About Me

I'm a Ph.D student of Yonsei university, department of computational science and engineering. I'm an AI developer and researcher of Computational Methods in Medical Engineering (CMME) Lab. My research interest is in adapting computational engineering techniques in medical domain, specifically for modeling and analyzing physical phenomena during treatment procedures.

Research Interests

  • Transcranial Focused Ultrasound

    Low-intensity transcranial focused ultrasound (LIFU) is an emerging noninvasive technique for precisely modulating targets within the brain. Its major challenge is skull-induced attenuation and aberration, which distort the acoustic focus. To address this, we develop AI-accelerated simulation methods and patient-specific acoustic holographic lenses for accurate and efficient treatment planning.

  • Physics-Guided AI

    High-fidelity numerical solvers accurately model physical phenomena, but their computational cost limits real-time applications. Physics-guided AI combines simulation data with physical knowledge to learn fast and reliable mappings between treatment conditions and resulting fields. Our work explores surrogate modeling, super-resolution networks, and neural operators across acoustic, thermal, and mechanical problems.

  • Digital Twins

    Medical digital twins provide virtual representations of patients and treatments that can be updated as conditions change. Their practical use requires models that integrate patient anatomy, treatment parameters, and physical processes while producing predictions in real time. We develop simulation-driven digital twins for personalized treatment planning and monitoring, with a particular focus on transcranial focused ultrasound.

  • Medical AI

    Medical AI can extract clinically relevant information from complex imaging and physiological data. However, purely data-driven models may overlook the physical mechanisms that govern treatment response. We combine multimodal learning from CT and MR images, tissue segmentation, and physics-based prediction to support safer and more personalized therapies.

Outside the Lab

  • Rock music and festivals. Pokémon. Contemporary art. And a good highball!

Resume

Education

  1. Yonsei University

    Mar 2023 — Present

    Integrated M.S./Ph.D., Compuational Science and Engineering

  2. Ewha Womans University

    Mar 2019 — Feb 2023

    B.S., Division of Mechanical and Biomedical Engineering
    (Minor: Computer Science and Engineering)

Research experiences

  1. Computational Methods in Medical Engineering Lab

    Mar 2023 — Present

    Researcher (Advisor: Prof. Kyungho Yoon)
    Deep learning-based forward simulation

  2. Neuromodulation and Tissue Engineering Lab

    Jan 2024

    Visiting Scholar (Advisor: Prof. Seung-Schik Yoo)
    Forward simulation of tFUS using FDTD and PSTD

  3. Computational Methods in Medical Engineering Lab

    Jun 2022 — Feb 2023

    Undergraduate Researcher (Advisor: Prof. Kyungho Yoon)
    Deep learning-based inverse simulation

  4. Medical Imaging & Learning Lab

    Jan 2021 — Dec 2021

    Undergraduate Researcher (Advisor: Prof. Taehoon Shin)
    Deep learning-based body tissue segmentation

Honors & Awards

  1. Runner-up Best Paper Award in MICCAI 2025 Workshop on DT4H, 2025.

  2. 2025 KSME Bioengineering Division Best Paper Awards, 2025.

  3. 27th Yonsei CSE Poster Exhibition, Award of Excellence (The 1st Prize), 2025.

  4. 22th Yonsei CSE Poster Exhibition, Award of Excellence, 2023.

  5. 2022 KSES Energy Data and AI Utilization Idea Contest, Award of Encouragement, 2022.

Skills

  • Python
    80%
  • MATLAB
    70%
  • C
    40%

Publications

Journal

  1. 1. M. Seo, H. You, M. Seol, SS. Yoo, S. Lee, K. Yoon, Optimization of acoustic lens design for transcranial holographic ultrasound using an active learning-based genetic algorithm, Journal of Computational Design and Engineering, 2026;13:1-20.

  2. 2. M. Seol, M. Seo, M. Shin, SS. Yoo, K. Yoon, Deep learning based real-time phase-amplitude correction for phased array transducers in transcranial focused ultrasound, Journal of Computational Design and Engineering, 2026;13:95-115.

