Poompol (Paul) Buathong
Ph.D. Candidate in Applied Mathematics at Cornell University
I am a Ph.D. candidate in Applied Mathematics at the Center for Applied Mathematics, Cornell University, USA, where I work with Prof. Peter Frazier on Bayesian optimization and AI for Science.
I received a Bachelor’s degree in Mathematics (2017) and a Master’s degree in Applied Mathematics (2020) from Mahidol University, Thailand. My master’s thesis focused on Bayesian optimization for set-valued input functions under the supervision of Assoc. Prof. Tipaluck Krityakierne, with co-supervision from Prof. David Ginsbourger during my internship at Idiap Research Institute, Switzerland. I also obtained a second Master’s degree in Applied Mathematics from Cornell University in 2024.
I am a recipient of the DPST scholarship from the Institute for the Promotion of Teaching Science and Technology (IPST), Thailand, supporting my studies from undergraduate through doctoral levels.
My research focuses on Bayesian optimization, machine learning, and their applications to scientific problems, including food science, materials design, and chemistry.
✈️ Upcoming Travels
June 14–19, 2026
Singapore
July 31–August 3, 2026
Raleigh, NC
November 1–4, 2026
San Francisco, CA
Research Highlights
Kernel Methods
Set-valued Input Functions
Developed kernel methods and Bayesian optimization techniques for optimization problems where the inputs are sets rather than fixed-dimensional vectors, enabling black-box optimization over combinatorial and structured domains.
Grey-box Bayesian Optimization
Function Networks & Partial Evaluations
Developed Bayesian optimization methods that exploit internal objective-function structure and partial evaluations, substantially improving sample efficiency for expensive scientific optimization problems.
AI for Science
Protein Function Prediction
Developing machine learning methods for protein function prediction under heavily biased evolutionary and surveillance data using positive-unlabeled learning and evolutionary modeling.
AI for Science
Formulation Design
Applying Bayesian optimization and machine learning to optimize protein formulation design for improved thermal stability and functionality in food systems.
Last updated: May 21, 2026