Research

My research lies at the broader area of Computer Vision and Deep Learning, with a long-term aim of developing intelligent models that emulate the human intelligence.

In the short term, my research mainly focuses on data- and computation-efficient deep learning. I approach this through three key directions.

Synthetic Training

Training AI models with synthetic samples can enhance model performance without data acquisition and annotation. I'm particularly interested in data synthesis, especially geometry-based data synthesis, which generates massive diverse samples with precise annotations.

Data- and computation-efficient models.

Another key direction is to develop data- and computation-efficient models. This includes designing models that can learn with fewer data acquisitions, or models that require less computation to achieve the same performance.

AI4Science

In the meantime, I also have a strong interest in, and have been actively working on, applying AI to scientific research, especially in the field of computational biology, including but not limited to biological image analysis, biological sequence (Protein, RNA, DNA etc) analysis.