Sandia Energy > Programs > Electric Grid > Advanced Grid Modeling > Key Personnel > Logan Blakely Logan Blakely Member of Technical Staff Contact Information Logan Blakely /505-845-7827 Biography Logan Blakely received his Master of Computer Science degree, specializing in Machine Learning, from Portland State University in 2018. His research focus is in machine learning applied to power systems challenges, particularly in the intersection merging physics domain knowledge with machine learning techniques. Education Master of Computer Science, Portland State University Google Scholar: Logan Blakely Key Publications 2020 L. Blakely, M. J. Reno, “Phase Identification Using Co-Association Matrix Ensemble Clustering”, IET Smart Grid, Jun. 2020 F. Wilches-Bernal, B. Knueven, A. Staid, and J.P. Watson. “Models and Analysis of Fuel Switching Generation Impacts on Power System Resilience.” In Proceedings of the 2020 IEEE Power and Energy Society General Meeting, Montreal (Virtual), QC, Aug. 2020. L. Blakely and M. J. Reno, “Identifying Errors In Service Transformer Connections,” PES General Meeting, Aug. 2020 (pending). K. Mason, M. J. Reno, L. Blakely, S. Vejdan, S. Grijalva, “A Deep Neural Network Approach for Behind-the-Meter Residential PV Size, Tilt, and Azimuth Estimation”, Solar Energy, vol 196, pp. 260-269. Jan. 2020.