Teaching

Instruction begins when you, the teacher, learn from the learner; put yourself in his place so that you may understand… what he learns and the way he understands it. —Søren Kierkegaard

CE 400/500: Special Topics: AI for Chemical Engineers

This course is an AI/ML literacy elective course, designed to be accessible and appealing to all junior and senior ChemE undergraduate and graduate students. The course was first taught in Fall 2025 in the Department of Chemical & Biological Engineering at the University at Buffalo and received exceptional feedback from both undergraduate and graduate attendees.

Some highlights of this course:

  • Structured to help ChemE students develop fundamental abilities to understand, critically evaluate, and interact with AI/ML systems.
  • Covers a comprehensive spectrum of AI/ML concepts: AI-assisted programming, predictive modeling (e.g., regression and classification), prescriptive algorithms (e.g., genetic algorithm), and generative AI (e.g., transformer), emphasizing breadth over depth.
  • Narrative lectures on AI/ML algorithms are accompanied by highly interactive hands-on demonstrations in the context of chemical science and engineering.
  • Features guest lectures from leading scientists and engineers in both academia and industry (see past guest speakers).

All course materials are made public via GitHub.

CE 525: Advanced Chemical Engineering Thermodynamics (Fall 23-25)

This course is a graduate-level core course that provides a rigorous and unified treatment of classical and statistical thermodynamics with direct relevance to modern chemical engineering research and practice. The course emphasizes thermodynamic analysis of non-ideal fluids, phase equilibria, and high-pressure systems, while introducing statistical mechanics as a molecular foundation for macroscopic thermodynamic laws. Students will develop the ability to derive and apply fundamental thermodynamic relations, interpret phase behavior, and connect probability distributions and partition functions to measurable properties. The course also introduces molecular simulation methods, including Monte Carlo and molecular dynamics, to bridge theory with computational practice and contemporary research problems in fluids and materials.