Research Vision

My research bridges the gap between dark matter phenomenology and scientific machine learning, creating a unified approach to understanding fundamental physics through both traditional analytical methods and modern AI techniques.

I investigate dark matter candidates—particularly primordial black holes and axion-like particles—using a combination of cosmological observations, astrophysical constraints, and novel computational methods. Simultaneously, I develop operator learning frameworks that enable AI to not merely fit data, but to genuinely understand and emulate the underlying physical laws governing these systems.

This dual expertise positions me uniquely at the intersection of particle cosmology and AI4Science, where I can both discover new physics and create the computational tools needed to explore it.

Current Focus & Future Directions

Physics-Informed Machine Learning for Cosmology

Developing neural operators (DeepONet, MambONet) that learn Hamiltonian mechanics and can accelerate computationally expensive phenomenology calculations in primordial black hole physics. This approach has shown that AI can outperform traditional numerical solvers (RK4) while maintaining physical interpretability.

Future Direction: Extend operator learning to inverse problems in dark matter detection, enabling real-time parameter inference from observational data.

Dark Matter Phenomenology

Exploring the interplay between primordial black holes and axion-like particles as complementary dark matter candidates. My work establishes novel constraints using astrophysical probes (gamma-ray observations, neutrino detectors) and demonstrates how PBH evaporation can serve as a “factory” for axion production.

Future Direction: Investigate multi-messenger signatures combining gravitational waves, electromagnetic signals, and particle detections to probe the early universe.

Interdisciplinary AI Applications

Applying deep learning techniques tailored to each domain: operator learning for cosmology, sequence-to-sequence models for biomedical signal processing, learning rate schedulers for optimization. Developing domain-specific architectures while building transferable frameworks for cross-domain knowledge transfer.

Future Direction: Build a unified scientific ML platform that adapts to diverse physics problems, leveraging the appropriate neural architecture for each domain—from quantum field theory to experimental data analysis.

Publications

2025

Learning Hamiltonian Dynamics with Bayesian Data Assimilation Taehyeun Kim, Tae-Geun Kim , Anouk Girard, Ilya Kolmanovsky 📄 arXiv:2501.18808

@article{kim2025learning,
  title={Learning Hamiltonian Dynamics with Bayesian Data Assimilation},
  author={Kim, Taehyeun and Kim, Tae-Geun and Girard, Anouk and Kolmanovsky, Ilya},
  journal={arXiv preprint arXiv:2501.18808},
  year={2025}
}

Primordial Black Holes as a Factory of Axions: Extragalactic Photons from Axions Tae-Geun Kim , Jong-Chul Park, Seong Chan Park, Yeji Park 📄 PTEP ptag011 | 📄 arXiv:2212.11977

@article{kim2023primordial,
  title={Primordial Black Holes as a Factory of Axions: Extragalactic Photons from Axions},
  author={Kim, Tae-Geun and Park, Jong-Chul and Park, Seong Chan and Park, Yeji},
  journal={Progress of Theoretical and Experimental Physics},
  volume={2023},
  number={1},
  pages={ptag011},
  year={2023},
  doi={10.1093/ptep/ptag011}
}

2024

Neural Hamilton: Can A.I. Understand Hamiltonian Mechanics? Tae-Geun Kim , Seong Chan Park 📄 arXiv:2410.20951 | 💻 Code

@article{kim2024neural,
  title={Neural Hamilton: Can A.I. Understand Hamiltonian Mechanics?},
  author={Kim, Tae-Geun and Park, Seong Chan},
  journal={arXiv preprint arXiv:2410.20951},
  year={2024}
}

HyperbolicLR: Epoch insensitive learning rate scheduler Tae-Geun Kim 📄 arXiv:2407.15200

@article{kim2024hyperboliclr,
  title={HyperbolicLR: Epoch insensitive learning rate scheduler},
  author={Kim, Tae-Geun},
  journal={arXiv preprint arXiv:2407.15200},
  year={2024}
}

Unsupervised sequence-to-sequence learning for automatic signal quality assessment Chang Min Hyun, Tae-Geun Kim , Kyounghun Lee 📄 CMPB 108079 | 📄 arXiv:2305.09368

