobe Research Fellowship.
- Zachary Blanco receives the James Leroy Potter Award for research excellence.
- Sympiler is now online!
- "Sympiler: Transforming Sparse Matrix Codes by Decoupling Symbolic Analysis" accepted at SC17. Paper
- "A Unified Optimization Approach for Sparse Tensor Operations on GPUs" accepted at Cluster17. Paper
- Kazem Cheshmi wins First Place in the 2017 Grand Finals of the ACM’s Student Research Competition for our work on "Decoupling Symbolic from Numeric in Sparse Matrix Computations." The SRC Grand Finals are the culmination of a year-long competition that involved more than 300 students presenting research projects at 25 major ACM conferences.
- Maryam Dehnavi receives the NSF CRII grant on Performance-in-Depth Sparse Solvers for Heterogeneous Parallel Platforms.
- Kazem Cheshmi wins First Place in the ACM CGO Student Research Competition 2017.
- Aadiya Shukla wins the best poster award in the ECE research day for: Fault Tolerant Iterative Solvers with Adaptive Reliability.