Xiaotian Duan is a research-focused machine learning engineer and Ph.D. candidate in Computer & Data Science at the University of Chicago with a decade of experience applying deep learning to computational chemistry and bioinformatics. Currently a Research Assistant and active postdoc researcher at Argonne National Laboratory, he works at the intersection of LLMs and AI4Science, translating advanced models into scientific discovery workflows. His background spans rigorous academic training (Ph.D. work, MS from RPI with a 4.0) and hands-on lab experience at national labs, enabling both theoretical contributions and reproducible engineering. Xiaotian is comfortable bridging systems-level thinking with domain-specific modeling, and he often focuses on turning complex scientific problems into tractable ML research projects. Colleagues describe him as someone who pairs strong quantitative foundations with a knack for making models practically useful in lab settings.
10 years of coding experience
Master of Science (M.S.), Computer and System Engineering, 4.0, Master of Science (M.S.), Computer and System Engineering, 4.0 at Rensselaer Polytechnic Institute
University of Illinois Urbana-Champaign
Doctor of Philosophy - PhD, Computer Systems and Machine Learning, 3.7, Doctor of Philosophy - PhD, Computer Systems and Machine Learning, 3.7 at University of Chicago
Contributions:57 commits, 46 pushes, 1 branch in 2 years 9 months
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Xiaotian Duan - Research Assistant at University of Chicago