김태근 (Axect)을 소개합니다.

저는

수학, 물리학 그리고 프로그래밍을 좋아하는 연구자입니다.

경력

  • Joint Postdoc: Institute of Modern Physics, Fudan University & RIKEN iTHEMS (2025.10 ~ )

학력

  • Ph.D.: 연세대학교 대학원 물리학과 (2017.03 ~ 2025.08)
  • B.S.: 연세대학교 천문우주학과 (2012.03 ~ 2017.02)

연구 관심사

제 연구는 암흑물질 현상론과 AI4Science를 연결하여, 원시 블랙홀, 액시온 유사 입자를 탐구하고 물리학을 위한 연산자 학습 프레임워크를 개발합니다. 자세한 연구 비전과 출판물은 Research 페이지를 참고해주세요.

학문 및 기술

수학

  • 함수해석학
  • 수치해석학
    • 유한차분법
    • 유한요소법
  • 미분기하학
  • 위상수학

물리학

  • 천체입자물리학
  • 일반상대성이론
  • 양자장이론
  • 수리물리학

기계학습

  • 통계적 기계학습
    • 선형회귀 (LASSO, Ridge)
    • 로지스틱 회귀
    • 선형분류
    • Kernel Based Methods
      • Kernel Smoothing
      • Kernel Density Estimation
  • 인공신경망
    • MLP, CNN, RNN (LSTM, GRU), Transformer, Mamba
    • Operator learning & Nerual ODE
    • Bayesian Neural Network

프로그래밍

  • 주 언어
    • Rust, Julia, Python
  • 보조 언어
    • C/C++, Haskell
  • 프레임워크 및 라이브러리
    • 수치 계산
      • peroxide, BLAS, LAPACK, numpy, scipy
    • 시각화
      • matplotlib, vegas, ggplot2, plotly
    • 웹
      • Django, Vue, Firebase, Surge, Hugo, Zola
    • 머신러닝
      • PyTorch, JAX, Optax, Equinox, Wandb, Optuna, Candle, Tensorflow, Norse

오픈소스 프로젝트

오픈소스 프로젝트의 전체 목록은 Software 페이지를 참고해주세요.

주요 프로젝트:

  • Peroxide - 선형대수, ODE 솔버, 최적화 도구를 제공하는 종합 과학계산 Rust 라이브러리 (1M+ 다운로드, 500+ stars)
  • DeeLeMa - 암흑물질 질량 추정을 위한 PyTorch 기반 프레임워크, Physical Review Research 게재 (2023)
  • Neural Hamilton - 데이터로부터 해밀토니안 역학을 재구성하는 연산자 학습 및 Neural ODE 현재 연구, 전통적인 RK4 솔버와 벤치마킹 (arXiv 2024)

학술활동

논문

  • Taehyeun Kim, Tae-Geun Kim , Anouk Girard and Ilya Kolmanovsky, Learning Hamiltonian Dynamics with Bayesian Data Assimilation, arXiv:2501.18808 (2025)

  • Tae-Geun Kim and Seong Chan Park, Neural Hamilton: Can A.I. Understand Hamiltonian Mechanics?, arXiv:2410.20951 (2024)

  • Tae-Geun Kim , HyperbolicLR: Epoch insensitive learning rate scheduler, arXiv:2407.15200 (2024)

  • Chang Min Hyun, Tae-Geun Kim , and Kyounghun Lee, Unsupervised sequence-to-sequence learning for automatic signal quality assessment in multi-channel electrical impedance-based hemodynamic monitoring, CMPB 108079, arXiv:2305.09368 (2023)

  • Kayoung Ban, Dong Woo Kang, Tae-Geun Kim , Seong Chan Park and Yeji Park, DeeLeMa : Missing information search with Deep Learning for Mass estimation, Phys. Rev. Research 5, 043186, arXiv:2212.12836 (2022)

  • Yongsoo Jho, Tae-Geun Kim , Jong-Chul Park, Seong Chan Park and Yeji Park, Axions from Primordial Black Holes, PTEP ptag011, arXiv:2212.11977 (2022)

