Building Bridges Between Human Ingenuity and AI Innovation.
At the Foundation Models and Applications Lab (FMAL), we are developing intelligent AI collaborators designed to empower people, such as scientists, educators, and artists, in tackling the world’s most pressing challenges. By harnessing cutting-edge foundation models and scalable training platforms, our AI systems can rapidly adapt to diverse tasks, from accelerating drug discovery and combating climate change to transforming education into a more engaging, personalized experience.
The mission of the lab is to connect brilliant minds with innovative technology to create a better future together. We shape a future where AI enhances, rather than replaces, human potential. We believe every young person deserves to grow up in a world where AI helps people, not replaces them, and we’re working to make that future real.
Since its establishment in 2025, the Lab has been directed by Meng Jiang, Associate Professor of Computer Science and Engineering, and Xiangliang Zhang, the Leonard C. Bettex Collegiate Professor of Computer Science and Engineering.
Research Focus
Key characteristics of FMAL’s focus include:
- Chemistry & Polymer Discovery: The researchers of FMAL design AI models that can identify new chemicals and materials
- Social Sciences and Society: Researchers within FMAL develop trustworthy personalized foundation models for analyzing human behavior and social patterns.
- Lab Safety: FMAL builds AI models to predict and prevent accidents in laboratory environments.
- Fluid and Environmental Science: FMAL researchers design foundation models to analyze and simulate the movement of liquids and gases.
- Geoscience: FMAL creates AI models to tackle specialized challenges, such as studying Earth’s structure and composition and exploring planetary systems.
- Mental Health & Well-being: Researchers within FMAL support mental health research by designing AI models to detect and treat mental health conditions.
- Data Interpretation: FMAL builds and trains AI models to analyze and understand complex charts and data visualizations, increasing research accessibility.
