Core Francisco Park
Ph.D. in Physics, Harvard University
Research Interests
- Machine Learning: Deep Learning, Probabilistic Models, Uncertainty Quantification
- Astrophysics: Cosmology, Image Space Statistics, Bayesian Inference, Fourier Analysis
- Neuroscience: Whole-Brain Imaging, Connectomics, Practical Machine Learning
- Mechanistic Understanding of ML: Compositional Generalization, Diffusion Models, In-Context Learning
Education
Ph.D. in Physics
Harvard University · 2019 - May 29, 2025
Machine Learning for Physical Sciences
GPA: 3.945/4.0
Advisors: Aravinthan Samuel, Douglas Finkbeiner, Hidenori Tanaka, Michael Brenner
Thesis: Deep Learning as a Scientific Tool and a Model Organism of Intelligence
Graduate Coursework in Physics
Seoul National University · 2019
Advanced physics coursework
Exchange Student
Ecole Polytechnique, France · 2017
Physics and Computer Science
B.S. in Physics, Advanced Major
KAIST · 2015 - 2019
Summa Cum Laude, Focus: Computational Physics
GPA: 4.08/4.3
Thesis: Real time DAQ setup and dead-time measurement for CAPP 18T Dark Matter Axion search
Professional Experience
Postdoctoral Fellow
Harvard University · May 30, 2025 - Present
- Working with Dr. Venkatesh Murthy and Dr. Hidenori Tanaka
- Research on AI and neuroscience
Research Intern
NTT Research · Jan 2025 (1 month), Jul - Aug 2024 (2 months)
- Understanding Mechanisms and Capabilities of AI
- Supervisor: Hidenori Tanaka
- Developing synthetic experiments for AI understanding
Research Assistant
Harvard University · Jun 2020 - May 2025
- Leading research on compositional generalization and in-context learning
- Developing ML tools for astrophysics and neuroscience applications
- Publishing in top-tier venues (NeurIPS, Nature Methods, ApJ)
- Supervisors: Dr. Aravinthan Samuel, Dr. Douglas Finkbeiner, Dr. Cecilia Garraffo
Teaching Assistant
Harvard University · 2021 - 2022
- Applied Physics 50: Physics as Foundation for Science & Engineering (Spring 2022)
- Physics 141: The Physics of Sensory Systems in Biology (Fall 2021)
- Mentoring undergraduate and graduate students
Publications
Awards & Honors
- 2nd Place - Citadel Datathon2023
- Best Machine Learning Project Award - KIAS2019
- Purcell Fellowship - Harvard2019-2020
- Summa Cum Laude - KAIST2019
- Best Project Award Physics Winter Camp - KIAS2018
- Dean's List - KAIST Physics2017
- Korea Presidential Science Scholarship2015-2019
- Dean's List - KAIST (Fall & Spring)2015
Skills & Expertise
ML/AI Research
Programming
Computational
Data Analysis
DevOps
Experimental
Languages
Professional Activities
Membership
- Sigma-XiJun 2025 - Present
Peer Review (30 papers total)
- NeurIPS6 papers · 2025
- ICML HiDL Workshop3 papers · 2025
- ICML MOSS Workshop2 papers · 2025
- CoLM2 papers · 2025
- ICLR Workshop on Tackling Climate Change with ML2 papers · 2025
- ICLR4 papers · 2024
- NeurIPS Workshop on Scientific Methods for Understanding Deep Learning4 papers · 2024
- ICML Workshop on Mechanistic Interpretability3 papers · 2024
- ICLR Workshop on Tackling Climate Change with ML4 papers · 2024
Extracurricular Activities
- Harvard AI Safety Team2024
Member
- KITP Neurophysics of Locomotion School2022
Summer Student
- APCTP-POSTECH Biophysics School2019
Summer Student
- KIAS-SNU Physics Winter Camp2018
Best Project Award: Accretion of Supermassive Black Holes
- Stockholm International Youth Science Seminar2018
Korean Representative
- APCTP-NIMS-KISTI-KASI Summer School on Numerical Relativity2018
Summer Student
- APCTP-POSTECH Biophysics School2018
Summer Student
- KAIST International Discovery Program2017
Selected Team
- Asian Science Camp2014
Korean Representative
- Molecular Frontiers Symposium2013
School Representative