Teaching & student mentoring
From guided examples to independent inquiry
My goal is to help students connect mathematics to what they already understand, then develop the confidence to use those ideas in unfamiliar problems.
Approach
I make abstract concepts accessible through concrete models, worked examples, and computation while preserving the reasoning that gives mathematics its explanatory power. Applications give students a reason to engage with the mathematics; computational experiments help them examine its consequences.
I adapt computational work to students’ preparation. Students should be able to explain a method and interpret its output, not simply run code. In both the classroom and research mentoring, I provide enough guidance for students to make progress and enough space for them to make discoveries of their own.
Courses taught
University of Houston
- Fall 2027MATH 3363 · Introduction to Partial Differential Equations
scheduled.
- Spring 2027MATH 6397 · Equation-Based Learning: Theory and Application
Selected topics course; scheduled.
- Fall 2026MATH 3363 · Introduction to Partial Differential Equations
- Spring 2026MATH 3331 · Intermediate Differential Equations
- Fall 2025No Teaching (On Leave)
- Spring 2025MATH 3331 · Intermediate Differential Equations
- Fall 2024MATH 4364 · Introduction to Numerical Analysis in Scientific Computing
Illinois Institute of Technology
- Spring 2024MATH 569 · Statistical Learning
- Fall 2023DS 151 · Introduction to Data Science
- Spring 2023MATH 332 · Elementary Linear Algebra
- Fall 2022DS 151 · Introduction to Data Science
Created this new course for the B.S. in Data Science curriculum.
Texas A&M University
- Spring 2022ECEN 744 · Scientific Machine Learning
Co-created and co-taught this new course in Electrical and Computer Engineering.
Johns Hopkins University
- Fall 2019Calculus II for Biological, Social and Management Sciences
- Summer 2019Calculus II for Biological, Social and Management Sciences
- Spring 2019Calculus I for Biological, Social and Management Sciences
- Fall 2018Introduction to Mathematical Programming
- Spring 2018Applied Mathematics for Engineering
Graduate level.
- Fall 2017Scientific Computing: Linear Algebra
- Spring 2017Applied Mathematics for Engineering
Graduate level.
Summer schools & REUs
- Summer 2025Introduction to Data Science and Machine Learning
Lectures for the University of Houston REU.
- Summer 2025Introduction to Scientific Machine Learning
Three-day lecture series for the Texas A&M Institute of Data Science summer school.
- Summer 2023Scientific Machine Learning and Social Science Modeling
Lecture series for the Illinois Tech REU.
Doctoral mentoring
Current
- Joseph Dingillo
UH MathematicsDeep learning for conservation laws and computational physiologyPrimary co-adviser.
- Kamrun Milly
UH MathematicsMathematical modeling of social dynamics, crowd behavior, and urban crimeCo-adviser and thesis committee chair.
- Chi-An Chen
Illinois TechData-driven modeling of Keller-Segel-type PDEs and SPDEsResearch co-mentor.
- Baoli Hao
Illinois TechData-driven modeling of complex systems and their emergent behaviorCo-adviser.
Completed
- Ziheng Guo
Ph.D. 2026System Identification of Stochastic DynamicsSole adviser; advising began at Illinois Tech. Placement: Assistant Vice President and quantitative researcher at Wells Fargo.
- Jason Miller
Ph.D. 2021Learning Interaction Kernels and Emergent Behaviors for Second-Order Interacting Agent SystemsResearch co-mentor at Johns Hopkins. Placement: Quantitative Researcher at Tower Research.
Undergraduate mentoring
- 2025–2026Egemen Senvardarli · University of Houston
Research co-mentor and senior honors thesis committee member.
- Summer 2023Jean-Yves Thomason, Cameron Hatler, and Camryn Rhude
Sole adviser for Illinois Tech REU projects on modeling social problems in Chicago.
- 2023–2024Trenton Gerew · Illinois Tech
Sole adviser. The project contributed to research on concurrent clustering, flocking, and synchronization; placement in the mathematics Ph.D. program at the University of Kansas.