J. Austin Ellis is a staff member in the Analytics & AI Methods at Scale group at the OLCF. He completed his B.S. degree in Applied Mathematics at the University of North Carolina at Chapel Hill in 2012. From 2016 to 2018, he was an intern at ORNL in the radiation transport group working on the Shift Monte Carlo radiation transport code. He completed his Ph.D. in Applied Mathematics at North Carolina State University in 2018. Recently, he was a postdoc in the Scalable Algorithms group at Sandia National Laboratories working on machine learning and high performance computing with a focus in material science and climate science.
2020 - Gordon E. Moon, J. Austin Ellis, Aravind Sukumaran-Rajam, Srinivasan Parthasarathy, and P. Sadayappan. 2020. ALO-NMF: Accelerated Locality-Optimized Non-negative Matrix Factorization. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '20). Association for Computing Machinery, New York, NY, USA, 1758–1767. doi:https://doi.org/10.1145/3394486.3403227
2019 - J. Austin Ellis and Siva Rajamanickam, "Scalable Inference for Sparse Deep Neural Networks using Kokkos Kernels," 2019 IEEE High Performance Extreme Computing Conference (HPEC), Waltham, MA, USA, 2019, pp. 1-7, doi: 10.1109/HPEC.2019.8916378.