The Amazon Fellows are (from left) Minsu Kim and Ying Shen. Photos courtesy of the subjects.
The Amazon–Virginia Tech Initiative for Efficient and Robust Machine Learning will support two Amazon Fellows and five innovative research projects led by Virginia Tech faculty in the 2023-24 academic year that further the initiative’s mission of advancing innovation in machine learning.
An open call for fellowship nominations and faculty projects went out across the Virginia Tech campuses. An advisory committee of Virginia Tech faculty and Amazon researchers selected two Amazon Fellows from 27 nominations — more than double what was received last year — and five faculty projects from 17 submitted proposals. Read full story here.
(From left) Reza Ghanadan, senior principal scientist, Amazon Alexa and the new Amazon center liaison for the Amazon-Virginia Tech initiative; Shehzad Mevawalla, vice president of Alexa Speech Recognition, Amazon Alexa; Virginia Tech President Tim Sands; Lance Collins, vice president and executive director, Innovation Campus; Julie Ross, the Paul and Dorothea Torgerson Dean of Engineering; Naren Ramakrishnan, the Thomas L. Phillips Professor of Engineering and director of the Amazon-Virginia Tech initiative; and Wanawsha Shalaby, program manager for the Amazon-Virginia Tech initiative. Photo by Lee Friesland for Virginia Tech.
Announced last year, the initiative — funded by Amazon, housed in the College of Engineering, and directed by researchers at the Sanghani Center for Artificial Intelligence and Data Analytics on Virginia Tech’s campus in Blacksburg and at the Innovation Campus in Alexandria — supports student- and faculty-led development and implementation of innovative approaches to robust machine learning, such as ensuring that algorithms and models are resistant to errors and adversaries, that could address worldwide industry-focused problems. Read full story here.
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 clickhere 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.
