Research

• AI for bioengineering and biology: Graph neural networks for molecular and cellular systems, gene regulatory network inference, and single-cell omics analysis.
• Protocol-aware active learning: Bayesian optimization frameworks that autonomously drive scientific experiments and support data-efficient exploration of experimental conditions.
• Presentations: Poster at AutoML 2025 on Object-Flow Machine Learning; oral presentation at the RIKEN BDR Student Symposium 2025; poster at the Spring School for Theoretical Biology 2025.