Qian Yang
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Qian Yang 371 Fairfield Way, Unit 4155 Office: ITE 259 |
Bio
My research lies at the intersection of computational science and the physical sciences, with an emphasis on machine learning for materials, physics, and chemistry applications. I completed my Ph.D. from the Institute for Computational and Mathematical Engineering at Stanford University, and hold a B.A. in applied mathematics/computer science from Harvard College. Before joining UConn, I was a postdoctoral scholar in the Materials Computation and Theory group at Stanford University.
News
Jan 2026: Conference paper, DeltaSegment: Multifidelity Medical Image Segmentation via Change Detection, accepted at IEEE International Symposium on Biomedical Imaging (ISBI 2026).
Dec 2025: Congratulations to newly minted Dr. Graham Roberts and Dr. Yushuo Niu!
Nov 2025: Journal paper, MultiTaskDeltaNet: Change Detection-based Image Segmentation for operando ETEM with Application to Carbon Gasification Kinetics, accepted by Digital Discovery.
Oct 2025: Graduate student Nila Mandal presents her work, Graph Neural Networks for Melting Temperature Prediction of Molten Salt Eutectic Mixtures, at 7th Molecular Machine Learning Conference (MoML @ MIT 2025).
Check out our podcast exploring the development of machine learning for materials science, physics, and chemistry applications! Each episode features conversations with researchers at the forefront of this growing interdisciplinary field. In collaboration with the Stanford Materials Computation and Theory Group.
Currently available on iTunes, via RSS feed and here. Visit each episode's webpage for links to papers and additional materials.
Season 2 Episodes:
Professor Gábor Csányi (University of Cambridge)
Professor Anatole von Lilienfeld (University of Basel)
Dr. Patrick Riley (Google Accelerated Science)