About

I am a Ph.D. student in Computer Science and Engineering at the University of Texas at Arlington, advised by Dr. Junzhou Huang. My research lies at the intersection of trustworthy AI, correlation learning, and AI for science.

Research Interests

• Trustworthy and Interpretable AI: Explainability and uncertainty quantification in AI systems.
• Correlation Learning: Learning and exploiting label and feature correlations in modern machine learning.
• AI4Science: Applications of deep learning in healthcare (time-series, pathology data) and biology (genetic data).

Education

• Ph.D. in Computer Science and Engineering, University of Texas at Arlington (Expected 2028) — GPA: 4.0/4.0
• Computer Science, Rutgers University (2021–2023) — GPA: 4.0/4.0
• M.S. in Electrical Engineering, University of Southern California (2019–2021) — GPA: 3.94/4.0
• B.E. in Communication Engineering, University of Science and Technology Beijing (2015–2019) — GPA: 3.66/4.0

News

• 2026: Three papers accepted at NeurIPS 2026 (Poster): B2P-Corr, CytoWave, and Decomposed Representations for Multi-Omics.
• September 2026: Our paper on uncertainty-aware multimodal gait representation learning for scoliosis screening was accepted by IEEE Transactions on Medical Imaging (TMI).
• 2026: GAMI accepted at BIBM 2026.
• 2026: Received the ICML 2026 Gold Reviewer Award.
• 2026: Two papers accepted — one at ICLR 2026 (Poster) and one at AAAI 2026 (Oral)!
• 2025: GoBERT accepted at AAAI 2025 (Poster).
• 2023: Paper on self-interpretable time series prediction accepted as ICML 2023 Oral (top 8%).

Services

Gold Reviewer Award: ICML 2026

Reviewer: ICML, ICLR, NeurIPS, CVPR, AAAI, WACV, TMLR