Teaching

Below you can find a list of courses I am currently teaching as well as courses I have taught in the past. More details can be found on the webpages for the individual courses. Courses marked with a * are new courses I have developed.

Current Courses (Spring 2024)

MATH 2318 Linear Algebra ( Course Webpage )
MATH 6397 Computational and Mathematical Methods in Data Science* ( Course Webpage )

Past Courses

MATH 6366 Optimization Theory ( Course Webpage; Fall 2023 )
MATH 6397 Bayesian Inverse Problems and UQ* ( Course Webpage; Spring 2023 )
MATH 2318 Linear Algebra ( Course Webpage; Spring 2023 )
MATH 6366 Optimization Theory ( Course Webpage; Fall 2022 )
MATH 3336 Discrete Mathematics ( Course Webpage; Spring 2022 )
MATH 6366 Optimization Theory ( Course Webpage; Fall 2021 )
MATH 3336 Discrete Mathematics ( Course Webpage; Fall 2021 )
MATH 3336 Discrete Mathematics ( Course Webpage; Spring 2021 )
MATH 6397 Applied Inverse Problems* ( Course Webpage; Fall 2020 )
MATH 6366 Optimization Theory ( Course Webpage; Fall 2020 )
MATH 2331 Linear Algebra ( Course Webpage; Spring 2020 )
MATH 6366 Optimization Theory ( Course Webpage; Fall 2019 )
MATH 2331 Linear Algebra ( Course Webpage; Spring 2019 )
MATH 6366 Optimization Theory ( Course Webpage; Fall 2018 )
MATH 2331 Linear Algebra ( Course Webpage; Spring 2018 )
MATH 2331 Linear Algebra ( Course Webpage; Fall 2017 )

References

Here's a list of books and online resources related to courses I teach and research I do.

Inverse Problems

An Introduction to Data Analysis and Uncertainty Quantification for Inverse Problems by L. Tenorio. SIAM 2017.
An Introduction to the Mathematical Theory of Inverse Problems by A. Kirsch, Springer, 1996.
Computational Inverse Problems by C. Vogel, SIAM Press, 2002.
Computational Uncertainty Quantification for Inverse Problems by J. Bardsley. SIAM Press 2018.
Discrete Inverse Problems: Insight and Algorithms by P.C. Hansen, SIAM Press, 2010.
Inverse Problem Theory and Methods for Model Parameter Estimation by Albert Tarantola, SIAM Press, 2004.
Rank-Deficient and Discrete Ill-Posed Problems by P.C. Hansen, SIAM Press, 1998.
Parameter Estimation and Inverse Problems by R. C. Aster, B. Borchers and C. H. Thurber, Elsevier, 2019.
Statistical and Computational Inverse Problems by J. Kaipio and E. Somersalo, Springer, 2005.

Optimization

Convexity and Optimization in R^n by Leonard D. Berkovitz. John Wiley and Sons 2002.
Convex Optimization by S. Boyd and L. Vandenberghe. Cambridge University Press 2004.
Introduction to Nonlinear Optimization by A. Beck. SIAM 2014.
Lectures on Convex Optimization by Yurii Nesterov. Springer 2018.
Numerical Optimization by J. Nocedal and S. J. Wright. Springer 2006.
Optimization with PDE Constraints by M. Hinze, R. Pinnau, M. Ulbrich, and S. Ulbrich. Springer 2009
Perspectives in Flow Control and Optimization by M. D. Gunzburger. SIAM 2003.

Uncertainty Quantification

An Introduction to Data Analysis and Uncertainty Quantification for Inverse Problems by L. Tenorio. SIAM 2017.
Uncertainty Quantification: An Accelerated Course with Advanced Applications in Computational Engineering by Christian Soize. Springer 2017.
Introduction to Uncertainty Quantification by T. J. Sullivan. Springer 2015.

Applied & Computational Mathematics

Computational Methods in Geophysical Electromagnetics by Eldad Haber. SIAM 2015.
Foundations of Computational Imaging: A Model-Based Approach by Charles A. Bouman. SIAM 2022.