Ho-min Park

Ho-min Park

PhD in Computer Science Machine Learning Researcher

About

Ho-min Park

Computer Science Engineering PhD with deep expertise in machine learning, spanning from foundational concepts to state-of-the-art architectures including CNNs, Transformers, GANs, and Diffusion models.

Research Focus

I specialize in applying machine learning to diverse domains:

  • Medical Imaging: Developing interpretable AI models for MRI-based diagnosis (Rotator Cuff Tears)
  • Bioinformatics: CRISPR-Cas systems optimization and protein structure prediction
  • Affective Computing: Multimodal stress detection and sentiment analysis
  • Environmental Science: Automated microplastics detection using deep learning
  • Industrial Systems: AI-powered measurement systems for logistics

ML Education & Curriculum Design

AI Vacation School (AIVS) - Founder & Lead Instructor (2021 - 2025)

Ghent University Global Campus

  • Founded and led a volunteer-driven ML bootcamp (100+ hours per session) for undergraduate students over 7 sessions across 4 years
  • Designed a 20-lecture curriculum spanning: regression, MLPs, CNNs, RNNs, Transformers, LLMs, GANs, Diffusion models, unsupervised learning, and XAI (SHAP)
  • Created three types of original materials: presentation slides, Google Colab notebooks, and Excel worksheets
  • Mentored over 10 entry-level AI researchers

Education

Degree Institution Year Details
Ph.D. Computer Science Engineering Ghent University, Belgium 2018-2025 Dissertation: “Advancing Detection through Deep Learning for Industrial, Environmental, and Health Applications”
M.S. Computer Engineering Ajou University, South Korea 2016-2018 Thesis: “Cited count prediction modeling from large citation network”
B.S. Computer Science and Engineering Ajou University, South Korea 2010-2016 Major in Computer Science and Engineering

Skills

ML & AI Expertise

  • Architectures: CNN, RNN, LSTM, GRU, Transformer, GAN, Diffusion Models, MLPs
  • Techniques: Supervised/Unsupervised Learning, Transfer Learning, Feature Engineering, Model Explainability (SHAP, CAM)
  • Tools: PyTorch, PyTorch Lightning, Weights and Biases (WandB), Python

Languages

  • Korean: Native speaker
  • English: Professional proficiency (TOEIC 860, OPIc IH)

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