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.
Teaching Philosophy
My teaching philosophy is rooted in Confucius' saying: "When three people walk together, I am sure to find my teacher among them." This belief shapes my view of teaching as a collaborative and reciprocal process where both students and instructors learn from each other. To maximize this mutual benefit, it is necessary to embrace differences and I believe teaching should be personalized to accommodate students' varied academic needs, prior knowledge and learning styles, rather than relying on a one-size-fits-all approach. I design course materials and assessments that are flexible and responsive, enabling students to engage with course content in ways that align with their personal goals and strengths. I also value the importance of connecting theory to practice. When theoretical concepts are illustrated through real-world examples, students not only grasp complex ideas more effectively, but also develop the confidence and ability to apply what they learn in real-world situations.
Courses Taught
[0] 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.
[1] Integer and Dynamic Programming Sole Instructor
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.
[2] 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.