Youping Sun
Youping Sun
孙佑平
MS Computer Engineering
Northwestern University

I am an MS student in Computer Engineering at Northwestern University, which I began in September 2026. I received my bachelor's degree in Robotics Engineering from Shandong University, College of Control Science and Engineering, in June 2026. I am passionate about the intersection of machine learning, control systems, and human–robot interaction.

My research focuses on machine learning-based human gait analysis — specifically, detecting and predicting gait phases from surface electromyography (sEMG) signals using artificial neural networks. I have published three papers at international conferences and received national and provincial awards in robotics competitions.

I spent Fall 2025 as an exchange student at the University of Wisconsin–Madison (GPA: 4.0 / 4.0), where I worked in the Flexible Electronics & Additive Printing Lab on electrohydrodynamic (EHD) printing.

Education

Sep 2026 – May 2028 (Expected)
Northwestern University
MS Computer Engineering · Evanston, IL
Sep 2025 – Dec 2025
University of Wisconsin–Madison
Exchange Student · Industrial & Systems Engineering · Madison, WI
GPA 4.0 / 4.0
Sep 2022 – Jun 2026
Shandong University
BS Robotics Engineering · College of Control Science and Engineering · Jinan, China
GPA 84.74 / 100

Research Experience

Oct 2024 – Sep 2026
Robotics Center, Shandong University
Research Assistant · Prof. Zhang Huanghe (张黄河)
Gait Phase Detection & Prediction with Machine Learning on sEMG Signals
  • Analyzed lower-limb surface EMG signals and built a complete gait phase detection and prediction data processing pipeline.
  • Cleaned and preprocessed raw EMG data, extracted features, and trained ANN models for multi-class gait phase classification.
  • Investigated and significantly improved model generalization across different locomotion patterns (level walking and stair ambulation).
Sep 2025 – Dec 2025
Flexible Electronics & Additive Printing Lab, UW–Madison
Research Assistant · Prof. Hantang Qin
EHD Printing — 3D Model Building, Scanning & Pattern Projection
  • Designed continuously curving 3D models in SolidWorks and completed high-precision 3D scanning at 0.10 mm accuracy.
  • Mapped 2D patterns onto irregular 3D surfaces in Blender; resampled machining trajectories to maintain 1 mm point spacing for precise EHD-printing control.

News

Publications

2026
Exploring the Correlation Between Level Walking and Stair Ambulation for Fine-Grained Gait Phase Prediction
Y. Sun, X. Li, C. Ma, X. Ma, and H. Zhang
2026 IEEE 20th International Conference on Control and Automation (ICCA), pp. 1742–1747
Cross-Locomotion Task Generalization in sEMG-Based Gait Phase Prediction Using Artificial Neural Networks
Y. Sun, H. Wan, X. Li, F. Zhang, W. Wu, and H. Zhang
2026 10th International Conference on Artificial Intelligence, Automation and Control Technologies (AIACT), pp. 77–81
2024
Gait Phase Detection and Prediction with Machine Learning Models Based on sEMG
Y. Sun, J. Wang, Z. Wang, J. Li, Y. Li, R. Song, and H. Zhang
2024 3rd International Conference on Artificial Intelligence, Human-Computer Interaction and Robotics (AIHCIR), pp. 324–330

Projects

Humanoid Robot Control for Warehouse Scenarios
19th "Challenge Cup" AI+ Special Competition  ·  Jul 2025 – Nov 2025
★ National 2nd Prize
Built a Docker-based simulation environment and developed upper-level control algorithms enabling a humanoid robot's dexterous hand to perform object grasping and mobile manipulation planning for warehouse logistics tasks.
Intelligent Quadruped Robot — Webots Simulation & Motion Control
Intelligent Robotics Course Practice  ·  Feb 2025 – Mar 2025
Implemented stair-climbing pose transformation and motion control in an Ubuntu + Webots environment. Designed LiDAR- and camera-fused line-tracking and obstacle avoidance algorithms achieving high-speed stable navigation.
Visual Recognition & Vehicle Motion Control Algorithm
19th National Intelligent Vehicle Competition · iFLYTEK Track  ·  Sep 2023 – Aug 2024
★ Provincial 1st Prize
Developed OpenCV-based track recognition with curve-type classification and adaptive steering control. Integrated ROS with PID control algorithms to achieve high-speed, stable navigation across varied track configurations.

Awards & Honors

Skills

Programming
Python, C/C++, R, MATLAB, Wolfram
Machine Learning & Signal Processing
PyTorch, ANN, CNN, SVM, KNN, sEMG signal processing & feature extraction
Tools & Frameworks
ROS, OpenCV, Webots, Docker, SolidWorks, Blender, COMSOL Multiphysics, Tableau

Contact

📍Northwestern University, Evanston, IL, USA