Jie Chen

Research Staff Member

MIT-IBM Watson AI Lab,
IBM Research

75 Binney St, Cambridge, MA 02142, USA

Email: chenjie -AT- us.ibm.com

My research interests root in matrices, fundamental mathematical objects that are not numbers, but tables of numbers and also mappings between two universes. What I study are as deep as the theory and the computation, as wide as numerical analysis, scientific computing, and parallel processing, and as applied as statistics and machine learning. My work is heavily convoluted with data, because numerical and scalable computations play a crucial role there. A line of my efforts focuses on linear-complexity computations of large dense matrices defined by kernels. In practice, they entail the most common structure for matrices that are both large and dense. Linear complexity is the right, if not the only, way to match the theoretical appeals of matrix methods with the Moore's law progress of computer technology and the surge of data and information.

Graphs are siblings of matrices, encoding relational interactions within a complex system. Another line of my efforts focuses on graph-based deep learning, including generative modeling, structure learning, stochastic optimization, and further resurgent subjects propelled by the empirical success of deep learning. The gap between theory and practice of deep neural networks remains, not to mention the long journey toward artificial general intelligence, but innovations enabled by exploding computing power are never underestimated. Would quantum computing be the next?

- Ph.D. 2010, Computer Science, University of Minnesota.
- B.S. 2004, Mathematics, Chu Kochen Honors College, Zhejiang University.

Jie Chen is a Research Staff Member at MIT-IBM Watson AI Lab, IBM Research. He received his B.S. degree in mathematics from Zhejiang University and Ph.D. degree in computer science from University of Minnesota. His research spans a broad spectrum of disciplines, including machine learning (deep learning, kernel methods, and dimension reduction), statistics (Gaussian processes), scientific computing (matrix functions, preconditioning, graph partitioning, and tensor approximations), and parallel computing. The results of his work have been published in prestigious journals and conferences in the respective fields. 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. He has been PI/co-PIs of projects supported by the U.S. Department of Energy.