I teach business and engineering students at the undergraduate, master's, doctoral, and executive levels. Here you'll find a summary of my classroom methods along with course information.
Many students arrive in quantitative classrooms convinced that mathematics is out of reach. I believe the opposite: with the right approach, every student can experience the insight and excitement that quantitative analysis makes possible. Quantitative education should be available to everybody, regardless of background. In my classroom, complex concepts are made clear, and symbols and equations never obscure understanding. I put ideas into plain terms that spark curiosity and insight.
Accessible quantitative education requires two things: stylized analysis and clear-cut delivery. Stylized analysis begins with a simplified version of a complex problem. It strips away detail until the essential tradeoffs are visible. Through guided investigation, students discover how to navigate these tradeoffs, then scale their insights to more complex settings. The result is a heuristic that not only clarifies the theory but also equips students to make better real-world decisions.
Clear-cut delivery transforms quantitative instruction into an accessible and engaging experience. Rather than static symbols on a chalkboard, I use interactive and visual methods. Random variables come alive through simulation, and calculus concepts are conveyed through intuitive Monte Carlo–style exercises. These methods replace intimidation with discovery, turning abstract mathematics into insight students can grasp.
I design my teaching to create “Aha!” moments. These occur when complex ideas suddenly become clear. Through stylized analysis and clear-cut delivery, I tame what seems complex or intimidating and turn it into insight that students from all backgrounds can access. These insights stay with students long after the course ends.
This freshman seminar makes math personal. What should we do (or not) in a day or a lifetime? What amount of new and familiar is most fulfilling? How much mess is ok? This isn't a class for math majors. It's a course for everyone on thinking algorithmically, on learning about the fundamental structures of the problems we face, and ultimately on discovering something about ourselves.
I have always felt more comfortable with numbers and equations than with words and sentences. That comfort led me to study mathematics deeply. Over time, I came to see that mathematics has as much to say about living as philosophy or literature.
Yet students rarely encounter this perspective. They dive into novels to see the world differently, and they mine essays and poetry for insight. By contrast, the quantitative lens they bring to class is often nearsighted. We give them end-of-chapter problems and a handful of applications, but the connection to their personal lives remains so distant that it is out of focus. I wanted to change that.
Algorithms to Live By is my effort to bring the quantitative lens into everyday experience. The course invites students to view personal and lifelong challenges through mathematics in a way that is accessible, engaging, and transformative.
This doctoral-level course focuses on sequential decision making under uncertainty. Such problems arise across business, engineering, the sciences, and health care, where decision makers must identify alternatives that perform well not only in the present but across an entire horizon. Because sequential decision problems span so many domains, they are studied within multiple disciplines.
The course draws on advances in optimal control, Markov decision processes, and reinforcement learning to address core topics in modeling, policy design, and dual bounds. It will appeal to students who wish to connect deterministic optimization techniques with stochastic solution strategies, bridging classical methods with modern approaches.
Although Saint Louis University does not maintain a doctoral program in reinforcement learning, I have had the opportunity to teach stochastic dynamic programming at universities around the world. These experiences have reinforced my conviction that rigorous theory, taught with clarity, can equip doctoral students to make lasting contributions across domains.
How to match supply with demand at the firm level. Design, performance, and improvement of operations in manufacturing and service settings.
Undergraduate & Master's
How to match supply with demand across businesses. Managing the production, storage, and distribution of goods across a network of suppliers.
Master's
How to use predictive and prescriptive analytics to guide the allocation of limited resources. Quantitative methods to transform data into insight for better decision making.
Master's
How to hedge against the unknown by laying down resources across a range of outcomes. Why your forecast is wrong and what to do about it.
Executive
How to reduce, buffer, and pool variability. Operational strategies to hedge against fluctuations in supply and demand.
Executive