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Jon Calhoun

Jon CalhounAssistant Professor of Electrical and Computer Engineering

Ph.D., 2017 - University of Illinois, Urbana Champaign
Computer Science
B.S., 2012 - Arkansas State University
B.S., 2012 - Arkansas State University
Computer Science

Contact Information
Office: 221-C Riggs Hall
Office Phone: 864.656.2646
Fax: 864.656.5910


Prior to joining Clemson University in 2017, Dr. Calhoun graduated summa cum laude with a B.S. in computer science and summa cum laude with a B.S. in mathematics with a minor in statistics from Arkansas State University in 2012. While there, he became the first student in Arkansas State University’s history to successfully complete two senior honors theses. In recognition of his accomplishments at Arkansas State University, Dr. Calhoun was elected into the Arkansas TRIO program Hall of Fame in 2013. In 2017, he received his Ph.D. in computer science from the University of Illinois at Urbana-Champaign. His dissertation entitled “From Detection to Optimization: Impact of Soft Errors on High-Performance Computing Applications” was completed under the direction of Dr. Luke Olson and Dr. Marc Snir. While at Illinois Dr. Calhoun was selected as a Blue Waters Graduate Fellow in 2014, won best poster at Cluster 2015, and interned at Lawrence Livermore National Laboratory in 2014 and Argonne National Laboratory in 2016. 

Dr. Calhoun’s  research interests lie in fault tolerance and resilience in high-performance computing (HPC) systems. In particular, Dr.  Calhoun investigates soft errors (transient, undetected state corruption due to hardware faults or undetected programming bugs) and their impact on HPC applications. To facilitate development and evaluation of software based soft error detection schemes, he has lead the development of fault injection and analysis tools. His compiler based fault injector FlipIt is used by several research group and research laboratories. In addition to his work on soft error detection, Dr. Calhoun investigates how application runtime can be optimized by using approximate computing techniques and lossy compression for data storage and transmission.