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Basics

Name Core Francisco Park (박고래프란츠)
Position PhD Candidate
Affiliation Department of Physics, Harvard University
Email corefranciscopark@g.harvard.edu
Website https://cfpark00.github.io
Summary I build robust, reliable and deployable ML tools and methods for various problems in the physical sciences.

Education

Interests

Machine Learning
Deep Learning
Probabilistic Models
Uncertainty Quantification
Astrophysics
Cosmology
Image Space Statistics
Deep Learning
Bayesian Inference
Fourier Analysis
Neuroscience
Whole-Brain Imaging
Connectommics
Practical Machine Learning
Real Time Machine Learning
Mechanistic Understanding of ML
Compositional Generalization
Diffusion Models

Projects

  • 2024.02 - Present
    Understanding Emergence of Capabilities in Deep Learning
    • Emergence
    • Compositional Generalization
    • Diffusion Models
    • Collaborators: Maya Okawa, Ekdeep Singh Lubana, Andrew Lee, Hidenori Tanaka
  • 2023.09 - Present
    Emulating the Structure of the Universe using Diffusion Models
    • Large Scale Structure Emulation
    • Probabilistic Debiasing
    • Probabilistic Completion
    • Collaborators: Carolina Cuesta-Lazaro, Victoria Ono, Yueying Ni, Nayantara Mudur, Francisco Villaescusa-Navarro, Douglas Finkbeiner
  • 2023.09 - Present
    Hyperspectral Machine Learning for Tackling Climate Change
    • MethaneSAT Cloud Detection
    • MethaneSAT Shadow Correction
    • Collaborators: Cecilia Garraffo, Maya Nasr, Douglas Finkbeiner, Steven Wofsy
  • 2022.10 - Present
    Machine Learning Driven Accelerated Connectomics
    • SmartEM: Real Time ML driven Electron Microscopy Acquisition
    • VAE EM Compressor
    • Collaborators: Yaron Meirovitch, Ishaan Chandok, Aravinthan Samuel, Jeff Lichtman
  • 2021.04 - 2023.05
    Whole Brain Imaging
    • Neural Imaging of C.elegans Thermotaxis
    • Neural Imaging of C.elegans Mating Behavior
    • Collaborators: Vladislav Susoy, Helena Casademunt, Aravinthan Samuel
  • 2020.09 - 2022.10
    Astro-statistics
    • Quantifying the validity of high–dimensional statistics
    • Estimating correlations of Large Scale structure and galactic dustmaps:
    • Fourier space sparse Wavelet Scattering Transform on the GPU
    • Collaborators: Nayantara Mudur, Douglas Finkbeiner, Erwan Allys, Francisco Villaescusa-Navarro
  • 2020.08 - 2022.03
    3D Tracking for Confocal Microscopy data
    • Tracking neurons in a moving and deforming brain
    • Human Interfacing Deep Learning
    • Automatic Synthetic Data Generation
    • Collaborators: Sahand Rahi, Mahsa Barzegar-Keshteli, Vladislav Susoy, Aravinthan Samuel
  • 2018.06 - 2019.02
    Axion Dark Matter Search
    • Real-time DAQ system for the CAPP18T Axion Dark Matter Experiment
    • Collaborators: Kangheun Kim, Jonghee Yoo

Publications

Significant contribution

Secondary contribution

Presentations

  • 2024.07.26

    Poster

    3D Reconstruction of Dark Matter Fields with Diffusion Models: Towards Application to Galaxy Surveys
    ICML 2024 Workshop AI for Science, Vienna
    Poster
  • 2024.07.26

    Poster

    Hidden Learning Dynamics of Capability before Behavior in Diffusion Models
    ICML 2024 Workshop on High-Dimensional Learning Dynamics, Vienna
    Poster
  • 2024.07.02

    Talk

    Probabilistic Completion of Astrophysical Fields for Robust Statistics with Diffusion Models
    EAS 2024, Padova
    Slides
  • 2024.06.20

