EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
Research Staff Member
Jie Chen is a senior research scientist and manager at the MIT-IBM Watson AI Lab, IBM Research. He received the B.S. degree in mathematics with honors from Zhejiang University and the Ph.D. degree in computer science from the University of Minnesota. His research spans a broad spectrum of disciplines, including machine learning, statistics, scientific computing, and parallel processing, with results published in prestigious journals and conferences in the respective fields. His interests include graph-based deep learning, kernel methods, dimension reduction, Gaussian processes, matrix functions, preconditioning, graph partitioning, and tensor approximations. He directs research projects that integrate scientific merits with practical values in industry and business, covering sectors including finance, energy, and materials; and supported by various member companies of the lab as well as the U.S. Department of Energy. He was a recipient of SIAM Student Paper Prize in 2009, a plenary speaker at the 2017 International Conference on Preconditioning Techniques for Scientific and Industrial Applications, and a recipient of IBM Outstanding Technical Achievement Award in 2018.
Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics
Scalable Graph Learning for Anti-Money Laundering: A First Look
Publications with the MIT-IBM Watson AI Lab