Undergraduate course, Texas A&M University, 2026
This laboratory course develops computational approaches for data engineering applications involving stochastic systems, reinforcement and ensemble learning, and the visual presentation of data.
Undergraduate course, Texas A&M University, 2025
This laboratory course uses computational tools to formulate and solve data engineering problems, emphasizing optimization and machine learning.
Graduate course, Texas A&M University, 2025
This course covers the basic theory of sets, functions, numbers, linear algebra, and real analysis necessary for graduate coursework in operations research. Engineering PhD students focusing in intensely mathematical or theoretical fields will also find this material to be integral to their work.
Graduate course, Texas A&M University, 2024
Convex analysis provides the mathematical framework for studying nonlinear optimization. Topics include cones and convex sets, hyperplane separation, subgradients, convex conjugacy, duality, and the effects of changing model parameters, together with applications of these principles and methods.
Undergraduate course, Texas A&M University, 2023
This course introduces deterministic optimization through the formulation and solution of linear programs, integer programs, and network flow models. Students use optimization software to apply these methods to practical problems.
Graduate course, Texas Tech University, IMSE, 2023
This course develops the theory and applications of linear optimization. Topics include simplex and revised-simplex algorithms, dual and primal-dual methods, sensitivity analysis, parametric programming, decomposition, complementarity, assignment and transportation models, and Karmarkar’s algorithm.
Graduate course, Texas Tech University, IMSE, 2022
Deterministic and probabilistic methods and models in operations research.
Undergraduate course, Texas Tech University, IMSE, 2022
Introduction to operations research, linear programming, dynamic programming, integer programming, traveling salesman problem, transportation, and assignment problems.
Graduate course, Texas Tech University, IMSE, 2022
This course is an accelerated and advanced introduction to linear algebra and real analysis for engineering PhDs who will heavily engage in quantitative theory.
Graduate course, Texas Tech University, IMSE, 2021
This is the second in a complete two-course introduction to theory of linear, nonlinear, and combinatorial optimization. We plan to cover all of linear optimization and some of the rudiments of nonlinear optimization in this course. We will emphasize the theory and analysis of optimization problems with a view toward understanding the complexity of key algorithms and problems.
Graduate course, Texas Tech University, IMSE, 2020
This course covers the fundamentals of first- and second-order optimization methods used in modern machine learning and statistics. Topics include: gradient and subgradient descent, the proximal gradient method, mirror descent, the Frank-Wolfe method, stochastic gradient descent, variance reduction, and quasi-newton methods.
Undergraduate course, Carnegie Mellon University, Department of Mathematical Sciences, 2015
This course introduces the basic concepts, ideas and tools involved in doing mathematics. As such, its main focus is on presenting informal logic, and the methods of mathematical proof. These subjects are closely related to the application of mathematics in many areas, particularly computer science. Topics discussed include a basic introduction to elementary number theory, induction, the algebra of sets, relations, equivalence relations, congruences, partitions, and functions, including injections, surjections, and bijections. A basic introduction to the real numbers, rational and irrational numbers. Supremum and infimum of a set.