My teaching emphasizes the connection between mathematical structure and computational implementation. In my courses, students learn to translate mathematical models into reproducible, testable programs, with attention to numerical stability, probabilistic reasoning, algorithmic design, and scientific computing practice.
I aim to help students develop both conceptual understanding and practical fluency. This means moving between mathematical formulations, algorithms, implementation, debugging, visualization, and interpretation. Across my courses and mentoring, I emphasize that computational science is not only about writing code, but about using computation carefully to answer scientific and mathematical questions.
ISC 4933/5935: Computational Probabilistic Modeling - Spring 2023, Spring 2024, Spring 2025, Spring 2026
Computational Probabilistic Modeling is offered as a joint undergraduate/graduate course, with different expectations and requirements for students enrolled at each level. The course introduces probabilistic programming and modeling for modern data science, machine learning, and scientific computing applications. Topics include probability and learning theory, Bayesian modeling, likelihoods and priors, predictive inference, graph-based methods, neural networks, dimensionality reduction, and algorithms for large-scale data analysis. The course emphasizes both mathematical foundations and implementation in Python.
ISC 4304C: Programming for Science Applications - Spring 2024, Spring 2025, Spring 2026
This course introduces scientific programming for students in computational science and related fields. Students learn to translate mathematical expressions and algorithms into working code, with attention to reproducibility, debugging, documentation, numerical stability, data analysis, and visualization. The course emphasizes practical implementation in Python together with compiled-language concepts relevant to high-performance scientific computing.
Directed Research, Honors, and Dissertation Supervision
I also supervise undergraduate research, directed individual studies, honors research, graduate dissertation research, and independent student projects in scientific computing.
APMA 940: Mathematics of Data Science - Simon Fraser University, Summer 2021
A graduate course on theory and algorithms for data science, with emphasis on mathematical aspects of supervised learning, unsupervised learning, dimension reduction, deep learning, algorithms for large-scale data, and foundations of learning.
MATH 475: Mathematical Topics in Data Science - Simon Fraser University, Spring 2022
An undergraduate topics course on the mathematics of data science, including machine learning, compressed sensing, clustering, randomized numerical linear algebra, complex networks, and random graph models.
MATH 232: Applied Linear Algebra - Simon Fraser University, Spring 2019 and Spring 2020
An undergraduate course covering linear systems, matrices, determinants, vector spaces, linear transformations, bases, complex numbers, eigenvalues and eigenvectors, diagonalization, inner products, orthogonality, and least squares.
MATH 141: Calculus I - University of Tennessee, Fall 2017
An undergraduate course in single-variable differential calculus for students in science, engineering, mathematics, and computer science.
MATH 231: Differential Equations - University of Tennessee, Fall 2016
Teaching assistant for a large undergraduate differential equations course covering first-order equations, linear equations, constant-coefficient equations, Laplace transforms, and series solutions.
I mentor undergraduate and graduate students on projects in scientific computing, approximation theory, compressed sensing, scientific machine learning, uncertainty quantification, inverse problems, and data-driven modeling.
Shifur Rahman Shakil - PhD student, Department of Scientific Computing, Florida State University. Co-supervised with Hristo Chipilski. Current work includes compressed sensing and deep learning surrogate models for cloud microphysics and Bayesian inverse problems.
MJ Shooshtari - PhD student, Department of Scientific Computing, Florida State University. Current work includes adaptive sampling and scientific machine learning for challenging data-driven modeling problems.
Latira Campbell - PhD student, Department of Mathematics, Florida State University. Co-supervised with Sanghyun Lee. Current work includes active learning strategies for forward and inverse problems in scientific computing and uncertainty quantification.
Kunal Kanawade - PhD student, Department of Scientific Computing, Florida State University. Current work includes compressed sensing and sparse approximation methods for parametric PDEs, including extensions to more complicated nonstationary and nonlinear models.
I currently mentor undergraduate students on honors, practicum, and independent research projects in scientific computing and machine learning. Recent honors and practicum projects have included generative compressed sensing, sports analytics, and scientific machine learning.
Alexander DeLise - Honors in the Major research. Current work includes conditional generative compressed sensing and active sampling for image recovery from limited Fourier measurements.
Baylor Boyers - Undergraduate practicum research.
Juan M. Cardenas - PhD student in Mathematics, Simon Fraser University. Co-supervised with Ben Adcock, Spring 2020-Summer 2023. Research area: adaptive sampling strategies for function approximation in high dimensions.
Sebastian Moraga - PhD student in Mathematics, Simon Fraser University. Co-supervised with Ben Adcock, Spring 2020-Summer 2024. Research area: optimal and efficient algorithms for learning high-dimensional, Banach-valued functions from limited samples.
Miles Rosoff - Honors in the Major research. Research project on using machine learning for optimal pitch sequencing in baseball, with emphasis on predictive modeling, game context, and data-driven analysis of pitch selection, Florida State University, January-May 2026.
Cole Pridgen - Undergraduate practicum research, Florida State University, January-May 2026.
C. McGinnis - Undergraduate practicum research, Florida State University, January-May 2023.
M. Shontz - Undergraduate practicum research, Florida State University, January-May 2023.
Student projects in my group often combine mathematical analysis, algorithm design, numerical experiments, and reproducible software. Current and recent student research areas include:
Scientific machine learning and active learning
Compressed sensing and sparse approximation
Christoffel sampling and information design
Bayesian inverse problems and uncertainty quantification
Generative compressed sensing
Operator learning and PDE surrogate modeling
Tensor methods and high-dimensional approximation
Scientific programming and reproducible computation
This page summarizes my teaching and mentoring activities. A more complete list of courses, student committees, and professional activities is available in my CV.
The cover photo was taken at Schloss Dagstuhl – Leibniz Center for Informatics in Wadern, Germany.