Our group highly values teaching and offers a comprehensive set of courses on scientific computing, applied mathematics, data science, mathematical modeling, and its applications. All our courses are taught in relatively small sizes, allowing close interactions with students.

#### Advanced Undergraduate Courses

- MATH 315: Numerical Analysis
- MATH 345: Mathematical Modeling
- MATH 347: Introduction to Nonlinear Optimization
- MATH 351: Partial Differential Equations
- MATH 352: PDEs in Action
- MATH 361&362: Probability & Statistics

### Slected Special Topics Undergraduate-Level Courses

- Progamming for Mathematics of Data Science (Fall 2022)
- Convex Optimization (Fall 2022)

#### Regularly Offered Graduate-Level Courses

- MATH 515: Numerical Analysis I (Numerical Linear Algebra)
- MATH 516: Numerical Analysis II
- MATH 517: Numerical Analysis III (Iterative Methods)
- CS555: Parallel Processing
- MATH 561: Matrix Analysis
- MATH 571: Numerical Optimization
- MATH 572: Numerical Partial Differential Equations

#### Selected Special Topics Graduate-Level Courses

- RTG Seminar: Computational Methods and AI for Discovering New Mathematics (Fall 2023)
- Introduction to Tensor Decompositions (Fall 2023)
- RTG Seminar: Mathematical Research Through Model Reduction and Mixed Precision (Fall 2022)
- RTG Seminar: Computational Methods for Data Science (Fall 2021)
- Numerical Methods for Deep Learning (Spring 2020)
- Data Assimilation (Spring 2018)
- Numerical Methods for Deep Learning (Spring 2018)
- Structured Matrix Computation (Fall 2017)
- Bayesian Inverse Problems (Spring 2016)
- Advanced Numerical Linear Algebra Methods with Applications
- Fluid Dynamics