발표

읽은 책들

수학

  • 선형대수학
    • Mark S, Gockenbach, Finite-Dimensional Linear Algebra. 1st ed., CRC Press (2010)
  • 해석학
    • Walter Rudin, Principles of Mathematical Analysis. 3rd ed., McGraw Hill (1976)
    • Elias M. Stein, Rami Shakarchi, Fourier Analysis: An Introduction. Illustrated ed., Princeton University Press (2003)
    • Elias M. Stein, Rami Shakarchi, Real Analysis: Measure Theory, Integration, and Hilbert Spaces. 1st ed., Princeton University Press (2005)
  • 미분기하학
    • William M. Boothby, An Introduction to Differentiable Manifolds and Riemannian Geometry. Revised 2nd ed., Academic Press (2002)
    • Barrett O’Neill, Elementary Differential Geometry. Revised 2nd ed., Academic Press (2006)
  • 위상수학
    • James R. Munkres, Topology. 2nd ed., Pearson College Div (2000)
    • Werner Ballmann, Introduction to Geometry and Topology. 1st ed., Birkhäuser (2018)

물리학

  • 고전역학
    • L. D. Landau, E. M. Lifshitz, Mechanics: Volume 1. 3rd ed., Butterworth-Heinemann (1976)
    • Herbert Goldstein, Classical Mechanics. 3rd ed., Pearson (2001)
  • 양자역학
    • Ashok Das, Lectures on Quantum Mechanics. 2nd ed., World Scientific Publishing Company (2012)
    • J. J. Sakurai, Jim J. Napolitano, Modern Quantum Mechanics. 2nd ed., Pearson (2010)
  • 일반상대성이론
    • Harvey Reall, Part 3 General Relativity, University of Cambridge 65 (2013)
    • M. P. Hobson et al., General Relativity: An Introduction for Physicists. Illustrated ed., Cambridge University Press (2006)
    • F. de Felice, C. J. S. Clarke, Relativity on Curved Manifolds, Cambridge University Press (1992)
  • 양자장이론
    • Lewis H. Ryder, Quantum Field Theory. 2nd ed., Cambridge University Press (1996)
    • Michael E. Peskin, Daniel V. Schroeder, An Introduction to Quantum Field Theory, Student Economy Edition. 1st ed., Westview Press (2015)
    • Michele Maggiore, A Modern Introduction to Quantum Field Theory, Oxford University Press (2005)
    • Ashok Das, Field Theory: A Path Integral Approach. 3rd ed., World Scientific (2006)

기계학습

  • 통계적 기계학습
    • Masashi Sugiyama, Introduction to Statistical Machine Learning. 1st ed., Morgan Kaufmann (2015)
    • Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer (2006)
    • Gareth James et al., An Introduction to Statistical Learning: with Applications in R. 1st ed., Springer (2013)
    • Trevor Hastie et al., The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed., Springer (2016)
    • Yaser S. Abu-Mostafa et al., Learning from Data, AMLBook (2012)
  • 심층학습
    • Zhang et al., Dive into Deep Learning. 1.0.0-alpha0. (2022)
    • Eli Stevens et al., Deep Learning with PyTorch, Manning (2020)
    • 오가와 유타로, 만들면서 배우는 파이토치 딥러닝: 12가지 모델로 알아보는 딥러닝 응용법, 한빛미디어 (2021)
  • 강화학습
    • Laura Graesser and Wah Loon Keng, Foundations of Deep Reinforcement Learning: Theory and Practice in Python. 1st ed., Addison-Wesley Professional (2020)
    • Csaba Szepesvári, Algorithms for Reinforcement Learning. 1st ed., Morgan & Claypool Publishers (2009)

기타

  • 알고리즘
    • Tim Roughgarden, Algorithms Illuminated: Part1: The Basics. Illustrated ed., Soundlikeyourself Publishing (2017)
  • Rust
    • Steve Klabnik, Carol Nichols, The Rust Programming Language. 1st ed., No Starch Press (2018)
    • Jim Blandy, Jason Orendorff, Programming Rust: Fast, Safe, Systems Development. 1st ed., O’Reilly Media (2018)
    • Tim McNamara, Rust in Action, Manning (2021)