Hello, I'm Tae-Geun Kim 👋

🙋‍♂️ About Me

• Graduate student in Physics at Yonsei University
• Member of Yonsei HEP-COSMO
• Curriculum Vitae | Blog

❤️ Interests

• High energy astrophysics, dark matter, and cosmology
• Scientific computation
• Machine Learning / Deep Learning / Statistics
• Quantum Computing

💼 Key Projects

1. Peroxide: Rust numeric library for scientific computing

   • Linear algebra, numerical analysis, statistics, and machine learning
   • User-friendly syntax similar to R, NumPy, and MATLAB
   • Supports functional programming and automatic differentiation

2. HyperbolicLR: Novel learning rate schedulers for deep learning

   • Addresses learning curve decoupling problem
   • Improves performance and stability across increasing epochs
   • Implemented and evaluated using PyTorch

3. Puruspe: Pure Rust library for special functions

   • Implements gamma, beta, and error functions with no dependencies
   • Lightweight and efficient for mathematical computing

4. Forger: Reinforcement Learning library in Rust

   • Modular design for agents, environments, and policies
   • Supports customizable strategies and learning algorithms

5. PyTorch Template Project: Flexible template for ML experiments

   • Configurable experiments using YAML files
   • Integration with Weights & Biases and Optuna
   • Support for multiple random seeds and device selection

6. DeeLeMa: Deep learning for particle collision analysis

   • Estimates mass and momenta in high-energy collider events
   • Adaptable to different event topologies

📚 Selected Publications

1. T.-G. Kim, "HyperbolicLR: Epoch insensitive learning rate scheduler", arXiv:2407.15200 (2024)
2. C.M. Hyun, T.-G. Kim, K. Lee, "Unsupervised sequence-to-sequence learning for automatic signal quality assessment...", CMPB 108079 (2023)
3. K. Ban et al., "DeeLeMa: Missing information search with Deep Learning for Mass estimation", Phys. Rev. Research 5, 043186 (2022)

Your support will help me continue developing open-source scientific computing tools and pursuing research in physics and machine learning. Thank you for considering sponsorship!