The teacher who is indeed wise does not bid you to enter the house of his wisdom but rather leads you to the threshold of your mind. -- Kahlil Gibran

Teaching Experience

Network Science
(4-credit undergraduate course, 2018 Spring, 2018 Fall, 2019 Fall)
This is an undergraduate course for Yao class sophomore as requirement. The course first overviews the classical results in probability theory, and then study the beautiful techniques mostly used in queueing theory and stochastic process, including discrete-time and continuous-time Markov Chains, renewal theory, BCMP product-form for M/G/1/PS networks, Laplace transforms, heavy tail distributions, and more. Throughout the course, we emphasize the intuition behind the results and their connections to the real world applications, e.g., online game matchmaking, fast delivery service’s impact on the restaurant industry, ride sharing platform’s market efficiency evaluation, etc.

Computational Modeling for Urban System Regulation
(2-credit undergraduate course, 2017 summer, 2018 summer, 2019 summer)
This is also an undergraduate course for Yao class sophomore as requirement. It is designed to be a follow-up course to supplement Network Science. The students are encouraged to combine their knowledge on queueing theory, game theory, stochastic process, and machine learning to develop the course project. The course aims at helping the students to build up a computational perspective to view the world. It also intends to prompt the students’ ability to raise interesting research questions as well as computational modeling.

Computational Energy Economics
(3-credit graduate course, 2017 Fall, 2019 Spring)
The future smart energy system seeks to exploit the pervasive intelligent devised in the power system to enhance the reliability of the system control paradigm and to improve the efficiency of the electricity market design. In this seminar course, we provide a combined perspective, with the emphasis on algorithmic issues and the economic issues, for the students to understand the smart energy system. The students are required to read classical papers as well as the state-of-art works, and actively participate in the in-class discussion. The content in the course are useful to students in Electrical Engineering, Computer Science, and Industrial Engineering.

Teaching Award

On November 6 2018, I was selected into Teaching Excellence in Computer Science Scheme, standing out of 106 candidates from 9 universities across the country. This Teaching Award Scheme is initiated by Prof. John Hopcroft, Turing Award Laureate, CAS Foreign Member, and Prof. Wen Gao, member of Chinese Academy of Engineering, Chairman of China Computer Federation, and receives support from the Ministry of Education and National Natural Science Foundation of China. It is also sponsored by ten Hi-Tech enterprises including Toutiao, Sinovation Ventures, Didi, Baidu. Inc. MSRA, SenseTime, Alibaba Group, IFLYTEK, Lenovo, and Qihoo 360 Technology.

Launched in October 2018, the Scheme is to recognize excellence in college and university teaching in computer science, with winners selected by a panel of experts from China Computer Federation, the Ministry of Education and Chinese Teacher Development Foundation.

Teaching Philosophy

For the theoretical content, I strive to be the educator who excites curiosity in my students by explaining deep results in a rigorous yet intuitive way. To incite curiosity, the theoretical curriculum that I wish to construct shall be one that not only delivers the beauty of theoretical results but also abounds with real world applications and impacts.

For the technical content, I want the students to have a deep understanding of both the significance and the limitations of the models. This is extremely important for interdisciplinary research. Take the CS plus energy as an example, when customizing computer science algorithms or frameworks for the electricity sector, the refined concepts or models in the energy system often enrich the scope of computer science. This is my way to help incite students’ innovative thinking.