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Tian obtained his B.Sc. and M.Sc. in Chemistry from Tsinghua University, followed by a Ph.D. in Chemical Engineering from ETH Zürich, where he was supervised by Prof. Chih-Jen Shih. His doctoral research focused on multiscale simulation of the engineering interfacial properties of two-dimensional materials. Between 2021 and 2023, he held a Swiss National Science Foundation Postdoc Mobility Fellowship for postdoctoral research at Carnegie Mellon University under Prof. Zachary W. Ulissi, specializing in machine-learning-assisted material simulations. Additionally, he briefly worked at the Georgia Institute of Technology on developing machine-learning-enabled density functional theory communication layers. Dr. Tian's research group at the University of Alberta develops machine learning-accelerated simulation methods for designing interfacial materials, with applications in energy storage systems, light-emitting polymers, and colloidal soft matter. His group combines physics-based modeling with data-driven learning to improve predictive materials design and enhances open-source computational tools that bridge computation and experiment. He is also actively involved in teaching, overseeing courses in Mass Transfer and Kinetics of Materials, while recruiting PhD and Master's students for upcoming research projects.
University of Alberta • Edmonton, AB
Research and teaching in the field of Chemical Engineering, with a focus on Machine Learning and Computational Materials.
Department: Mechanical Engineering and Engineering Management