Mathematics and Computer Science student at KAIST.

Competitive Mathematics: Bronze medalist at IMO, APMO, National Olympiad in Informatics and winner at Simon Marais Math Competition (Jane Street Prize).

Experience: Currently a Machine Learning Engineer Intern at HayanMind, building dynamic pricing and rare-event purchase prediction models with Transformer-based sequential architectures and Hawkes processes. Previously optimized inference performance for Hybrid LLM models at Huawei, and led the Samsung Health × Wellness AI student entrepreneur project at Samsung Lifenology Lab, building a multi-agent system that turns wellness guidance into personalized interventions members actually sustain. Published at AACL on evaluating LLMs in low-resource languages (KAIST Users & Information Lab), and conducted Samsung-collaborated IoT Insomnia classification research. Additional experience in stochastic process modeling and domain adaptation at Institute for Basic Science.

Expertise: Statistics, Mathematical problem solving, Data structures & algorithms. Research in data science, deep learning (CNN, LSTM, ResNet), mathematical modeling, and LLM inference optimization.

Interests: Geometric and statistical understanding of data, time series AI, quantitative finance , online and offline learning, deep learning .

Currently looking for: Quantitative finance, AI/ML engineering and research-oriented roles.