PhD Candidate
University of Chicago
Areas of Interest
- Artificial Intelligence
- Software engineering
Poster
Accurate Anytime Learning for Energy and Timeliness in Software Systems
Abstract
This project focus on disciplined methods for dynamically managing application-specific latency, accuracy, and energy requirements for software systems incorporating DNNs. We create robust methods to configure both DNNs and system resources to satisfy differing requirements and goals across a variety of users and applications.
Bio
Chengcheng Wan is a fourth-year Ph.D. student in the Department of Computer Science at the University of Chicago, advised by Prof. Shan Lu. Her research focus on software engineering and system support for machine learning in real world applications. She has worked on the proposal of advanced DNN systems for helping developers satisfy differing requirements and goals across a variety of users and applications. Her research has lead to papers published at top conferences (ICSE'19, ATC’20, ICML’20). She received her Bachelor’s degree from Shanghai Jiao Tong University in 2017.
