PNNL

Andrew Engel is a data scientist in the AI and Data Analytics Division of the National Security Directorate at Pacific Northwest National Laboratory (PNNL). His research focuses on scientific use cases for machine learning and artificial intelligence (AI) explainability. His research on photometric redshift algorithms has been applied to analyze the population of observed supernovas in a collaborative Young Supernova Experiment.

Engel’s current research interests focuses on how to fuse multiple astronomical survey sources together in one computer vision neural network, with the research efforts being funded by the National Aeronautics and Space Administration (NASA) astrophysical data analysis program. He is excited by the prospect of using AI explainability tools to probe behavior expected from domain knowledge in computer vision models. Engel’s remaining project portfolio has included studying AI to speed up the search for axion-like dark matter, kernel methods for training data attribution, and evaluating radio frequency (RF) device location algorithms.

Engel holds a BS in physics from the University of Illinois Urbana-Champaign, where he performed research on AI and astrophysics under Gautham Narayan. His research at PNNL would not be possible without guidance from his mentors: Tony Chiang, Christian Boutan, Eleanor Byler, Jan Strube, and Luke Gosink. 

• Artificial intelligence
• Astronomy
• Computer vision
• Machine learning
• Mathematical modeling
• Oscilloscope
• Physics
• Reinforcement learning
• Statistical modeling
• Visualization

BS in engineering physics, University of Illinois at Urbana-Champaign