Hi, I’m Alex — an applied mathematician, educator, and scientific consultant.
I use mathematics, statistics, optimization, and scientific computing to help students and researchers understand difficult problems. I am particularly interested in teaching mathematics, statistics, data science, and collaborating on research in data science, life sciences, earth sciences, physics, and engineering.
I hold a Ph.D. in Applied Mathematics and Statistics from the Colorado School of Mines. My background combines mathematical research, university teaching and tutoring, scientific modeling, and several years of professional experience applying quantitative methods to real-world problems.
Teaching and Mentorship
I am currently pursuing adjunct, lecturer, and instructor opportunities in mathematics, statistics, and mathematically oriented data science. I enjoy helping students connect abstract mathematical ideas to physical systems, scientific questions, and problems they encounter in their own fields.
Courses I am especially interested in teaching include:
- Calculus and multivariable calculus
- Linear algebra
- Differential equations
- Probability and statistics
- Numerical analysis and scientific computing
- Optimization and operations research
- Mathematical modeling
- Data science taught from a mathematical and statistical perspective
My goal as an educator is to make mathematics rigorous without making it inaccessible. I want students to understand not only how a method works, but why it works, what assumptions it depends on, and when it is useful.
Current Scientific Consulting
I currently consult with Denver Life Sciences on the statistical modeling of physiological study data. My work uses structured and regularized regression methods to distinguish stable baseline differences among experiments from changes that occur within experiments over time.
The broader goal is to improve prediction and help researchers interpret complex physiological data in the context tissue hydration. This work combines statistical estimation, mathematical modeling, careful model validation, and close collaboration with domain experts.
Research Background
I completed my Ph.D. in Applied Mathematics and Statistics at the Colorado School of Mines, where my dissertation examined deep learning methods for large-scale problems in physics. My research brought together ideas from optimal transport, optimal control, inverse problems, numerical optimization, statistical learning, and scientific computing.
My graduate research included:
- Optimal transport and generative modeling: developing methods that connect continuous normalizing flows with Wasserstein gradient flows.
- Mean-field optimal control: designing computational methods for controlling large interacting systems, including simulated swarms of autonomous agents.
- Inverse problems and scientific imaging: using hyperspectral remote-sensing data to estimate mineral composition and support geologic interpretation.
- High-dimensional scientific computing: creating scalable approximation and optimization methods for problems that are difficult to solve using conventional numerical techniques.
These projects taught me how to formulate mathematical questions, design computational methods, evaluate their limitations, and communicate results to researchers from different disciplines. My publications are available through the Google Scholar link in the sidebar.
Professional Background
Before refocusing my career on teaching and scientific collaboration, I spent several years working as a senior data scientist and machine learning researcher. I worked at On The Barrelhead and later NerdWallet, where I developed statistical models, optimization methods, experiments, and quantitative decision systems.
I subsequently worked as a Senior Machine Learning Researcher at Launch Potato, serving as a technical resource for statistical modeling, experimentation, optimization, and machine learning research.
That experience gave me a practical understanding of how mathematical ideas move from theory to implementation. I am now most interested in using that background to support education, scientific research, and work that makes a meaningful contribution to the communities it serves.
Interests and Opportunities
I am currently interested in:
- Adjunct, lecturer, and instructor positions in mathematics, statistics, and data science
- Mentoring undergraduate students and supporting student research
- Collaborations in the life sciences, earth sciences, physics, and engineering
- Environmental and geophysical modeling
- Inverse problems, optimization, and scientific computing
- Selective consulting projects involving mathematical modeling and statistical research
I am especially interested in opportunities that combine teaching, mentorship, scientific inquiry, and service to the surrounding community. Please feel free to contact me if you would like to discuss a teaching opportunity, research collaboration, or mathematically interesting project.
Example Research
The animation below shows simulated quadcopters avoiding one another using methods from my research in mean-field optimal control.