One of the recurring challenges in mathematics education is the perception of math as inaccessible and overly abstract. I strive to counter this by linking mathematical concepts to real-world applications and interdisciplinary themes: calculus to classical mechanics, probability theory to financial modeling, and linear algebra to optimization problems in machine learning. I believe applied mathematics courses must include hands-on computational components, and I often design assignments where students develop small-scale algorithms or simulations to reinforce mathematical principles.
Instruction experience
| Role |
Institution |
Course |
When |
| Instructor & course designer |
Northwestern University |
Agentic AI for Scientific Computing (graduate, project-based; funded frontier-model access for enrolled students) |
Fall 2026 (forthcoming) |
| Instructor |
Northwestern University |
Engineering Analysis 4: Differential Equations & Numerical Methods |
Fall 2023 |
| Summer Instructor |
University of Pittsburgh |
MATH 290: Introduction to ODEs and Applications |
Summer 2017 |
| Teaching Fellow / TA |
University of Pittsburgh |
Calculus I–III, Business Calculus, ODEs |
2016 – 2021 |
Mentoring
I have mentored six undergraduate students and two graduate/master’s students across Northwestern, UC Berkeley, CMU, IIST, and the University of Pittsburgh, co-authoring papers with several of them. My mentorship philosophy is to nurture young talent and foster their growth as independent researchers, especially around real, usable research software.
Courses I am prepared to offer
- Agentic AI for Scientific Computing — principled use of frontier AI agents for numerical solvers, parameter estimation, and HPC orchestration; validation of AI-generated scientific artifacts; cost-aware workflows. First offering at Northwestern in Fall 2026; adaptable to undergraduate audiences.
- Introduction to Scientific Machine Learning — equation learning (SINDy / DAE-FINDER family), PINNs, neural ODEs, with weekly Python projects.
- Optimization Techniques for ML and Data Science — first- and second-order methods, convex and non-convex optimization, automatic differentiation.
- Numerical Methods for Scientific Machine Learning — classical numerical analysis meeting modern ML applications.
- Mathematics and Optimization in Trading and Finance — options pricing, hedging, risk sensitivities, and portfolio optimization, drawing on my industry quant experience.
- Standard offerings: numerical analysis, ODEs/PDEs, optimization, probability and statistics, calculus, and engineering-mathematics service courses.
Teaching is also an opportunity to deepen my own understanding: preparing courses involves revisiting familiar material from new perspectives, reinforcing my grasp of the subject and providing fresh insights.