[jie chen's photo]Jie Chen

Research Staff Member
MIT-IBM Watson AI Lab, IBM Research
314 Main 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, training systems, 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?


Jie Chen is a research staff member and a 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 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.