  3. 3. M. Shin, M. Seo, H. Choi, J. Jung, K. Yoon, Local stress fields prediction using global displacement through Fourier Neural Operators, Journal of Computational Design and Engineering, 2025;12:21-40.

  4. 4. M. Seo, M. Shin, G. Noh, SS. Yoo, K. Yoon, Multi-modal networks for real-time monitoring of intracranial acoustic field during transcranial focused ultrasound therapy, Computer Methods and Programs in Biomedicine, 2024;257:108458.

  5. 5. M. Shin, M. Seo, S. Cho, J. Park, JH. Kwon, D. Lee, K. Yoon, PhysRFANet: Physics-guided neural network for real-time prediction of thermal effect during radiofrequency ablation treatment, Engineering Applications of Artificial Intelligence, 2024;138:109349.

  6. 6. M. Shin, M. Seo, SS. Yoo, K. Yoon, tFUSFormer: Physics-guided super-resolution Transformer for simulation of transcranial focused ultrasound propagation, IEEE Journal of Biomedical and Health Informatics, 2024;28:4024-4035.

  7. 7. M. Shin, M. Seo, K. Lee, K. Yoon, Super-resolution techniques for biomedical applications and challenges, Biomedical Engineering Letters, 2024;14:465-496.

Conference Presentations

  1. 1. H. Ghafoor, M. Seo, S. Cho, K. Yang, J. Lee, K. Yoon, SMILE: A Structured Multi-Agent Clinical Reasoning for Liver Ultrasound Understanding, Winter Conference on Applications of Computer Vision, 2027.

  2. 2. M. Seo, H. Ghafoor, M. Seol, S. Cho, K. Yoon, tFUSOperator: Operator Learning for Transcranial Focused Ultrasound Digital Twins, Digital Twin for Healthcare Workshop at MICCAI, 2026.

  3. 3. M. Seo, M. Shin, G. Noh, SS. Yoo, K. Yoon, Acoustic Simulation with Deep Learning for Low-intensity Transcranial Focused Ultrasound Digital Twins, Digital Twin for Healthcare Workshop at MICCAI, 2025.

  4. 4. M. Seo, M. Shin, G. Noh, SS. Yoo, K. Yoon, Multi-modal deep learning for real-time monitoring of intracranial acoustic field in transcranial focused ultrasound, International Society for Therapeutic Ultrasound, 2025.

  5. 5. M. Seo, K. Yoon, Deep Learning-based Denoising of Monte Carlo Simulation for Laser-Tissue Interaction Modeling, Korean Society of Mechanical Engineers Bioengineering Division, 2025.

  6. 6. M. Seo, S. Lee, K. Yoon, Deep Learning-based Lens Design Method for Transcranial Holographic Ultrasound, Korean Society of Mechanical Engineers Annual Meeting, 2025.

  7. 7. M. Seo, M. Shin, G. Noh, K. Yoon, Real-Time Simulation of tFUS using Multimodal Swin Transformer, Korean Institute of Communications and Information Sciences Summer conference, 2024.

  8. 8. M. Seo, M. Shin, G. Noh, K. Yoon, Multi-modal Generative Adversarial Network for transcranial focused ultrasound simulation, Korean Institute of Communications and Information Sciences Fall conference, 2023.

  9. 9. M. Seo, M. Shin, K. Yoon, Real-time pressure field monitoring of transcranial focused ultrasound using Generative Adversarial Network, Korean Society of Mechanical Engineers Bioengineering Division, 2023.

  10. 10. M. Seo, K. Yoon, Generative Adversarial Network-based real-time pressure field monitoring of transcranial focused ultasound, Annual Meeting of the Korean Society for Industrial and Applied Mathematics, 2022.

  11. 11. M. Seo, K. Yoon, Thermal energy digital twin model for building performance monitoring, Korean Solar Energy Society Autumn Annual Conference, 2022.

Contact

Advanced Science and Technology Center (ASTC) 616, Yonsei University, South Korea
You can reach me via email at islandz@yonsei.ac.kr.