Khanh Nguyen
TAMU CSE

    Khanh Nguyen

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I am an Assistant Professor in the Department of Computer Science and Engineering at Texas A&M University. I received my PhD in Computer Science from UCLA. My advisor was Prof. Guoqing (Harry) Xu. Prior to joining Texas A&M, I was a Visiting Researcher at Google.
My research aims to develop practical techniques to solve scalability issues in Big Data systems. Specifically, I am interested in developing programming language extensions, compiler and runtime supports to improve system performance including efficiency, scalability, and latency.

I am recruiting students who are interested in systems research. If you are a prospective student, please email me a brief description of your research interests, experience, and your CV.



Publications
  • Chenxi Wang, Haoran Ma, Shi Liu, Yuanqi Li, Zhenyuan Ruan, Khanh Nguyen, Michael Bond, Ravi Netravali, Miryung Kim, and Guoqing Harry Xu "Semeru: A Memory-Disaggregated Managed Runtime", the 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI), Banff, Alberta, Canada, November 4-6, 2020. (Acceptance rate: 70/398, 18%) [PDF] [Slides]
  • Christian Navasca, Cheng Cai, Khanh Nguyen, Brian Demsky, Shan Lu, Miryung Kim, and Guoqing Harry Xu. "Gerenuk: Thin Computation over Big Native Data Using Speculative Program Transformation", the 27th ACM Symposium on Operating Systems Principles (SOSP), Huntsville, Ontario, Canada, October 27-30, 2019. (Acceptance rate: 38/276, 14%) [PDF] [Slides]
  • Cheng Cai, Qirun Zhang, Zhiqiang Zuo, Khanh Nguyen, Guoqing Harry Xu, and Zhendong Su. "Calling-to-Reference Context Translation via Constraint-Guided CFL-Reachability", ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI), Philadelphia, PA, June 18-22, 2018. (Acceptance rate: 55/245, 22%) [PDF] [Slides]
  • Khanh Nguyen, Lu Fang, Christian Navasca, Guoqing Harry Xu, Brian Demsky, and Shan Lu. "Skyway: Connecting Managed Heaps in Distributed Big Data Systems", the 23rd International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), Williamsburg, VA, March 24-28, 2018. (Acceptance rate: 56/319, 18%) [PDF] [PDF Slides] [Slides]
  • Khanh Nguyen, Kai Wang, Yingyi Bu, Lu Fang, and Guoqing Harry Xu. "Understanding and Combating Memory Bloat in Managed Data-Intensive Systems", ACM Transactions on Software Engineering and Methodology (TOSEM), Volume 26 Issue 4, January 2018. [PDF]
  • Khanh Nguyen, Lu Fang, Guoqing Harry Xu, Brian Demsky, Shan Lu, Sanazsadat Alamian, and Onur Mutlu. "Yak: A High-Performance Big-Data-Friendly Garbage Collector", the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI), Savannah, GA, November 2-4, 2016. (Acceptance rate: 47/267, 18%) [PDF] [PDF Slides] [Slides]
  • Khanh Nguyen, Lu Fang, Guoqing Harry Xu, and Brian Demsky. "Speculative Region-based Memory Management for Big Data Systems", the 8th Workshop on Programming Languages and Operating Systems (PLOS), Monterey, CA, October 4, 2015. (Acceptance rate: 7/16, 44%) [PDF] [Slides]
  • Lu Fang, Khanh Nguyen, Guoqing Harry Xu, Brian Demsky, and Shan Lu. "Interruptible Tasks: Treating Memory Pressure As Interrupts for Highly Scalable Data-Parallel Programs", the 25th ACM Symposium on Operating Systems Principles (SOSP), Monterey, CA, October 4-7, 2015. (Acceptance rate: 30/186, 16%) [PDF] [1-column PDF] [Slides]
  • Khanh Nguyen, Kai Wang, Yingyi Bu, Lu Fang, Jianfei Hu, and Guoqing Harry Xu. "Facade: A Compiler and Runtime for (Almost) Object-Bounded Big Data Applications", the 20th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), Istanbul, Turkey, March 14-18, 2015. (Acceptance rate: 48/287, 17%) [PDF] [Slides]
  • Khanh Nguyen, and Guoqing Harry Xu. "Cachetor: Detecting Cacheable Data to Remove Bloat", Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering (ESEC/FSE), Saint Petersburg, Russia, August 18-26, 2013. (Acceptance rate: 51/251, 20%) [PDF] [Slides]
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