About

I am Hailiang (Leon) Dong, currently a Ph.D. student in Computer Science at the University of Texas - Dallas (graduating Dec.2023), working as a research assistant at Center for Machine Learning - PGM&ML Lab. My research mainly focus on the development of machine learning models, specifically the tractable probabilistic graphical models with continuous random variables in temporal domain.

I am broadly interested in artificial intelligence and optimization problems, and is equipped with strong skills in deep learning, machine learning, troubleshooting, data structures and algorithms. I am inquisitive person and is often curious of how things work under the surface. I am a fast-learner, and work in an independent, self-driven style. To learn more about my professional experience and skills, you are welcomed to visit my LinkedIn profile.

Contact

Email: LeonDong1993@gmail.com
Alternative Email: LeonDong1993@qq.com
WeChat: HiLeonDong

Education

• University of Texas at Dallas (Doctorate), Computer Science, 2018.08 - 2023.12
  Dissertation: Learning Tractable Probabilistic Graphical Models in Continuous Temporal Domains
  Advisor: Dr. Nicholas Ruozzi

• Chongqing University (Master), Computer Science, 2015.09 - 2018.06
  Thesis: Optimization and Design of High Reliability Parallel Heterogeneous Multi-Core Systems
  Advisor: Dr. Yujuan Tan and Dr. Edwin Sha

• Chongqing University (Bachelor), Information and Computing Science (under the Department of Mathematics and Statistics), 2011.09 - 2015.06
  Thesis: Image LBP Feature Extraction Algorithm and its Application in Texture Classification
  Advisor: Dr. Bin Xu

Publications

The complete list of publications can be viewed at my Google Scholar profile.

Ph.D. at UT - Dallas

• Hailiang Dong, James Amato, Vibhav Gogate, and Nicholas Ruozzi. Learning Distributionally Robust Tractable Probabilistic Models in Continuous Domains, Conference Under Review.

• Hailiang Dong, James Amato, Vibhav Gogate, and Nicholas Ruozzi. A New Modeling Framework for Continuous, Sequential Domains, In International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 206:11118-11131, 2023.

• Hailiang Dong, Chiradeep Roy, Tahrima Rahman, Vibhav Gogate, and Nicholas Ruozzi. Conditionally Tractable Density Estimation using Neural Networks, In International Conference on Artificial Intelligence and Statistics (AISTATS), pp. 6933-6946. PMLR, 2022.

• Roy, Chiradeep, Tahrima Rahman, Hailiang Dong, Nicholas Ruozzi, and Vibhav Gogate. Dynamic Cutset Networks, In International Conference on Artificial Intelligence and Statistics (AISTATS), pp. 3106-3114. PMLR, 2021.

• Moore, Alec G., Ryan P. McMahan, Hailiang Dong, and Nicholas Ruozzi. Extracting Velocity-Based User-Tracking Features to Predict Learning Gains in a Virtual Reality Training Application, In 2020 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pp. 694-703. IEEE, 2020.

Master at CQU

• Jiang, Weiwen, Edwin H-M. Sha, Qingfeng Zhuge, Hailiang Dong, and Xianzhang Chen. Optimal Functional Unit Assignment and Voltage Selection for Pipelined MPSoC with Guaranteed Probability on Time Performance, ACM SIGPLAN Notices 52, no. 5 (2017): 41-50.

• Sha, Edwin, Hailiang Dong, Weiwen Jiang, Qingfeng Zhuge, Xianzhang Chen, and Lei Yang. On the Design of Reliable Heterogeneous Systems via Checkpoint Placement and Core Assignment, In Proceedings of the 2018 on Great Lakes Symposium on VLSI, pp. 475-478. 2018.

• Hailiang Dong, Edwin H-M. Sha, Weiwen Jiang, Xianzhang Chen, Runyu Zhang, and Qingfeng Zhuge. Towards the Design of Optimal Range Assignment for Elevator Groups under Fluctuate Traffic Loads, In 2017 IEEE 23rd International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA), pp. 1-6. IEEE, 2017.

• Sha, Edwin Hsing-Mean, Weiwen Jiang, Hailiang Dong, Zhulin Ma, Runyu Zhang, Xianzhang Chen, and Qingfeng Zhuge. Towards the Design of Efficient and Consistent Index Structure with Minimal Write Activities for Non-Volatile Memory, IEEE Transactions on Computers 67, no. 3 (2017): 432-448.

Teaching Assistantship

UT - Dallas

• Spring 2020
  CS6375 - Machine Learning
  CS4365 - Artificial Intelligence

• Fall 2020
  CS6314 - Web Programming Languages
  CS6303 - Cyber Security Essentials for Practitioners

• Fall 2019
  CS4375 - Introduction to Machine Learning

• Spring 2019
  CS4347 - Database Systems

Professional Experience

• Machine Learning Scientist Intern, Search & Recommendation @ Wayfair, 2023.06 - 2023.08

• Back-End Engineer Intern, Large Scale Live Streaming @ Tencent, 2017.06 - 2017.08

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