Faculty Profiles

Dr. Kecheng Yang

Ph.D., Computer Science, University of North Carolina at Chapel Hill, 2018.

Yang, Kecheng (Principal). CAREER: Predictable Real-Time Computing in the Presence of Unpredictabilities, National Science Foundation (NSF), Federal, $587080. (Submitted: July 2024, Funded: July 2025 - June 2030). Grant.
Yang, Kecheng (Principal). Real-Time Scheduling Algorithms and Analysis for ROS2 Systems, Texas State University, Texas State University, $8000. (Submitted: October 2021, Funded: January 2022 - May 2023). Grant.
Yang, Kecheng (Principal). CRII: CNS: Supporting Mixed-Criticality Real-Time Systems on Heterogeneous Platforms, National Science Foundation (NSF), Federal, $175000. (Submitted: October 2020, Funded: July 1, 2021 - June 30, 2025). Grant.
Yang, Kecheng (Principal). Mixed-Criticality Scheduling in Compositional Real-Time Systems, Texas State University, Texas State University, $8000. (Submitted: October 2018, Funded: January 2019 - December 2020). Grant.
Yang, Kecheng (Principal), Ngu, Hee Hiong (Co-Principal). Supplement to REU Site: Research Experiences for Undergraduates in Edge Computing, National Science Foundation (NSF), Federal, $10000. (Submitted: November 2023, Funded: January 2024 - February 2025). Grant.

Baruah, S., Ratul, I. J., & Yang, K. (n.d.). Accuracy Anomalies in Classifier Cascades. In Proceedings of the 47th IEEE Real-Time Systems Symposium (RTSS) (pp. xxx–xxx). IEEE Computer Society Press.
Ratul, I. J., & Yang, K. (2026). Fast and Accurate Classification with Parallel IDK Classifier Cascades. In Proceedings of the 50th IEEE Annual Computers, Software, and Applications Conference (COMPSAC) (pp. 1496–1501). IEEE. https://doi.org/10.1109/COMPSAC69091.2026.00197
Ratul, I. J., Guo, Z., & Yang, K. (2026). Accuracy-Aware IDK Cascades for Real-Time Object Classification at the Edge. Journal of Systems Architecture (JSA), 175, 103753:1-10. https://doi.org/10.1016/j.sysarc.2026.103753
Ratul, I. J., Zhou, Y., & Yang, K. (2025). Accelerating Deep Learning Inference: A Comparative Analysis of Modern Acceleration Frameworks. Electronics, 14(15), 2977:1-2977:20. https://doi.org/10.3390/electronics14152977
Ratul, I. J., Guo, Z., & Yang, K. (2025). Cascading IDK Classifiers to Accelerate Object Recognition While Preserving Accuracy. In Proceedings of the 49th IEEE Annual Computers, Software, and Applications Conference (COMPSAC) (pp. 1522–1525). IEEE. https://doi.org/10.1109/COMPSAC65507.2025.00194

Award / Honor Recipient: CAREER Award, National Science Foundation (NSF). 2025
Award / Honor Recipient: CRII Award, National Science Foundation (NSF). 2021
Award / Honor Recipient: Outstanding Paper Award, 40th IEEE Real-Time Systems Symposium. 2019
Award / Honor Recipient: Best Student Paper Award, 40th IEEE Real-Time Systems Symposium. 2019
Award / Honor Recipient: Schloss Dagstuhl - NSF Support Grant for Junior Researchers, Dagstuhl Seminar 19101, Schloss Dagstuhl and NSF. 2019