people
members of the lab or group
555 your office number
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layout: about title: About permalink: / subtitle:
profile: align: right image: bio.jpeg image_circular: false # crops the image to make it circular more_info:
news: true # includes a list of news items selected_papers: true # includes a list of papers marked as “selected={true}” social: true # includes social icons at the bottom of the page —
I am a PhD student at CUHK-Shenzhen, advised by Prof. Feng Yin and Prof. Tsung-Hui Chang. Prior to my PhD, I obtained my BSc degree in Statistics from CUHK-Shenzhen.
I am interested in building open-ended intelligent systems capable of continuous learning, efficient knowledge acquisition from limited data, and generalization across an evolving range of tasks. To this end, my research explores:
- Bayesian optimization for sample-efficient, adaptive exploration in open-ended search spaces
- Gaussian processes to enable principled decision-making and uncertainty quantification
- Foundation models for open-ended algorithm discovery, creative problem solving, and generative design in science and engineering
If you are interested in collaborating or discussing research ideas, feel free to reach out via email.
555 your office number
123 your address street
Your City, State 12345
layout: about title: About permalink: / subtitle:
profile: align: right image: bio.jpeg image_circular: false # crops the image to make it circular more_info:
news: true # includes a list of news items selected_papers: true # includes a list of papers marked as “selected={true}” social: true # includes social icons at the bottom of the page —
I am a PhD student at CUHK-Shenzhen, advised by Prof. Feng Yin and Prof. Tsung-Hui Chang. Prior to my PhD, I obtained my BSc degree in Statistics from CUHK-Shenzhen.
I am interested in building open-ended intelligent systems capable of continuous learning, efficient knowledge acquisition from limited data, and generalization across an evolving range of tasks. To this end, my research explores:
- Bayesian optimization for sample-efficient, adaptive exploration in open-ended search spaces
- Gaussian processes to enable principled decision-making and uncertainty quantification
- Foundation models for open-ended algorithm discovery, creative problem solving, and generative design in science and engineering
If you are interested in collaborating or discussing research ideas, feel free to reach out via email.