@article{hyun2024unsupervised,
  title={Unsupervised sequence-to-sequence learning for automatic signal quality assessment in multi-channel electrical impedance-based hemodynamic monitoring},
  author={Hyun, Chang Min and Kim, Tae-Geun and Lee, Kyounghun},
  journal={Computer Methods and Programs in Biomedicine},
  pages={108079},
  year={2024},
  doi={10.1016/j.cmpb.2024.108079}
}

2023

DeeLeMa: Missing information search with Deep Learning for Mass estimation Kayoung Ban, Dong Woo Kang, Tae-Geun Kim , Seong Chan Park, Yeji Park 📄 Phys. Rev. Research 5, 043186 | 📄 arXiv:2212.12836 | 💻 Code

@article{ban2023deelema,
  title={DeeLeMa: Missing information search with Deep Learning for Mass estimation},
  author={Ban, Kayoung and Kang, Dong Woo and Kim, Tae-Geun and Park, Seong Chan and Park, Yeji},
  journal={Physical Review Research},
  volume={5},
  number={4},
  pages={043186},
  year={2023},
  doi={10.1103/PhysRevResearch.5.043186}
}

Selected Talks & Presentations

2026

Accelerating PBH Phenomenology via Neural Operators The 2nd AI+HEP in East Asia @ KEK, Tsukuba, Japan [Oral]

2025

From Primordial Black Holes to Nuclei: Solving Inverse Problems in Physics with Operator Learning Nuclear Lunch Seminar @ Fudan University, Shanghai, China [Oral, Invited]

Can AI Understand Hamiltonian Mechanics? Summer Institute 2025, Yeosu, Korea [Oral] | 📊 Slides

A Neural Operator for Primordial Black Hole Physics The 4th workshop on Symmetry and Structure of the Universe, Jeonju, Korea [Oral] | 📊 Slides

PBH Phenomenology with Operator Learning LSSU Seminar @ JBNU, Jeonju, Korea [Oral, Invited] | 📊 Slides

AI with Hamiltonian Mechanics: From Predictions to Understanding AI+HEP in East Asia 2025, Daejeon, Korea [Oral] | 📊 Slides

Can A.I. Understand Hamiltonian Mechanics? RIKEN DEEP-IN Seminar, Online [Oral, Invited]

Can A.I. Understand Hamiltonian Mechanics? Dark Matter as a portal to New Physics 2025, Pohang, Korea [Oral, Invited]

Can A.I. Understand Hamiltonian Mechanics? Focused workshop on AI in High Energy Physics 2025, Seoul, Korea [Oral]

2024

Can A.I. Understand Hamiltonian Mechanics? 2024 Korea-France STAR Workshop, Seoul, Korea [Oral]

Can A.I. Understand Hamiltonian Mechanics? Invited Seminar @ KIAS, Seoul, Korea [Oral, Invited]

Can A.I. Understand Hamiltonian Mechanics? 21st Saga-Yonsei Joint Workshop, Seoul, Korea [Oral]

Primordial Black Hole dominant Axion background 2024 KPS Fall Meeting, Yeosu, Korea [Oral]

2023

Exploration of Primordial Black Holes and Axion-Like Particles 27th International Summer Institute on Phenomenology, Nantou, Taiwan [Poster] | 📊 Poster

2022

Exploration of Primordial Black Holes and Axion-Like Particles 16th International Conference on Interconnections between Particle Physics and Cosmology, Daejeon, Korea [Oral]

Constraining ALPs via PBH with time-varying decay process Workshop on Physics of Dark Cosmos, Busan, Korea [Oral] | 📊 Slides

Constraining ALPs via PBH with time-varying decay process Part.2 KPS 70th Anniversary and 2022 Fall Meeting, Busan, Korea [Oral] 🏆 Best Oral Award | 📊 Slides

2019

Bird’s eye view of Neutron star cooling 16th Saga-Yonsei Joint Workshop [Oral] | 📊 Slides

Honors & Awards

• Shanghai Superpostdoc Fellowship, Shanghai Municipal Government (2025-2027)
• Fudan Superpostdoc Fellowship, Fudan University (2025-2027)
• Academy Research Fellowship, Yonsei University (2022-2023)
• Best Oral Presentation Award, KPS 70th Anniversary and 2022 Fall Meeting (2022)