Yu David Liu

My research interest is software systems. I am particularly drawn to solutions that streamline the interface between the application and the underlying system, and address cross-cutting concerns such as reliability, energy efficiency, performance, and security. These solutions may result from novel designs of runtime systems, compilers, programming languages, and software frameworks.

I received my Ph.D. from the Johns Hopkins University, advised by Scott Smith. I was a recipient of the NSF CAREER Award in 2010, the Google Faculty Research Award in 2011, the Outstanding Research Achievement Award from the Department of Computer Science at SUNY Binghamton in 2018, and from Watson School of Engineering and Applied Science in 2019. My research is currently supported by the National Science Foundation, currently through awards 1815949, 1823260, 1910532, 2053391, and 2215016.

Current Projects

(credit: M. Cohen)		Energy-Efficient and Energy-Aware Applications From data centers to smart objects, energy efficiency is a critical design consideration of modern computing. Read about Vincent (ECOOP'22), Eflect (ICSE'22), Chappie (FSE'20) and Ent (PLDI'17).
(credit: hypepotamus.com)		Optimizing Data-Intensive Software Data analytics applications are on the rise. Correct and efficient optimization is critical for their design. Read about DON Calculus (OOPSLA'22).
(credit: binghamton.edu)		Unmanned Aerial Vehicles UAVs are an emerging computing platform increasingly becoming common in our society. Read about safety-critical concerns in open-source UAV auto-pilots (ICSE'21), and Java-based UAV autopilot JCopter (IROS'21).
(credit: pixabay.com)		Thwarting Side-Channel Attacks Side channels pose a new threat to future software/hardware systems. Read about Composable Cachelets (USENIX Security'22).

Recent Work

• Philip Dexter, Yu David Liu, Kenneth Chiu, The Essence of Online Data Processing, OOPSLA, 2022.
• Anshul Gandhi, Kanad Ghose, Kartik Gopalan, Syed Rafiul Hussain, Dongyoon Lee, Yu David Liu, Zhenhua Liu, Patrick McDaniel, Shuai Mu, Erez Zadok, Metrics for Sustainability in Data Centers, HotCarbon, 2022.
• Kenan Liu, Khaled Mahmoud, Joonhwan Yoo, Yu David Liu, Vincent: Green Hot Methods in the JVM, ECOOP, 2022.
• Daniel Townley, Kerem Arikan, Yu David Liu, Dmitry Ponomarev, Oguz Ergin, Composable Cachelets: Protecting Enclaves from Cache Side-Channel Attacks, USENIX Security, 2022.
• Timur Babakol, Anthony Canino, Yu David Liu, Eflect: Porting Energy-Aware Applications to Shared Environments, ICSE, 2022.
• Sofiya Semenova, Steven Y. Ko, Yu David Liu, Lukas Ziarek, Karthik Dantu, A Quantitative Analysis of System Bottlenecks in Visual SLAM, HotMobile, 2022.
• Adam Czerniejewski, John Henry Burns, Farshad Ghanei, Karthik Dantu, Yu David Liu, Lukasz Ziarek, " JCopter: Reliable UAV Software through Managed Languages," International Conference on Intelligent Robots and Systems (IROS), 2021.
• Xiaozhou Liang, John Henry Burns, Joseph Sanchez, Karthik Dantu, Lukasz Ziarek, Yu David Liu, "Understanding Bounding Functions in Safety-Critical UAV Software," ICSE, 2021.
• Earlier...

Team

• Kerem Arikan (Ph.D.), co-advised with Dmitry Ponomarev
• Timur Babakol (Ph.D.)
• Jack Burns (Ph.D.).
• Congratulations to Crystal Low, who received her M.S. in Spring 2022.
• Jack E Mcmahon (B.S.)
• Zain Nasir (B.S.)
• Dylan Navy (M.S.)
• Congratulations to Joseph Raskind, who received his M.S. in Spring 2022. Joe will continue as a Ph.D. student.
• Congratulations to Alex Strong, who received his M.S. in Spring 2022.
• Joonhwan Yoo (B.S.)
• Alumni

Teaching

• CS476/576: Programming Models for Emerging Platforms (Fall 2021)
• CS571: Programming Languages, Section I and II (Spring 2022)

Events

• PC Member, PLDI 2023
• PC Member, ICCQ 2023
• RC Member, Programming 2023
• PC Member, COP 2022
• PC Member, MPLR 2022
• Special Issue Editor, JOT
• Associate Editor, ICRA 2022
• Associate Editor, J. Computer Languages
• Co-Chair, COP'21
• Steering Committee Member, VMIL
• Past Events

Fun

• Alan Turing is a "great-grand-uncle'' of mine on the academic genealogy tree.