Jeff Schueler’s Biography
Jeff Schueler is an experimental particle physicist specializing in improving particle detector performance through novel machine learning techniques. Born and raised in the Seattle area, he earned his Bachelor’s degrees in Mathematics and Physics at the University of Washington.
After graduating, Jeff spent two years teaching physics at a public high school in Queens through the New York City Teaching Fellows. Jeff then moved to Honolulu to pursue graduate studies at the University of Hawaiʻi at Mānoa, where he worked with Sven Vahsen investigating beam-induced neutron backgrounds Belle II experiment and gas Time Projection Chamber (TPC) research and development. In his third year, he lived onsite at KEK in Tsukuba, Japan, and led the commissioning efforts of systems of shoebox-sized TPCs that remain in use to this day.
In the final year of his PhD, Jeff spearheaded the application of deep learning to enhance particle identification and directional reconstruction in gas TPCs. He developed a custom ResNet architecture that led to the first demonstration of vector-directional head/tail reconstruction of sub-10 keV nuclear recoils on real data—an important milestone toward competitive directional dark matter detectors.
Jeff received his PhD in 2022 and joined the University of New Mexico in early 2023 as a postdoctoral researcher in Dinesh Loomba’s research group, working on the MIGDAL experiment. The MIGDAL experiment is a rare event search experiment which aims to observe and characterize the Migdal effect in nuclear scattering for the first time. Early in his tenure, Jeff conceived the idea of applying object detection as a way to employ machine learning training on real data for rare event search experiments in contexts where the rare event signal is a composite composed of commonly observed species. He has since successfully demonstrated this idea in the MIGDAL experiment through the development of an end-to-end YOLOv8-based pipeline that he trained on real data to automate the experiment’s search for the Migdal effect. This work and its accompanying software are published in PRD.
Jeff’s current research interests involve developing techniques to separate overlapping objects in scientific image data, narrowing sim-to-real gaps, and self-supervised learning techniques for data compression and multi-modal analyses. While his background is in particle physics, all of these techniques are interdisciplinary and Jeff is eager to collaborate and dip into other domains!
In his spare time, Jeff enjoys traveling (all background photos on this site were taken by him), crossword puzzles, running, reading, boardgames, PC building, retro video games, cooking, hiking, and rock climbing. He also enjoys tinkering with edge-devices, creating DIY projects like this keypoint detection-based exercise form tracker that he custom-trained and deployed on a Jetson Nano. Jeff is also a certified hydro homie and is rarely spotted without his water bottle.
