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layout: about title: About permalink: / subtitle:
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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 fully-funded PhD student at School of Artificial Intelligence, CUHK-Shenzhen, advised by Prof. Feng Yin and Prof. Tsung-Hui Chang. Prior to my PhD, I obtained my BSc degree in Statistics (with first-class honors) from CUHK-Shenzhen.
I am broadly interested in building sample-efficient and reliable systems that can automate discovery in science and engineering. My current research explores:
- Bayesian optimization for sample-efficient black-box optimization and sequential decision-making
- Gaussian processes for probabilisitic modeling and uncertainty quantification
- Foundation models for open-ended algorithm discovery and generative design in science & engineering
I co-developed OpenEvolve, an evolutionary coding agent for automated algorithm discovery and optimization. I also helped build Kai, an autonomous agent that can evolve codebases by finding and patching software vulnerabilities.
I am also a founding committee member of the Institute for AI-driven Discovery of Algorithms (AIDDA), where I contribute to coordinating research, knowledge sharing, and community building in this emerging discipline.
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 fully-funded PhD student at School of Artificial Intelligence, CUHK-Shenzhen, advised by Prof. Feng Yin and Prof. Tsung-Hui Chang. Prior to my PhD, I obtained my BSc degree in Statistics (with first-class honors) from CUHK-Shenzhen.
I am broadly interested in building sample-efficient and reliable systems that can automate discovery in science and engineering. My current research explores:
- Bayesian optimization for sample-efficient black-box optimization and sequential decision-making
- Gaussian processes for probabilisitic modeling and uncertainty quantification
- Foundation models for open-ended algorithm discovery and generative design in science & engineering
I co-developed OpenEvolve, an evolutionary coding agent for automated algorithm discovery and optimization. I also helped build Kai, an autonomous agent that can evolve codebases by finding and patching software vulnerabilities.
I am also a founding committee member of the Institute for AI-driven Discovery of Algorithms (AIDDA), where I contribute to coordinating research, knowledge sharing, and community building in this emerging discipline.