In overall, I am interested in meta-learning (few-shot, zero-shot), domain adaptation (continual learning, transfer learning, out-of-distribution detection), and black-box optimization (Bayesian optimization). My long-term research plan is to develop machine-learning algorithms that can learn continuously with minimal data or supervision and effectively adapt to other domains outside of training distribution, towards efficient open-world machine-learning.

In particular, I am interested in exploring both meta-learning and domain adaptation and integrating them in joint settings on computer vision tasks, i.e. continual few-shot object detection. However, each research field has its own limitations, and combining them often results in extremely complex and constrained settings. Therefore, I am aiming to approach and formulate these joint settings from an optimization perspective, especially in the black-box context (derivative-free), to find effective solutions for solving them and then advance toward efficient algorithms for open-world machine learning.

In addition, by effectively optimizing black-box function(s) under various constraints and conditions based on meta-learning and domain adaptation, such as few-shot evaluations, conflicting objectives, or function shifts, we can develop flexible and robust optimization algorithms for a wide range of fundamental and applied scientific research, such as experimental design for material construction or self-driving car.

Although my background and my research interests mainly focus on computer vision, with the aforementioned research directions, I am happy to expand and explore other fields as well, such as NLP, LLM, or multimodal. I believe that these research lines can be effectively applied to improve any domain of machine learning, regardless of vision, language, or their combination.

Bellow is my Research Statement (updated December 2023):

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