Introduction to Machine Learning
Learning foundations, model evaluation, and empirical practice.
Courses and research supervision in AI, robotics, graphics, and multimodal systems.
At UW Tacoma, I teach courses that connect core computer science foundations with current advances in AI, robotics, computer graphics, and multimodal systems. My teaching emphasizes technical depth, hands-on implementation, empirical evaluation, reproducible experimentation, and open-ended project work.
I develop and refine my courses through student feedback, peer input, curriculum-level review, and faculty development activities, including the Student Experience Project and Teaching@UW.
Current UW students across Seattle, Tacoma, and Bothell can enroll through UW cross-campus registration, subject to course capacity, prerequisites, registration periods, and home-campus requirements. Undergraduate students must satisfy UW home-campus credit and registration-period requirements for cross-campus registration; graduate students have no cross-campus registration restrictions.
Official course descriptions are available through UW Course Descriptions.
Learning foundations, model evaluation, and empirical practice.
Algorithm design, analysis, and proof-driven problem solving.
Robot perception, planning, control, and embodied AI systems.
Geometry, rendering, image synthesis, and interactive graphics.
Vision-language models, multimodal learning, and evaluation.
Selected special topics may vary by quarter. Students should consult the UW Time Schedule for official registration information.
I also mentor undergraduate and M.S. students through the Multimodal Intelligence Lab (MILab), independent study, supervised research, thesis projects, and capstone projects. My mentoring emphasizes rigorous problem formulation, reproducible experiments, technical communication, collaborative problem solving, and deployable AI systems with practical impact.
UW students across Seattle, Tacoma, and Bothell can pursue research credit through TCSS 499, TCSS 600, TCSS 700, or TCSS 702, depending on student level, project type, and degree requirements. Students should contact me before registration to discuss research fit, project scope, deliverables, quarter timeline, and credit pathway; some credits may require program-level registration support.
Supervised undergraduate research in MILab.
Graduate independent study or research credit.
M.S. thesis research supervision.
Capstone supervision for applied research projects.