Teaching

Graduate Courses

MASC 575: Basics of Atomistic Simulation of Materials
MASC 576: Molecular Dynamics Simulations of Materials and Processes

These courses introduce science and engineering students to modern computational materials modeling — from atomistic simulations using Molecular Dynamics (MD) and Monte Carlo (MC) methods and quantum mechanical simulations via Density Functional Theory (DFT), to foundation material models and artificial intelligence (AI)–driven approaches. The landscape of computational materials science is rapidly changing. DFT has become a standard tool for investigating a wide range of materials properties, and remarkable progress in AI and machine learning (ML) has opened exciting new directions — generative AI, universal Machine Learning Interatomic Potentials (uMLIP), and foundation models — that deliver structure–property prediction and computational materials synthesis with quantum-mechanical accuracy at a fraction of the computational cost. Students will learn a full spectrum of techniques, from large-scale simulations on high-performance computers to deep learning–based AI systems for materials.

MASC 515: Basics of Machine Learning for Materials
MASC 520: Mathematical Methods for Deep Learning

These courses introduce Machine Learning (ML) and Python programming essentials for data-driven science and engineering. Artificial Intelligence (AI), Deep Learning (DL), Reinforcement Learning (RL), and Large Language Models (LLMs) are rapidly transforming how we interact with technology, and the fast pace of the field can make it challenging for beginners to enter. Python has become the industry standard for ML, and basic software engineering skills and literacy are essential for moving from theoretical knowledge to real-world application. The courses are designed for students from any engineering or science background — no prior experience with ML or Python programming is required.

Undergraduate Courses

MASC 110L: Materials Science
AME 231: Mechanical Behavior of Materials
CHE 405: Applications of Probability and Statistics for Chemical Engineers