I aim to teach machine learning and computing in a way that connects mathematical foundations, algorithmic understanding, implementation skills, and current research practice. My courses are designed to help students move from mastering concepts to reading papers, building systems, and pursuing independent research.
Courses at the University of Georgia
| Term | Course | Enrollment | Notes |
|---|---|---|---|
| Spring 2026 | CSCI/ARTI 8950 Machine Learning | 40 | Graduate machine learning course |
| Fall 2025 | CSCI 8000 Optimization in AI | 19 | Special topics course |
| Spring 2025 | CSCI/ARTI 8950 Machine Learning | 40 | Graduate machine learning course |
| Fall 2024 | CSCI 8000 Optimization in AI | 25 | Special topics course |
| Spring 2024 | CSCI/ARTI 8950 Machine Learning | 29 | Graduate machine learning course |
| Fall 2023 | CSCI 2720 Data Structures | 43 | Undergraduate core course |
Course Development
A major recent teaching project has been the development of CSCI 8000 Advanced Special Topics in CSCI with a focus on Optimization in AI. The course covers foundational and advanced optimization methods for AI, machine learning, and data science, including:
- convexity and gradient methods;
- projected and proximal methods;
- accelerated methods;
- Newton and quasi-Newton methods;
- nonconvex optimization;
- derivative-free optimization;
- federated and distributed learning;
- sharpness-aware methods;
- bilevel optimization.
Building on these offerings, I prepared a proposal for a new graduate course, CSCI 8830 Advanced Optimization Methods in AI.
Teaching Approach
In both machine learning and optimization courses, I combine:
- mathematical foundations;
- implementation-oriented assignments;
- example code and reproducible workflows;
- guided paper reading;
- student presentations;
- project-based learning.
The goal is to help students become fluent in both the principles and practice of modern AI.
Mentoring and Advising
My teaching extends beyond the classroom through active mentoring.
- Supervised 13 master’s students in project, research, or thesis work during the review period.
- Served on 29 graduate advisory committees and advising appointments.
- Currently advise PhD students including Ke Deng and Jiaxi Li.
Selected Student Outcomes
- Student projects from my courses have continued into research, master’s projects, and thesis work.
- Sai Akshitha Reddy Kota’s project, “A Vertex-Centric Distributed Framework for Pattern Matching in Massive Graphs,” was selected for presentation at the 2025 SoC Day Event.
- Khoa Le’s project, “Decoding Images from Brain Signals: A Functional Near-Infrared Spectroscopy Approach,” was selected for presentation at the 2026 SoC Day Event.
- Khoa Le was one of the winners of the Cox Automotive Inc. Sustainability Challenge at UGAHacks 2025.
Student Feedback and Recognition
Student feedback has consistently highlighted clear explanations, strong organization, helpfulness outside class, example code, and the value of research-oriented assignments and presentations. I received the University of Georgia Student Career Success Influencer Award in 2024.
Previous Teaching
Before joining UGA, I taught at the University of Michigan-Dearborn:
- CIS 583 Deep Learning
- CIS 579 Artificial Intelligence
- CIS 5700 Advanced Data Mining
- CIS 405/505 Algorithm Analysis and Design
I also served as a co-lecturer for CSE 5820 Machine Learning at the University of Connecticut.