Rate My Professor Jon Calhoun

Calhoun's research specializes in high-performance computing, focusing on integrating lossy and lossless data compression into scientific workflows to mitigate data generation, movement, and storage bottlenecks, while enhancing the reliability, scalability, and performance of large-scale parallel scientific software. Funded by the NSF, DOE, and U.S. Army, his contributions are evidenced in key publications such as "SZ3: A modular framework for composing prediction-based error-bounded lossy compressors" (2022), "cuSZ: An efficient GPU-based error-bounded lossy compression framework for scientific data" (2020), "IPAS: Intelligent protection against silent output corruption in scientific applications" (2016), "FlipIt: An LLVM based fault injector for HPC" (2014), and "Exploring the feasibility of lossy compression for PDE simulations" (2019). He actively contributes to the field as a regular NSF panelist, program committee member for conferences including Supercomputing, IPDPS, and ISC, and reviewer for journals like IEEE Transactions on Parallel and Distributed Systems, IEEE Transactions on Reliability, and Journal of Scientific Computing. Calhoun's work significantly impacts scientific and engineering disciplines by enabling more efficient HPC systems.