When an emergency like extreme weather strikes, making the right decision in real time can become a matter of life and death. Yet the mathematical optimization tools used to calculate the safest evacuation routes or allocate emergency resources typically require significant computing power and specialized expertise in programming and mathematics.
Haizhao Yang, an associate professor with joint appointments in the University of Maryland's Department of Mathematics and Department of Computer Science, wants to change that. Supported by a $20,000 seed grant from the Artificial Intelligence Interdisciplinary Institute at Maryland (AIM), Yang is developing OptimAI—a cloud-based platform that uses artificial intelligence to make advanced optimization accessible to anyone with a smartphone.
“In real-world applications, not everybody has the expertise to use optimization to find the best solutions, especially for resource allocation,” says Yang, who has an affiliate appointment in the University of Maryland Institute for Advanced Computer Studies (UMIACS) and is also a core member of the UMD Center for Machine Learning. “We aim to use AI to lower this barrier so that anyone can make better decisions, particularly in situations related to sustainable decision-making.”
The idea emerged shortly after the release of large language models (LLMs) like ChatGPT. Yang and his team realized that the same natural language capabilities that allow people to converse with AI could also make sophisticated optimization tools available to non-experts.
He points to emergency situations—such as flash floods or rapidly spreading California wildfires—to illustrate the platform's potential. In these moments, people fleeing danger rarely have access to powerful computers. They have only their phones.
With OptimAI, a user could simply describe their surroundings by speaking into their device. The AI would interpret that natural language, incorporate real-time environmental data from sources such as radar and satellites, and automatically generate a mathematical optimization model to calculate the safest, most efficient evacuation route—all without requiring users to install specialized software or understand the underlying mathematics.
Building a system capable of generating reliable, life-saving recommendations requires combining rigorous mathematical theory with cutting-edge computer science, an intersection where Yang has extensive expertise.
Because LLMs are prone to hallucinations—producing confident but incorrect responses—using them in safety-critical situations presents significant challenges. Yang's team plans to ground the AI's outputs in mathematical theory, using it as a source of truth to improve the system's reliability.
“This is interdisciplinary research that really relies on math to make the AI tool trustworthy,” Yang explains. “With mathematical theory, we may be able to develop very robust, trustworthy AI to reduce hallucination.”
At the same time, the platform's computer science framework must seamlessly translate spoken language into executable optimization code, run those computations on cloud-based hardware and return results to users within seconds.
The one-year AIM seed grant will primarily support student researchers, including undergraduate computer science students affiliated with UMIACS, who will help test and refine the platform's core functionality. The team will also leverage UMIACS's GPU clusters to train and run computational models.
Yang hopes the project's early results will position the team to pursue larger grants from the National Science Foundation. Ultimately, he envisions a future in which mathematical optimization is no longer limited to researchers and specialized industries but is available to anyone who needs to make better decisions—simply by asking a question.
—Story by Diya Sharma, UMIACS communications group