University of California, Santa Barbara
Chunfeng Cui is a Postdoctoral Scholar at UC Santa Barbara, CA, USA. She received her Ph.D. degree in computational mathematics from Chinese Academy of Sciences, Beijing, China in 2016. From 2016 to 2017, she was a Postdoctoral Fellow at City University of Hong Kong, Hong Kong. Her research lies in the general areas of EECS, with a particular interest in tensor computations, uncertainty quantification, and machine learning. She was the recipient of the 2018 Best Paper Award of IEEE EPEPS and the 2018 Best Journal Paper Award of Scientia Sinica Mathematica. In 2019, she was selected as one of the 32 “Rising Stars in Computational and Data Sciences” in the United States jointly by UT Austin and Sandia National Labs.
Due to the wide deployment of social networks and mobile devices, massive data is generated on the internet every second. Such big-data resources open new opportunities for artificial intelligence and machine learning, but meanwhile they also cause new challenges in data storage, representation, and computation. On the other hand, in many engineering designs obtaining data samples by measurement or numerical simulation is expensive, therefore people have to verify and optimize complex engineering designs with limited small data sets.
I have been working on computational techniques to address the challenges in both small- and big-data problems. Firstly, I have proposed a global optimization method for solving the non-convex tensor eigenvalue problem. Then, I have proposed a new uncertainty quantification framework with theoretical performance guarantees even if the input uncertainties are non-Gaussian correlated. Finally, I have studied the structural framework of DNN.