Current Research Focus

AI and Deep Learning for Healthcare Development of advanced multimodal AI algorithms that integrate image, signal, and clinical data for real-time diagnostics, disease prediction, and personalized healthcare. We also explore aging-related AI, including models for age estimation, biological aging prediction, and facial recognition-based health analytics.
AI-Integrated Point-of-Care Testing (POCT) Implementation of Sample-to-Answer Diagnostic Systems that merge CRISPR, LAMP, and immunodiagnostic platforms with smartphone-based and portable AI systems to enable rapid, on-site molecular diagnostics.
Nanofilter-Based Sample Preprocessing Development of high-efficiency nanofilter and enrichment technologies for the purification of complex biological fluids, enabling ultra-sensitive detection of nucleic acids, proteins, and exosomes in real-world samples.
Organoid and Molecular-Level AI Diagnostics Application of AI to organoid-based disease modeling for drug response prediction, mechanistic analysis, and personalized medicine. We utilize AI-driven image and omics interpretation to accelerate organoid research and therapeutic screening.
AI-Assisted LNP Synthesis and Drug Delivery Design of AI-guided lipid nanoparticle (LNP) synthesis platforms that optimize formulation parameters for gene and mRNA delivery. Our algorithms leverage deep generative models and transformer architectures to enhance synthesis yield and stability prediction.

Research we’ve done

1.AI-assisted diagnostics: mobile healthcare

We propose a groundbreaking technology that significantly enhances diagnostic capabilities using AI. Our technology enables on-site diagnostics through smartphone or video analysis alone, without the need for external devices or equipment.