Teaching Vision
My vision is to create a collaborative learning environment where my students and I can grow and thrive together in such an ever-changing and challenging world. I strive to foster a learning community that embraces a broad spectrum of experiences or perspectives, ensuring that students from diverse backgrounds feel respected, supported, and experience a profound sense of meaning and belonging. Together, we explore, use, and reflect on advanced technologies in a friendly, collaborative environment to improve quality of life and make a meaningful impact on society.
Courses Taught at University of Washington
[IND E 315] Probability And Statistics For Engineers
Summer 2026
Application of probability theory and statistics to engineering problems, distribution theory and discussion of particular distributions of interest in engineering, statistical estimation and data analysis. Illustrative statistical applications may include quality control, linear regression, and analysis of engineering data sets.
Past Courses
[IND E 427/527] Data Analytics for Systems Engineering Teaching Assistant
Autumn 2025
Emphasizes data-driven system modeling, including basic statistical learning models, and system modeling and decision-making. Covers experimental design for data collection, tree-based control charts for process monitoring, rule-based decision-making, and diagnosis of root causes as learning problems. Students develop connections between emerging statistical learning techniques with system modeling and optimization methods.
[IND E 412] Integer and Dynamic Programming
Spring 2024
Modeling and optimization of problems and dynamic programming approach to optimization. Topics include: integer programming formulation techniques, linear and Lagrangian relaxation, branch-and-bound and cutting-plane methods, integer programming applications, and dynamic programming.
[IND E 310] Linear and Network Programming Teaching Assistant
Autumn 2023
Modeling and optimization of linear network problems. Topics include optimization of linear systems, mathematical model design, simplex method, primal-dual algorithms, parametric programming, goal programming, network problems and algorithms.