Research Interests
Lab Automation
Enabling scalable, reproducible food experimentation through advanced sensing, robotic handling, and high-throughput physical characterization.
Key Areas:
- • Advanced material sensing
- • Contactless mixing systems
- • High-throughput texture analysis
Agents for Food Design
Developing intelligent agentic systems that reason, design, and monitor food products across formulation, nutrition, and quality dimensions.
Key Areas:
- • Sugar-reduction agents
- • Meal monitoring agents
- • Quality inspection agents
Structural Representation of Food Materials
Creating data-driven representations of food structure that connect composition, processing, and functionality.
Key Areas:
- • Unified food structure datasets
- • Multimodal machine learning
- • Structure-property modeling
Emerging Scientific AI Models for Food
Connecting the most relevant foundation models to experimental food science data across molecular, microstructural, and macroscopic scales.
Key Areas:
- • Protein foundation models for bio-functionality
- • Chemical language models for flavor and aroma identifications
- • Microscopy foundation models for food microstructure