NSF REU Class of 2025
Molly Abramson
Molly is a rising senior at CMU studying astrophysics. Molly has previous research experience in astrophysics and cosmology studying primordial magnetic fields. Through this REU, Molly worked with Professor Hy Trac studying galaxy cluster masses using Convolutional Neural Networks. She trained the neural network using Uchuu simulation data to return both a mass estimate and an uncertainty on that estimate, and applied the trained network to infer the mass of the galaxy cluster Abell 209. Molly is also interested in theoretical neuroscience, and has studied biological neural networks in the Biophysics REU at University of Michigan, Ann Arbor. Outside of academics, Molly enjoys playing hockey and the company of her cat.
Janna Goodman
Janna Goodman is a rising junior at Cornell University where she studies physics and math. She has research experience in accelerator physics and particle physics. She believes that experimental particle physics is a prime example of a field that can be substantially improved with advancements in machine learning. At CMU, she works with Professor Matteo Cremonesi and Dr. Peter Meiring to use machine learning for real time analysis of data from particle detectors. This will inform the development of future particle accelerators like the Future Circular Collider (FCC). She is excited to explore the utility of new technologies in physics research, and gain the tools and experience to integrate AI into her research in the future.
This material is based upon work supported by the National Science Foundation under the NSF AI Planning Institute for Data-Driven Discovery in Physics, grant number PHY2020295. Any opinions, findings, and conclusions or recommendations expressed on this website are those of the participants and do not necessarily reflect the views of the National Science Foundation or the participating institutions.
