About Tae Geun Kim (Axect)

I am

Graduate student & Rustacean

Education

  • M.S. & Ph.D. Integrated: Department of Physics, Yonsei University (2017 ~ )
  • B.S.: Department of Astronomy, Yonsei University (2012 ~ 2017)

Research Area

  • Astroparticle Physics
  • Dark matter & BSM
  • Scientific computation & Machine learning

Skills

Mathematics

  • Functional Analysis
  • Numerical Analysis
    • Finite Difference Method
    • Finite Element Method
  • Differential Geometry
  • Topology

Physics

  • General Relativity
  • Quantum Field Theory
  • Mathematical Physics

Machine Learning

  • Statistical Machine Learning
    • Linear Regression (LASSO, Ridge)
    • Logistic Regression
    • Linear Discrimination
    • Kernel Based Methods
      • Kernel Smoothing
      • Kernel Density Estimation
  • Neural Network
    • MLP, CNN, RNN (LSTM, GRU), Transformer, Mamba
    • Operator learning & Neural ODE
    • Bayesian Neural Network

Programming

  • Main language: Rust, Julia, Python
  • Sub languages: C/C++, Haskell
  • Frameworks or Libraries
    • Numerical: peroxide, BLAS, LAPACK, numpy, scipy
    • Visualization: matplotlib, vegas, ggplot2, plotly
    • Web: Django, Vue, Firebase, Surge, Hugo
    • Machine Learning: PyTorch, JAX, Optax, Equinox, Wandb, Optuna, Candle, Tensorflow, Norse

Project

  • Peroxide: Numerical library for Rust (Maintainer)

    • Link
    • Tech stacks
      • Vectorization: SIMD, BLAS, LAPACK
      • FFI: C, Fortran, Python
      • Data structures: Dataframe
      • IO: csv, netcdf, json, parquet, serde
      • Meta-programming: proc-macro

  • Puruspe: Pure Rust Special function library (Maintainer)

  • Radient: Rust automatic differentiation library (Maintainer)

    • Link
    • Tech stacks
      • Reverse mode automatic differentiation
      • Arena based memory management

  • Forger: Rust Reinforcement Learning library (Maintainer)

    • Link
    • Tech stacks
      • Reinforcement Learning
        • Monte Carlo
        • Temporal Difference
        • Q-Learning

  • DeeLeMa: Deep Learning for Mass estimation (Maintainer)

    • Link
    • Tech stacks
      • Deep Learning Framework: PyTorch, PyTorch Lightning
      • Hyperparameter Optimization: Wandb-sweep
      • Monitoring: Wandb, Tensorboard
      • Package Management: PDM

  • ZelLayGen: Zellij Layout Generator (Maintainer)

    • Link
    • Tech stacks
      • Language: Rust
      • Serialization & Deserialization: Serde
      • Data format: TOML, KDL

  • NCDataFrame.jl: Julia netCDF I/O with DataFrame (Maintainer)

  • Puruda: Pure Rust Dataframe library (Archived)

    • Link
    • Tech stacks
      • Meta-programming: proc-macro
      • IO: csv, netcdf

  • HNumeric: Numerical library for Haskell (Archived)

  • DNumeric: Numerical library for D (Archived)

Academic Activities

Papers

  • Tae-Geun Kim , 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, arXiv:2212.11977 (2022)

Conferences

Books I’ve read

Mathematics

  • Linear Algebra
    • Mark S, Gockenbach, Finite-Dimensional Linear Algebra. 1st ed., CRC Press (2010)
  • Analysis
    • 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)
  • Differential Geometry
    • 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)
  • Topology
    • James R. Munkres, Topology. 2nd ed., Pearson College Div (2000)
    • Werner Ballmann, Introduction to Geometry and Topology. 1st ed., Birkhäuser (2018)

Physics

  • Classical Mechanics
    • L. D. Landau, E. M. Lifshitz, Mechanics: Volume 1. 3rd ed., Butterworth-Heinemann (1976)
    • Herbert Goldstein, Classical Mechanics. 3rd ed., Pearson (2001)
  • Quantum Mechanics
    • 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)
  • General Relativity
    • 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)
  • Quantum Field Theory
    • 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)

Machine Learning

  • Statistical Machine Learning
    • 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)
  • Deep Learning
    • 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)
  • Reinforcement Learning
    • 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)

ETC

  • Algorithm
    • 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)