Past Seed Grants
AI Bridge Summit and Seed Grant
The summit combined tutorial training, spotlight presentations, poster sessions, and structured matchmaking to foster interdisciplinary collaboration and drive measurable progress toward innovative research proposals. Over the course of one week, the workshop focused on a different aspect of AI research each day, enabling Texas A&M researchers to gain insights into the latest methodologies, technologies, and innovations in the field. Topics covered included supervised and unsupervised learning, large language models, computer vision, reinforcement learning, and physics-informed machine learning. The goal of this workshop was to connect foundational and applied AI research across Texas A&M by pairing experts in AI methods with domain researchers, preparing them to submit proposals for innovative one-year research projects that have the potential for external funding.
Seed Program for AI, Computing, and Data Science (SPAICD)
In 2024, the Texas A&M Institute of Data Science (TAMIDS) created the Seed Program for AI, Computing, and Data Science (SPAICD) to fund projects that enhance novel ideas, foster innovation, initiate new collaborations, and develop results to support further funding opportunities within the fields of data science, artificial intelligence, and machine learning. Out of the 39 submissions, we awarded the top ten reviewed proposals, spanning twelve departments in five colleges, schools, and divisions, totaling 22 funded researchers. These projects covered various topics, including autonomous vehicles, personalized medicine, and environmental science, exemplifying TAMIDS’s mission to support innovative research projects and foster new multidisciplinary partnerships.
Funded Proposals
Leveraging Deep Learning and Big Data Analytics to Enhance Tropical Cyclone Seasonal Predictions with Spatial Heterogeneity
- Dan Fu, Assistant Professor, Atmospheric Sciences
- Zhengzhong Tu, Assistant Professor, Computer Science & Engineering
AIMS-META: AI-enhanced designing of Manufacturability-aware and Symmetry-driven METAmaterials for enhanced mechanical performance
- Suparno Bhattacharyya, Texas A&M Institute of Data Science
- Samuel Gonumakulapalle Lodi, Industrial & Systems Engineering
- Satish Bukkapatnam, Industrial & Systems Engineering
Optimizing Urban Vehicle Fleet Through Multi-Agent Reinforcement Learning: Integrating Built Environment Factors and Real-Time Road States
- Xinyu Li, Department of Landscape Architecture & Urban Planning
- Suphanut Jamonnak, Texas A&M Institute of Data Science
Enhancing Privacy and Security in Machine Learning and Data Science
- Alex Sprintson, Department of Electrical & Computer Engineering
- Krishna Narayanan, Department of Electrical and Computer Engineering
Mapping Cool Corridors: Thermal Exposure and Shade Dynamics for a Walkable TAMU
- Cuiling Liu, Department of Landscape Architecture & Urban Planning
- Yapping Xu, Department of Environmental and Geosciences, Sam Houston State University
Leveraging AI to Enhance the Urban Planning Process: Detecting Opportunities in Comprehensive and General Plans
- Thomas Sanchez, Department of Landscape Architecture & Urban Planning
- Lei Zou, Department of Geography
- James Caverlee, Department of Computer Science & Engineering
AutoFLUKA: An AI-Assisted Framework for Automating Monte Carlo Simulations
- Yang Liu, Department of Nuclear Engineering
- Jian Tao, Department of Visualization
Cross-Domain Knowledge Transfer for Extrapolation in Natural Hazards Engineering
- Victoria Crawford, Department of Civil & Environmental Engineering
- Stephanie Paal, Department of Civil & Environmental Engineering
An Integrated Digital Twin and Computer Vision Model for a Proactive Work Zone Intrusion Warning System
- Namgyun Kim, Department of Construction Science
- Yalong Pi, Texas A&M Institute of Data Science
Toward Smart Orthopedic Surgery Planning by using Physics-Informed Machine Learning
- Jaesung Lee, Department of Industrial & Systems Engineering
- Yuxiao Zhou, Department of Mechanical Engineering