News

ECE postdocs Chunfeng Cui and Hongwei Zhao invited to participate in 2019 Rising Stars in EECS Workshop

August 13th, 2019

photos of Zhao and Cui
ECE postdoctoral researchers are identified among the most promising women in their field

UC Santa Barbara’s Chunfeng Cui and Hongwei Zhao are among roughly 70 women nationwide invited to participate in the 2019 Rising Stars in Electrical Engineering and Computer Science (EECS) Workshop hosted by the University of Illinois at Urbana Champaign. Previously held at MIT, Carnegie Mellon, Stanford, and UC Berkeley, the Rising Stars of EECS seeks the brightest and most promising women in the field during the early stages of their academic careers.

“It will be a great opportunity to learn from the best in academia and connect with other up-and-coming women,” said Zhao, who defended her PhD in electrical and computer engineering (ECE) at UCSB in June 2019.

Zhao will soon begin a postdoctoral research position at UCSB for her PhD advisor, Jonathan Klamkin, an associate professor of electrical and computer engineering. Prior to UCSB, Zhao received her master’s degree from the Institute of Semiconductors, Chinese Academy of Sciences and completed her undergraduate studies in electronics at Huazhong University of Science and Technology.

The annual workshop unites women who are interested in pursuing academic careers in computer science, computer engineering, and electrical engineering. Participants will present their research, interact with faculty from top-tier universities, and receive advice for advancing their careers.

“I could not be more grateful or happy to be invited,” said Cui, who is a postdoctoral researcher at UCSB for Zheng Zhang, a professor in the ECE Department. “I look forward to meeting my academic peers and sharing our experiences as female researchers.”

Cui received her PhD in computational mathematics with a specialization in numerical optimization for tensor data analysis from the Chinese Academy of the Sciences. Cui’s research spans two main areas: uncertainty quantification for electronic and photonic design automation; and tensor methods for machine learning. A tensor is a mathematical object that generalizes multi-dimensional data in the context of machine learning.

COE News – “Rising Stars” (full article)

Rising Stars 2019 in EECS

Cui's Uncertainty- and Data-Driven Computing Laboratory Bio

Zhao's Integrated Photonics Laboratory (iPL) Bio