ECE postdoctoral scholar Chunfeng Cui selected to participate in the 2019 Rising Stars in Computational and Data Sciences workshop

February 15th, 2019

photo of Chunfeng CuiCui selected as one of 32 women from top-tier U.S. universities to participate in the workshop held at UT Austin’s Institute for Computational Engineering and Sciences (ICES)

Rising Stars is an academic and research career workshop for women graduate students and postdocs who are interested in pursuing academic and research careers. Originally launched at MIT in 2012, Rising Stars events have been hosted in many different fields at institutions across the world. The first Rising Stars event in Computational and Data Sciences will bring the selected researchers together to network and for presentations, poster sessions and interactive discussions.

Chunfeng Cui’s research activities are mainly focused in the areas of tensor computing, uncertainty quantification, machine learning, and their interface. She has been working on tensor data analysis by convex and non-convex optimization, high-dimensional uncertainty quantification with non-Gaussian correlations for electronic and photonics IC, and theoretical structural analysis of deep learning. She is the recipient of the 2018 Best Paper Award of IEEE Electrical Performance of Electronic Packaging and Systems (EPEPS) and the Best Journal Paper Award of Scientia Sinica Mathematica.

Cui received her Ph.D. degree in computational mathematics from the Chinese Academy of Sciences, Beijing, China in 2016 with a specialization in numerical optimization for tensor data analysis. From 2016 to 2017, she was a Postdoctoral Fellow at City University of Hong Kong, Hong Kong. In 2017, she joined Professor Zheng Zhang’s group as a Postdoctoral Scholar in UC, Santa Barbara’s Electrical and Computer Engineering Department.

The 2019 Rising Stars in Computational and Data Sciences workshop will be held from April 9 to April 10, 2019 and is hosted by UT Austin ICES and Sandia National Laboratories.

2019 Rising Stars in Computational and Data Sciences

The Zhang Group