    Talk

    3D probabilistic reconstruction of the local dark matter from galaxies
    Astro AI Workshop 2024, Center for Astrophysics
    Slides
  • 2024.01.23

    Quick Talk

    Reconstruction of the local dark matter using diffusion models
    Workshop on AI-driven Discovery in Physics and Astrophysics at Kavli IPMU
    Slides
  • 2023.12.16

    Poster

    Hyperspectral shadow removal with iterative logistic regression and latent Parametric Linear Combination of Gaussians
    NeurIPS 2023 Workshop on Tackling Climate Change with Machine Learning
    Poster
    Paper
  • 2023.12.15

    Poster

    Probabilistic reconstruction of Dark Matter fields from galaxies using diffusion models
    NeurIPS 2023 Workshop on Machine Learning and the Physical Sciences
    Poster
    Paper
  • 2023.10.30

    Talk

    Diffusion Models for Cosmology
    AstroAI Lunch Talk, Center for Astrophysics
    Slides
  • 2023.10.13

    Talk

    How is AI used in the Physical Sciences?
    Hansung Science High School Mentoring Talk at Harvard University
    Slides
  • 2023.09.20

    Quick Talk

    Mstar2Mcdm using Diffusion Models: Generating Cold Dark Matter density fields conditioned on stellar mass fields using Denoising Diffusion Probabilistic Models
    Camels Virtual Telecoms hosted by the Flatiron Institute
    Slides
  • 2023.08.27

    Poster

    SmartEM: Faster Connectomics Using Deep Error Prediction Based Rescanning
    ICML 2023 Workshop for Computational Biology
    Poster
    Paper
  • 2023.06.23

    Poster

    Freely Moving Whole Brain Imaging of C. elegans under a thermal stimulus
    International C.elegans conference 2023
    Poster
  • 2023.03.10

    Poster

    Automated neuron tracking inside moving and deforming animals using deep learning and targeted augmentation
    COSYNE 2023
  • 2023.03.06

    Talk

    Automated neuron tracking using deep learning and targeted augmentation allows fast collection of C. elegans whole brain calcium activity during behavior
    American Physical Society March Meeting 2023
  • 2022.06.09

    Talk

    On the Gaussianity of Non-Gaussian probes of Large Scale Structure
    American Astronomical Society 240
    Slides
  • 2022.05.06

    Poster

    From sensing to behavior, biological information processing in C. elegans
    Citadel Ph.D. Summit 2022
    Poster
  • 2018.12.09

    Talk

    Dark Matter Axion Search Experiment using 18T HTS Magnet
    Stockholm International Youth Science Seminar 2018
  • 2018.10.25

    Poster

    Data acquisition system for dark matter Axion search experiment using 18 T HTS magnet at CAPP/IBS
    Korean Physical Society Meeting 2018

Reviewer

  • 2024.02
    ICLR 2024 Workshop on Tackling Climate Change with Machine Learning

Scholarships

  • 2019.09
    Purcell Fellowship
    Harvard University
  • 2019.02
    Doctoral Study Abroad Scholarship
    Korea Foundation for Advanced Studies
  • 2018.03
    Physics Department Honorary Scholarship
    KAIST
  • 2017.02
    Undergraduate Student Scholarship
    Korea Foundation for Advanced Studies
  • 2015.02
    Korea Presidential Science Scholarship
    KOSAF
  • 2015.02
    Full Tuition Scholarship
    Korea Foundation for Advanced Studies

Awards

  • 2023.12
    2nd Place in Citadel Datathon
    Citadel Securities and Correlation One
    Project: Enhancing analysis and prediction of A/B tests with Bayesian posteriors of the click probability.
    Report
  • 2019.06
    Best Project Award at the 10th KIAS CAC Summer school on Scientific Computing & Artificial Intelligence
    Korea Institute for Advanced Study
    Project: Machine learning for text style analysis and audio composition.
  • 2019.02
    Summa Cum Laude
    Korea Advanced Institute of Science and Technology
  • 2018.12
    Best Project Award, KIAS-SNU Physics Winter Camp
    Korea Institute for Advanced Study
    Project: Can gravitationally biased QCD dipoles explain the early growth of supermassive black holes?
  • 2017.06
    KAIST Physics Dean’s List
    Korea Advanced Institute of Science and Technology
  • 2016.12
    Best Buddy Award
    KAIST International Office
    Best contribution to the buddy program, helping the acclimatization of international students to Korean culture and university life.
  • 2015.12
    Dean’s List
    Korea Advanced Institute of Science and Technology
  • 2015.06
    Dean’s List
    Korea Advanced Institute of Science and Technology

