I am a research scientist at the IBM Thomas J. Watson Research Center. Prior to that, I have been a research associate in the Department of Mathematics, Massachusetts Institute of Technology. I have graduated with a Ph.D degree from Department of Electrical and Computer Engineering at Johns Hopkins University. I got my Master degree at the same department and Bachelor degree in Electrical Engineering at Hanoi University of Science and Technology, Vietnam.
My research interest revolves around the theme of making accurate inference of high-dimensional data from noisy and corrupted observations. In particular, I am interested in the:
- development of statistical theory and computationally efficient methods for analyzing high-dimensional data with low intrinsic structure.
- development of distributed stochastic algorithms for sparse signal recovery.
- design and analysis of randomized numerical linear algebra algorithms
- Applying these techniques to solve problems in signal processing, machine learning, imaging... in which data are often large, corrupted or incomplete, and having certain low-dimensional structure.
Recently, I have been focusing more on deep learning.
My personal website is at: https://sites.google.com/site/namnguyenjhu/Home