Eric (Xin) Wang, an assistant professor of computer science in The Robert Mehrabian College of Engineering at UC Santa Barbara, has received a 2025 J.P. Morgan Chase Faculty Research Award for his work on artificial intelligence (AI). The financial corporation’s highly selective and competitive awards program aims to spark impactful research collaborations between academia and industry, advancing cutting-edge AI methods to solve real-world problems. 

“This award is both an honor and an important extension of our group’s work on building AI agents that can reason, interact, and adapt in complex environments,” said Wang. “Personally, it is incredibly meaningful to see our ideas recognized outside the traditional tech ecosystem. Professionally, it validates our belief that AI agent research has a powerful role to play in high-stake domains like finance, and it strengthens our ongoing efforts to bridge fundamental AI research with real-world impact.”

Wang notes that financial market simulations still rely largely on simplified, rule-based agents that do not capture the richness of real investor behavior. His r group’s project aims to address those limitations by developing a Large Market Model (LLM) — a scalable, multi-agent simulation framework that allows AI-driven agents to reason, adapt, and interact much more like actual market participants. The model will enable researchers to study how individual decisions scale into market-wide dynamics, reproduce well-known financial phenomena, and evaluate how markets respond to shocks or policy changes.

“In practical terms, such a system could help institutions better understand systemic risk, evaluate regulatory strategies, and stress-test financial systems in safe, controllable virtual environments,” explained Wang, who joined the UCSB faculty earlier this year after serving as an assistant professor at UC Santa Cruz. 

The award will directly support a PhD student, who will build the core simulation environment, develop self-adaptive investor agents grounded in real financial data, and run large-scale experiments that would otherwise be extremely difficult or costly to run.  

Wang is the recipient of numerous previous honors, including three Alexa Prize Awards, best paper awards at the International Conference on Learning Representations (ICLR) and the Conference on Computer Vision and Patter Recognition (CVPR), and faculty research awards from Google, eBay, and Cisco.

“Our group values industry collaboration because it keeps our research grounded in real-world impact. But this J.P. Morgan Chase award is particularly meaningful because it’s our first major research award from a non-tech company,” said Wang, who completed his PhD in computer science at UCSB in 2020. “The award reinforces the broader relevance of our work and motivates us to deepen our engagement with domains where AI can meaningfully improve decision-making and societal outcomes.”

Industry awards like this, Wang added, are especially valuable for junior faculty because they offer flexible, early-stage funding that is often difficult to secure through traditional federal sources, especially for high-risk or exploratory ideas. 

“For students, these awards create pathways to internships, mentorship, and career opportunities,” he said. “For faculty, they enable co-development of datasets, benchmarking suites, workshops, and joint publications — efforts our group has already successfully carried out with industry partners. These awards often become catalytic investments that accelerate the growth and impact of a young research lab.”