Teaching

BME2111 Neural Signal Processing and Data Analysis (ShanghaiTech)

Graduate course, Instructor, Offered in Fall 2022, Fall 2023

A graduate course in the field of intelligent medicine within biomedical engineering. As an introductory course to computational neuroscience and neural engineering, it is suitable for graduate students and senior undergraduates who aspire to pursue research in neural engineering, computational neuroscience, and related fields. The course mainly introduces the application of modern statistical signal processing, machine learning, and artificial intelligence technologies in neuroscience. It specifically focuses on how to record and describe neural signals, how to establish statistical models for different scales of experimental data, how to use statistical and machine learning methods to analyze neural data, and then analyze the characteristics of neural systems. It also integrates signal processing and control technologies to decode and regulate neural systems, cognitive behaviors, and psychiatric diseases. Through this course, students will understand and master the theories and methods of data analysis commonly used in neuroscience, laying the foundation for research in brain science, neural engineering, computational neuroscience, artificial intelligence, and related fields.

BME1106 Biomedical Signals and Systems II: Digital Signal Processing (ShanghaiTech)

Undergraduate course, Instructor, Offered in Spring 2023, Spring 2024

This is a foundational course in BME, which covers the digital representation of signals and the use of digital computers or digital circuits to analyze, modify, and extract information from signals. In biomedical engineering, electronic and computer engineering courses, digital signal processing is widely emphasized as a fundamental skill. The course aims to equip students with basic knowledge of digital signal processing, including time and frequency domain analysis methods for discrete-time signals and systems, mathematical models and concepts for signal sampling and reconstruction, basic concepts of discrete Fourier transform and signal spectral analysis methods, fundamental principles of the fast Fourier transform algorithm, and basic methods for designing digital filters.

10-708 Probabilistic Graphical Models (Carnegie Mellon, Spring 2018)

Graduate course, Teaching Assistant, Carnegie Mellon University, Machine Learning Department

A core PhD course that covers the theoretical foundations, advanced topics and applications of probabilistic graphical models. The class mainly covers three aspects: The core representation, including Bayesian and Markov networks, and dynamic Bayesian networks; probabilistic inference algorithms, both exact and approximate; and, learning methods for both the parameters and the structure of graphical models. [website]

Summer Undergraduate Research Program in Neuro Computation (uPNC， Carnegie Mellon, Summer 2016)

Workshop, Teaching Assistant, Carnegie Mellon University, Center for the Neural Basis of Cognition

2-week boot camp series that covers MATLAB/R coding and core concetps in computational neuroscience. [website]

18-202 Mathematical Foundations of Electrical Engineering (Carnegie Mellon, Spring 2013)

Undergraduate course, Teaching Assistant, Carnegie Mellon University, Department of Electrical & Computer Engineering

This course covers topics from engineering mathematics that serve as foundations for descriptions of electrical engineering devices and systems, including linear algebra, complex analysis, Fourier analysis, differential equations, etc. [website]