Sangkeun (Matt) M Lee Data Scientist (R&D Associate) Contact lees4@ornl.gov | 865.574.8858 All Publications Quantifying the Power System Resilience of the US Power Grid Through Weather and Power Outage Data Mapping A dataset of recorded electricity outages by United States county 2014–2022 Active learning of neural network potentials for rare events Visual Brick model authoring tool for building metadata standardization Performance analysis and comparison of data-driven models for predicting indoor temperature in multi-zone commercial building... Predicting Power Outage During Extreme Weather with EAGLE-I and NWS Datasets Understanding the Computing and Analysis Needs for Resiliency of Power Systems from Severe Weather Impacts Sensor Incipient Fault Impacts on Building Energy Performance: A Case Study on a Multi-Zone Commercial Building... High resolution dataset from a net-zero home that demonstrates zero-carbon living and transportation capacity... Analysis of Correlation between Cold Weather Meteorological Variables and Electricity Outages Real-time Multi-granular Analytics Framework for HIT Systems HPC Analytics of Fused Thermal Plants Data to Optimize Operating Envelope Incipient Sensor Fault Impacts on Building Performance Through HVAC Controls: A Pilot Study... Impacts of New Sensor Types for Selected Advanced Controls... COVID-19 Pandemic Ramifications on Residential Smart Homes Energy Use Load Profiles... Development of an Open-source Alloy selection and Lifetime assessment tool for structural components in CSP... Identification of Critical Infrastructure via PageRank... Efficient Contingency Analysis in Power Systems via Network Trigger Nodes... Exploiting user activeness for data retention in HPC systems... A machine learning approach to predict thermal expansion of complex oxides... DATA FUSION: A PROJECT UPDATE & PATHWAY FORWARD... Advanced Health Information Technology Analytic Framework and Application to Hazard Detection Data Analysis Approach for Large Data Volumes in a Connected Community... Toward Quantifying Vulnerabilities in Critical Infrastructure Systems Uncertainty Quantification of Machine Learning Predicted Creep Property of Alumina-Forming Austenitic Alloys... Pagination Current page 1 Page 2 Page 3 Next page ›› Last page Last » Key Links ORCID Organizations Computing and Computational Sciences Directorate Computer Science and Mathematics Division Mathematics in Computation Section Discrete Algorithms Group