I am a third-year Ph.D. student in Electrical and Computer Engineering at Case Western Reserve University. My research focuses on scalable machine learning methods (e.g., self-supervised learning, continual learning), efficient on-device learning, and large-scale model applications in time-series.
On-Board Continual Learning
Designed a custom edge-device board integrating sensors and a microprocessor for real-time IoT monitoring. The system supports optimized on-board machine learning model training and adaptation, enabling continual learning directly at the edge level without full cloud dependency.
Multi-Level Contrastive Learning
Developed multi-level contrastive learning methods (MOCO and DINO variants) for GM's large-scale welding image dataset. The proposed multi-scale paradigm improved representation quality and transfer performance across defect classification tasks.
"Harnessing unlabeled plant data and labeled lab data for enhanced quality prediction in laser welding"
Journal of Manufacturing Processes, 157, pp. 1015–1034.
"Unleashing the Power of Unlabeled Plant Data: A Hierarchical Contrastive Learning Framework for Dynamic Manufacturing Process Monitoring"
Journal of Manufacturing Systems, 83, pp. 483–493.
"Applicable and Generalizable Machine Learning for Intelligent Welding in Automotive Manufacturing"
Welding in the World, pp. 1–36.
"Transfer Learning-Enhanced Transformer for Virtual Process Sensing in Resistance Spot Welding"
Manufacturing Letters, 45, pp. 13–16.
"A Deployable Edge Computing Solution for Machine Condition Monitoring"
2024 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). Glasgow, United Kingdom, pp. 1–6.
"Efficient and Generalizable Machine Learning for Inline Defect Detection in Battery Laser Welding"
International Conference on Precision Engineering. Sendai, Japan.
Case Western Reserve University
2024 – Present
University of Kentucky
2021 – 2024
Beijing University of Technology
2019 – 2023