Research
Research Agenda (2026–2030)
- Build clinically reliable AI systems that integrate imaging, EHR, and emerging biomedical modalities for decision support and translational impact.
- Develop controllable multimodal generative models as compositional tools for simulation, synthesis, and hypothesis-driven data generation.
- Advance multimodal perception and understanding in dynamic environments, with emphasis on temporal robustness and scalable deployment.
I view these three tracks as one connected agenda: stronger perception improves controllability, and controllable models accelerate translational biomedical applications.
Translational Biomedical AI
I focus on translational AI systems that bridge methodological advances and deployable clinical value, including medical imaging foundation models, EHR intelligence, and scalable biomedical data understanding (including emerging modalities such as single-cell data).
CLINES: Clinical LLM-based Information Extraction and Structuring Agent
Learning residue-level context for modeling protein-protein interactions
A Weakly Supervised Transformer for Rare Disease Diagnosis and Subphenotyping from EHRs with Pulmonary Case Studies
Beyond Independent Genes: Learning Module-Inductive Representations for Gene Perturbation Prediction
Controllable Multimodal Generation
I develop controllable multimodal generation methods for image/video/3D creation, with emphasis on compositionality, attribute-level control, and robust behavior under realistic user constraints.
RefineAnything: Multimodal Region-Specific Refinement for Perfect Local Details
BideDPO: Conditional Image Generation with Simultaneous Text and Condition Alignment
Stroke3D: Lifting 2D strokes into rigged 3D model via latent diffusion models
Multimodal Perception and Understanding
I study scene understanding in dynamic environments through segmentation, tracking, and multimodal reasoning, aiming to improve robustness, temporal consistency, and transferability.
Efficient training of large vision models via advanced automated progressive learning
SELongVLM: Empowering Long Video Language Models with Self-Corrective Clip Selection
Progressive Online Video Understanding with Evidence-Aligned Timing and Transparent Decisions
Streaming Video Understanding and Multi-round Interaction with Memory-enhanced Knowledge
Collaboration
I welcome collaborations on clinically grounded AI, controllable multimodal generation, and robust scene understanding. If your interests align, feel free to reach out by email.