Teaching

Lehigh University

• ISE 364: Introduction to Machine Learning  (Fall 2023).

Topics: Python for Data Analysis, Linear Regression, Cross Validation and Bias-Variance Trade-Off, Logistic Regression, K-Nearest Neighbors, Decision Trees and Random Forests, Support Vector Machines, K-Means Clustering, PCA, Natural Language Processing, Neural Nets, and Deep Learning. (Syllabus)

Department of Industrial and Systems Engineering, Lehigh University, Bethlehem, PA (USA).

• DSCI 311: Optimization and Mathematical Foundations for Data Science (Summer 2023).

Topics: Most fundamental tools from calculus, linear algebra, probability, statistics, convex analysis, and optimization; Unsupervised/supervised learning problems, PCA, maximum likelihood estimation, linear and logistic regression, support vector machines, and neural network training. (Syllabus)

Department of Industrial and Systems Engineering, Lehigh University, Bethlehem, PA (USA).

• ISE 444: Optimization Methods for Machine Learning (Spring 2023).

Topics: Optimization models in data science, convexity and nonsmooth calculus, stochastic gradient methods, subgradient and proximal gradient methods, noise reduction methods, second-order methods. (Syllabus)

Department of Industrial and Systems Engineering, Lehigh University, Bethlehem, PA (USA).

• ISE 230: Introduction to Stochastic Models in Operations Research (Spring 2022).

Topics: Optimization under uncertainty, decision analysis, game theory, Markov chains, queueing theory, dynamic programming, Markov decision processes. (Syllabus)

Department of Industrial and Systems Engineering, Lehigh University, Bethlehem, PA (USA).

Sapienza University of Rome

• Teaching Assistant, Mathematics Course (Spring-Fall 2018).

Department of Methods and Models for Economics, Territory, and Finance, Sapienza University of Rome, Italy.

• Lectures on AMPL and applied models in Operations Research (Spring 2018). 

Department of Computer, Control, and Management Engineering, Sapienza University of Rome, Italy.