Teaching

Provided Masters and Ph.D. students with a unified, insightful, and modern treatment of machine learning using the lenses of convex, robust, and mixed-integer optimization. Introduced students to optimization coding in Julia.

Led recitations, assisted students, created course material, developed Julia tutorials, wrote and graded assignments and exams.

Introduced undergraduate students to fundamental techniques of using data, including advanced modeling, and how to think structurally about decision problems to make informed management decisions.

Created and delivered weekly recitations, assisted students with Python and Julia, wrote and graded assignments and projects.

Introduced MBA students to fundamental techniques of using data, including advanced modeling, and how to think structurally about decision problems to make informed management decisions.

Prepared and recorded supplementary lectures, assisted students with R, wrote and graded assignments and exams.

Created and taught Python workshops on data wrangling, visualization, machine learning, deep learning, and computer vision.

Introduced Master's and Ph.D. students to explainability, ensemble modeling, autoML, neural networks, and transfer learning.

Provided Ph.D. students with an overview of state-of-the-art techniques and tools used in machine learning and deep learning

Advised three teams of four students to conduct research on the Epidemium machine learning challenge