Ruoxi Jia, who joined the Bradley Department of Electrical and Computer Engineering at Virginia Tech as assistant professor in 2020, is the newest faculty member at the Sanghani Center for Artificial Intelligence and Data Analytics.
Jia’s research interest broadly spans the areas of machine learning, security, privacy, and cyber-physical systems. Her recent work focuses on building algorithmic foundations for data markets and developing trustworthy machine learning solutions. Towards that end, she and her group work on a range of projects, including data valuation and quality management, data privacy, active data acquisition, adversarial machine learning, and explainable machine learning.
Jia is teaching a course on “Trustworthy Machine Learning” this semester and is looking for postdocs and Ph.D., master’s, and undergraduate students to join her group. Because of the limitations of personal contact due to COVID-19, she is happy to work with them remotely. (Interested students should click here for more information.)
“We extend a warm welcome to Ruoxi,” said Naren Ramakrishnan, the Thomas L. Phillips Professor of Engineering in the Department of Computer Science and director of the Sanghani Center. “Her work in privacy and security aspects of machine learning can complement a range of work happening at the center.”
“I am excited to join the Sanghani Center and look forward to collaborating with the other faculty members and students to push the frontiers of data science and unleash the power of data in a trustworthy, responsible way,” said Jia.
Jia earned a bachelor of science degree from Peking University in 2013 and a Ph.D. in electrical engineering and computer sciences from the University of California Berkeley in 2018.
She is the recipient of several fellowships, including the Chiang Fellowship for Graduate Scholars in Manufacturing and Engineering, the 8108 Alumni Fellowship, and the Okamatsu Fellowship. In 2017, she was selected for Rising Stars in EECS.
Prior to joining Virginia Tech she served as a postdoc in the Computer Science Department at University of California, Berkeley.
Her work has been published at professional conferences and featured in multiple media outlets, including MIT Technology Review, IEEE Spectrum, and Synced.