Research Assistant (RA) positions available for self-motivated PhD students. I am passionate about designing and implementing timing-predictable, efficient, and safe autonomous embedded systems, with autonomous driving as the currently focused application domain. Recent research focus of our research group is on attacking/detecting/mitigating vulerabilities in DNN-driven autonomous driving systems and performing system-level optimization in such systems.
I am interested in working with self-motivated students who strive for success through research. In the past, students in our group interned at top research labs (e.g., IBM T.J. Watson and Fujitsu US Research), and had excellent academic and industry job placements. If you are interested in my research and working with me, please include some samples of your work (e.g., major projects, open source contributions, etc.) when contacting me directly.
For current UTD students who are interested in working with me, please stop by my office so that we can discuss and see if there is a good match.
» Thanks to NVIDIA for a gift including the Drive PX2 SoC, which provides a very timely hardware support for our ongoing research on enhancing the predictability and robustness of autonomous driving systems!
» Our paper titled "PIFA: An Intelligent Phase Identification and Frequency Adjustment Framework for Time-Sensitive Mobile Computing" has been accepted to the 25th RTAS (RTAS 2019)
» I have graduated my second PhD student, Husheng Zhou. Congratulations, Husheng!
» Our paper titled "Diagnosing Vehicles with Automotive Batteries" has been accepted to the 25th MobiCom (MobiCom 2019)
» Two papers have been accepted to the 39th RTSS (RTSS 2018): ApNet (exploring approximation potential of DNN for timing predictability), and PredJoule (a timing-predictable energy optimization framework for DNN-driven autonomous systems). Congrats to Soroush Bateni for getting two first-authored papers accetped by RTSS 2018!
» Our paper ''DeepRoad: GAN-based Metamorphic Testing and Input Validation Framework for Autonomous Driving Systems'' has been accepted to the 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE 2018)
» I have graduated my first PhD student (co-advised with Dr. Ding-Zhu Du), Guangmo (Amo) Tong, who will join the University of Delaware as a tenure-track assistant professor. Congratulations, Amo!
» Our paper "S^3DNN: Supervised Streaming and Scheduling for GPU-accelerated Real-Time DNN Workloads" received the Best Paper Award at RTAS 2018 ([email protected]). Congrats to Husheng and Soroush!
» Our paper on "Exploring Computation and Data Redundancy via Partial GPU Computing Result Reuse" has been accepted to the 32nd ACM International Conference on Supercomputing (ICS 2018).
» I received the NSF CAREER award. Thanks to NSF for support!
» Our papers on "predictable processing of GPU-accelerated DNN workloads" and "shared-resource-centric multicore scheduling" have been accepted to the 24th RTAS (RTAS 2018).
» Our paper titled "Analysis techniques for supporting hard real-time sporadic gang task systems" has been accepted to the 38th RTSS (RTSS 2017). This paper received the Outstanding Paper Award! Congrats to Zheng!
» Our paper titled "Rec: Predictable charging scheduling for electric taxi fleets" has been accepted to the 38th RTSS (RTSS 2017).
» Our paper titled "ATHOME: Automatic Tunable Wireless Charging for Smart Homes" has been accepted to the 2nd IoTDI (IoTDI 2017).
» Served on the External Review Committee at ASPLOS 2018.
» Served on the External Review Committee at ASPLOS 2017.
» Served on the TPC at RTSS 2017.
I am an Associate Professor in the Department of Computer Science at the University of Texas at Dallas. I received my Ph.D. in Computer Science from the University of North Carolina at Chapel Hill with Prof. Jim Anderson in 2013.
Current Focused Research Areas:
- Physical-World Attacking/Detecting/Mitigating Vulnerabilities in DNN-driven Autonomous Embedded Systems
- Predictable GPGPU Computing
- Real-Time Systems
- System-level Optimization in DNN-driven Autonomous Driving