Extracurricular

  • 2022.08
    KITP Neurophysics of Locomotion School
    Summer Student
    Project: Optogenetic control of haltere motion during flight in Drosophila.
  • 2019.07
    APCTP-POSTECH Biophysics School
    Summer Student
  • 2018.12
    KIAS-SNU Physics Winter Camp
    Summer Student
  • 2018.12
    Stockholm International Youth Science Seminar
    Representative of South Korea
    Presentation: Real time DAQ system for Axion Dark Matter Search.
  • 2018.07
    APCTP-NIMS-KISTI-KASI Summer School on Numerical Relativity
    Summer Student
  • 2018.06
    APCTP-POSTECH Biophysics School
    Summer Student
  • 2017.11
    KAIST International Discovery Program
    Team Leader
    Proposal: Visit to TUM's astrophysics and particle physics laboratory.
  • 2014.09
    Asian Science Camp
    Representative of South Korea
  • 2013.06
    Molecular Frontiers Symposium
    School Representative

Teaching

  • 2022.01
    Physics as a Foundation for Science and Engineering
    Teaching Fellow at Harvard University
  • 2021.09
    Physics of Sensory systems
    Teaching Fellow at Harvard University
  • 2019.03
    Physics Lab I
    Teaching Assistant / Lab Instructor at Seoul National University
  • 2018.09
    General Physics 2
    Tutor at KAIST
  • 2018.03
    General Physics 1
    Tutor at KAIST
  • 2017.03
    General Physics 1
    Tutor at KAIST
  • 2016.09
    General Physics 2
    Tutor at KAIST
  • 2016.03
    General Physics 1
    Tutor at KAIST

Skills

Computational Methods
GPU and CUDA computation
Fourier Analysis
Time Series Analysis
Bayesian Inference
Parallel Computing
Human-Computer Interfacing
ML/AI
Diffusion Models
Convolutional Neural Networks
Statistical Machine Learning
Interfacing Foundation Models
Prompting
Experimental
Real Time DAQ
Real Time Hardware Control
PID Control
Basic Optics
Microscopy
Programming Languages
Python (native speaker)
Julia (fluent)
LabVIEW (fluent)
Matlab (fluent)
Web Stuff (intermediate)
Java (basic)
C/C++ (basic)

Languages

English
Native speaker
Korean
Native speaker
French
(was a) Fluent speaker
Spanish
Beginner
Japanese
Beginner

References

Aravinthan Samuel
samuel@g.harvard.edu
Research Advisor for Neuroscience Projects
Douglas Finkbeiner
dfinkbeiner@cfa.harvard.edu
Research Advisor for Astrophysics Projects
Cecilia Garraffo
cgarraffo@cfa.harvard.edu
Research Advisor for AstroAI and EarthAI projects
Sahand Rahi
sahand.rahi@epfl.ch
Research Advisor for targettrack
Francisco Villaescusa-Navarro
fvillaescusa@flatironinstitute.org
Collaborator on vdm4cdm
Yaron Meirovitch
yaronmr@fas.harvard.edu
Collaborator on SmartEM
Carolina Cuesta-Lazaro
carolina.cuesta-lazaro@cfa.harvard.edu
Collaborator on vdm4cdm
Daniel Eisenstein
deisenstein@cfa.harvard.edu
Past Research Advisor in Cosmology
Jonghee Yoo
yoo.jonghee@kaist.ac.kr
Undergraduate Research Advisor
Hawoong Jeong
hjeong@kaist.edu
Undergraduate Academic Advisor