Bio

Jake Benhart is a Ph.D. student in Operations Research at North Carolina State University. He enjoys using operations research to solve real-world problems. Specifically, he studies how quantitative models guide important decisions. Although he has always loved numbers and analytics, he feels most energized by real impact. As a child, he loved basketball and admired strong shooters. Because inefficiency bothered him, he valued players who used each shot wisely. Therefore, that mindset grew into a passion for modeling complex systems. Over time, he focused on improving performance and efficiency through data.

Currently, his research centers on agent-based simulation in ride-hailing and emerging autonomous vehicle markets. In addition, he works to bring simplified models into the classroom. He uses these tools to support AI-assisted learning. Moreover, he studies mobility and logistics because these problems affect daily life. For example, almost everyone understands how to request a ride. At the same time, he explores the use of AI in education to help students learn more deeply.

Looking ahead five to ten years, he plans to pursue a research-focused career. For instance, he may serve as a faculty member or research scientist. In either role, he hopes to work in collaborative, high-engagement environments. There, teams will use operations research and AI to improve complex systems.

Outside of work, family and faith guide his life. Therefore, he values quality time with family and friends, often while watching sports. Likewise, he brings a team-oriented mindset into his professional life. As a result, he strives to build a positive culture of collaboration and continuous improvement. Ultimately, he wants students and colleagues to see his passion for teamwork and growth.

Advisor

Michael Kay

ORCID Profile

https://orcid.org/0009-0007-5642-1508

View Some of Jake Benhart’s Work

https://jbenhart44.github.io/

Area(s) of Expertise

Jake Benhart’s dissertation studies agent-based modeling of diverse participants in ride-hailing markets. Specifically, he examines how different drivers and riders make strategic decisions. He also studies how instructors can use these models in the classroom. This work combines stochastic modeling, optimization, and simulation methods. Together, these tools help him analyze behavior in current mobility systems. In addition, they allow him to explore future systems with autonomous vehicles. Therefore, his research connects technical modeling with practical applications.

Moreover, he seeks to turn these informed models into effective teaching tools. As AI enters classrooms, instructors must guide students in using it wisely. Therefore, he aims to model responsible and thoughtful AI integration. He designs courses that use AI to support, not replace, learning. For example, he uses ride-hailing systems like Uber as familiar case studies. Then, he builds AI-interactive learning environments around those models. As a result, students engage more deeply with complex ideas.

Ultimately, his research seeks to improve how educators integrate AI into the classroom. At the same time, it uses AI-driven models to study real-world problems. Furthermore, it explores how mobility markets may change with autonomous vehicles. Methodologically, his work emphasizes agent-based simulation. In addition, he draws on stochastic modeling and optimization tools. Increasingly, he also studies AI-enabled learning analytics for classroom use.