Undergraduate Student Teaching Fellow at CUHK-Shenzhen
- CSC1001: Introduction to Computer Science: Programming Methodology. Fall 2023.
This course introduces the basics of computer programming using Python. Students will learn the basic elements of modern computer systems, key programming concepts, problem solving and basic algorithm design. The key topics include the basic Python language syntax, data types, operators, flow control, defining and using function, I/O, data structure and algorithms, and the basics of object oriented programing. This course provides a foundation to further study in advanced computing topics. - STA4001: Stochastic Processes. Spring 2023.
This course covers basic stochastic models as well as their various applications including service systems, financial engineering, etc. Through these stochastic models, we will learn quantitative methods which are useful in analyzing, designing, and operating one of these systems.Topics to be covered include1. Review of probability theory2. Discrete time Markov chains 3. Poisson processes 4. Continuous time Markov chains and queuing model5. Brownian motion. - DDA3005: Numerical Method. Fall 2024.
This course introduces numerical tools and computational methodologies for problems occurring in science, data science, and engineering. The first part of the course focuses on classical numerical techniques and numerical linear algebra. Topics covered in this part include: linear systems; factorizations; errors and conditioning; floating-point operations; QR decomposition and linear least squares; eigenvalue problems. In the second part of the course, several modern techniques and principles for large-scale problems are studied. In particular, iterative solvers (such as the conjugate gradient method, fixed-point techniques, Newton’s method) and topics from stochastic optimization will be discussed. Throughout the course, a modern data science-driven perspective is adopted. Examples and applications from data science and machine learning are considered to demonstrate applicability and relevance of the developed numerical methodologies. Students taking this course are expected to have knowledge in calculus, linear algebra, and optimization. - DDA4002: Stochastic Simulation. Spring 2024
This course introduces the basic stochastic simulation techniques and how to use them to solve data and decision analytics problems in application domains such as manufacturing, service and finance. Students will learn the basic pipelines of simulation and also how to formulate real operation systems into suitable simulation models, and how to use computer code to implement simulation experiments. Topics include static and dynamic simulation, simulation with spreadsheets, statistical analysis of simulation results, experiment design and simulation optimization.