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Yu Guang Wang is an Adjunct Associate Professor at the School of Mathematics and Statistics, University of New South Wales (UNSW). His research interests primarily lie in artificial intelligence, computational mathematics, and data science. He specializes in areas such as geometric deep learning, graph neural networks, applied harmonic analysis, Bayesian inference, information geometry, and numerical analysis, with applications in biomedicine and protein design. He has previously worked as a research scientist at the Max Planck Institute for Mathematics in the Sciences within Prof. Guido Montufar's Deep Learning Theory Group. Yu obtained his PhD in applied mathematics from the University of New South Wales, supervised by Prof. Ian Sloan and Rob Womersley. He has received various accolades including the ICERM Semester Postdoctoral Fellowship at Brown University in 2018, and has been a long-term visitor at both UCLA and the University of Cambridge's AI Group in subsequent years.
University of New South Wales • Sydney
Adjunct Associate Professor at the School of Mathematics and Statistics, focusing on research in artificial intelligence and computational mathematics.
Includes Business Intelligence, Enterprise Systems, and Cybersecurity Management streams.