Profiles
Tong (Tony) Geng
Assistant Professor of Electrical and Computer Engineering and Computer Science
- Rochester NY UNITED STATES
- Computer Studies Building 723
- Department of Electrical Engineering and Computer Science
Geng is an expert in artificial intelligence foundations and applications in many fields
Areas of Expertise
Social
Links
Biography
Dr. Tony Geng is a tenure-track assistant professor in the departments of Electrical and Computer Engineering and Computer Science, and has a secondary appointment in the Goergen Institute for Data Science and Artificial Intelligence. He is also the director of the university's IntelliArch Lab.
His research interests are at the intersection of computer architecture and systems, machine learning, graph learning, artificial intelligence (AI) acceleration, and high-performance computing. Tony has published more than 50 papers, many of which have appeared in prestigious conferences and journals.
Education
Boston University
PhD
Computer Engineering
Eindhoven University of Technology
MS
Electronic Systems
Zhejiang University
BE
Electronic Engineering and Information Technology
Selected Media Appearances
Researchers developing tool to instantly conceal and anonymize voices
University of Rochester online
2024-12-05
The voice-changer system will produce computer-generated speech within milliseconds, allowing users to control factors like age, gender, and dialect. “In the end, a 30-year-old woman from Texas will be able to instantaneously transform her voice to be output by the virtual speaker to sound like a 50-year-old man with a British accent, for example, without producing artifacts that can be traced back to the identity of the user,” says Zhiyao Duan.
Audio deepfake detective developing new sleuthing techniques
University of Rochester
2023-11-09
A National Institute of Justice fellowship allows a Rochester graduate student to develop novel defenses against deepfake scams under the guidance of Zhiyao Duan.
Selected Event Appearances
Ising-Traffic: Using Ising Machine Learning to Predict Traffic Congestion under Uncertainty
Proceedings of the AAAI Conference on Artificial Intelligence
