How I teach
In office hours for Numerical Methods at UT Dallas, a few students showed me Python code for the Colebrook pipe-friction equation. After two Newton steps it returned NaN, and they were hunting for a bug. There wasn’t one. I asked them to plot the function and mark each step by hand, and the picture told the story: their starting guess had pushed the method to a negative friction factor, where the equation means nothing. They knew how to run Newton’s method. They did not yet know when it would fail. Much of my teaching is about closing that gap.
1
Say it in words first
Before solving a model, students put each assumption into plain words and guess how a change will move the answer, then test the guess. “The machine runs eight hours a day” comes before the algebra, and the next question is what one more hour is worth.
2
A little less help each time
I explain an idea, work one example together, then hand over a similar problem with less support. Students in my research projects follow the same path: use a worked model, adapt it to a new case, then defend their own assumptions.
3
The reasoning, not only the answer
An AI tool can explain the Colebrook failure in seconds, but following an explanation is not the same as being able to use it. So I give credit for how a problem is set up and what the result means, and students who use generated code show how they checked it.
Open course
Free, interactive, no account
Energy Systems, Step by Step
A course for high-school students, undergraduate and graduate students, and curious adults. It starts with running a small town’s power grid for one real day and builds up to writing, solving and questioning the optimization models that grid operators and planners use, then to designing a study of your own.
112 lessons live Four tiers: Foundations, Analyst, Modeler, Researcher Real 2025 hourly grid data (EIA-930) An optimization solver that runs in the browser
The three principles in the lessons
- Say it in words first: guess what will happen, then try it in a live model.
- A little less help each time: practice goes from worked, to partly worked, to from scratch.
- The reasoning, not only the answer: learners set up models and explain what the solution means.
Instruction
ECE 685
Course
Power Systems Design
Teaching assistant
The University of Texas at Dallas
Fall 2022
Engineering Optimization Numerical Methods
Texas State University
2019–2021
Probability & Statistics Operations Research
Mentoring
Mentored undergraduate researchers at The University of Texas at Dallas, 2022–2025, including through the Research Experience for Undergraduates (REU) program. Their projects covered techno-economic analysis of renewable and hydrogen systems. In these projects, students moved from following a worked model to adapting it and defending their own assumptions.
Summer 2022
Undergraduate student Mirabella Herrera (UT-Dallas) REU
Techno-economic analysis of co-located wind and green hydrogen systems.
Summer 2022
High school student TAST STEM Summer Research Program
Co-advised, focusing on techno-economic analysis modeling.
Summer 2023
Undergraduate student Jonathon Matutes (University of Indianapolis) REU
Techno-economic analysis of potential offshore wind energy development along the northeastern US coast.
Summer 2024
Undergraduate student Zaina Anarwala (UIUC) REU
Techno-economic analysis of potential offshore wind energy development in the US Gulf of Mexico.
Spring 2025
Undergraduate student Kevin Sun (UT-Dallas)
A research project focused on techno-economic analysis of nuclear and hydrogen systems.
Interactive explainers
Each explainer is a small interactive model on a project page that shows one mechanism from my research: move a slider and see why the result changes. They are designed to double as teaching material for techno-economic analysis, optimization and uncertainty quantification. The numbers are illustrative, not study results.
Investment appraisalRevenue stacking for an existing and a new nuclear plantNuclear–hydrogen
Policy designHow a per-MWh production credit changes the value of steam for coolingSMR cogeneration
Carbon accountingRe-timing electricity use with hourly emission intensityLoad regulation
For a full path from first principles to building models, see the open course Energy Systems, Step by Step.