About me

As an AI engineer, my current focus lies in the realm of context-aware search engines and neural representations of texts. I also take pride in identifying myself as an applied mathematician. Prior to embarking on my journey as an AI engineer, I obtained my PhD in Computational Science and Engineering, as well as Mechanical Engineering, from Massachusetts Institute of Technology (MIT). My research interests spanned across mathematical representations of optical scattering and physics-assisted neural networks.

Education

• PhD, Computational Science and Engineering and Mechanical Engineering; MIT
  • Dissertation: Scalar scattering theory and physics-inspired optimization for computational imaging
  • Minor: Applied Mathematics
• MS, Mechanical Engineering; MIT
  • Thesis: Machine learning regularized solution of the Lippmann-Schwinger equation
• BSc, Materials Science and Engineering; Seoul National University
  • Thesis: Investigation of metal-insulator transition in $\text{SrRuO}_3$ with electron energy loss spectroscopy

Publication

Please refer to my Google Scholar profile for a list of my publications.

Technical skills

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quadrantChart
    x-axis Uninteresting --> Interesting
    y-axis Inexperienced --> Experienced
    quadrant-1 Expertise & Passion
    quadrant-2 Professional Obligation
    quadrant-3 Avoidance Zone
    quadrant-4 Learning & Growth
    Python: [0.75, 0.85]
    Julia: [0.65, 0.8]
    Rust: [0.9, 0.4]
    Solidity: [0.2, 0.2]
    Typescript: [0.3, 0.3]
    Fortran: [0.55, 0.22]
    MATLAB: [0.3, 0.7]

Awards

2022
First Place in Graduate Science, de Florez Award, Department of Mechanical Engineering, Massachusetts Institute of Technology, USA

2021
First Place in Localization of Maximum Vertical Pocket in Prenatal Ultrasound, A-AFMA Ultrasound Challenge, Oxford Institute of Biomedical Engineering, Oxford, UK

2018-2020
Kwanjeong Lee Chonghwan Scholarship, Kwanjeong Lee Chonghwan Educational Foundation, Republic of Korea.

2016
Grand Prize in Computational Chemistry, EDucation-research Integration through Simulation On the Net (EDISON) Challenge, Ministry of Science, ICT and Future Planning of Korea, Republic of Korea.

• Paper: Free energy estimation in dissipative particle dynamics, Proceeding of EDISON Challenge, Korea Institute of Science and Technology Information.

2013 and 2016
National Scholarship for Science and Engineering, Korea Student Aid Foundation, Republic of Korea.

Teaching experience

2023
Teaching Assistant, Physical Systems Modeling and Design Using Machine Learning, MIT Course 2.C01/2.C51.

2021
Teaching Assistant, Physical Systems Modeling and Design Using Machine Learning, MIT Course 2.161/2.169.