About Me

I am a Ph.D. candidate in the Department of Electrical and Computer Engineering at Princeton University, and I am very fortunate to be advised by Prof. Sun-Yuan Kung. My research focuses on machine learning, particularly in model compression and efficient fine-tuning of large language models. Additionally, I have been awarded the Gordon Wu Fellowship from Princeton University.

Education

Ph.D. in Electrical and Computer Engineering, Princeton University (Expected: 2027)

M.A. in Electrical and Computer Engineering, Princeton University (2024)

B.S. in Computer Information Technology, Purdue University (2022)

Graduated with highest distinction (GPA: 3.96 / 4.0)

Publications

[C]: Conference, [J]: Journal

• [C] David J. Richter, Lance Natonski, Xiaxin Shen, and Ricardo A. Calix. “Attitude control for fixed-wing aircraft using q-learning”, 2021 Springer International Conference on Intelligent Human Computer Interaction (IHCI). Kent, OH, United States, December 20-22, 2021.
• [C] Upinder Kaur, Haozhe Zhou, Xiaxin Shen, Byung-Cheol Min, and Richard M. Voyles. “Robomal: Malware detection for robot network systems”, 2021 IEEE International Conference on Robotic Computing (IRC). Taichung, Taiwan, November 15-17, 2021.

Assistant in Instruction (AI) Experience

• ECE 115 Introduction to Computing: Programming Autonomous Vehicles
• EGR 154 Foundations of Engineering: Linear Systems

Awards

• Gordon Y.S. Wu Fellowship in Engineering - 2022
• Best Session Paper Award in 2021 Springer IHCI
  • Session Name: Machine Learning for HCI
  • Paper Title: Attitude Control for Fixed-Wing Aircraft using Q-Learning

• 3rd place in the SAE (Society of Automotive Engineers) Mobility Forward Challenge: AI Mini-Challenge Competition - 2021

• Award for Best Visualization in Purdue’s 7th Annual ASA DataFest Competition - 2021

• Finalist in the Illinois Technology Association (ITA) Tech Challenge Programming Competition - 2019