Teaching
I teach machine learning and AI to business audiences, and have assisted courses in natural language processing, large language models, analytics, simulation, and stochastic processes.
Deep Learning Bootcamp for Business Audience
Open to PhD students and faculty · 10–15 attendees per session
- 01
Introduction to machine learning
Supervised learning, multilayer perceptrons, stochastic gradient descent, backpropagation; MNIST in PyTorch
- 02
Introduction to natural language processing
Bag of words, word embeddings, recurrent neural networks
- 03
How ChatGPT works
Transformers, pretraining, instruction tuning, RLHF, the OpenAI API
- 04
Fine-tuning LLMs for preference prediction
Prompt engineering, LoRA, prediction heads on LLMs; case study: LOLA
Code on GitHub ↗Materials licensed CC BY-NC-ND 4.0
Teaching assistant
University of Toronto, Rotman School of Management
- RSM317H1
Text Mining and Natural Language Processing
Fall 2026Current
- RSM8430H
Applications of Large Language Models
Winter 2026
- RSM8431Y
Analytics Colloquium
Mentored three student teams (3–4 students each) on industry-partnered analytics projects.
2025–26
UC Berkeley, Industrial Engineering and Operations Research
- INDENG 174
Simulation for Enterprise-Scale Systems
- INDENG 173
Introduction to Stochastic Processes