Teaching

COmmon tools for data science (MSDZ06)

The world is one big data problem. By Andrew McAfee.

Intended Learning Outcomes: 

This course is to introduce commonly used programming languages and data analysis methods. At the end of this course, you will be able to: 

1. Code using Matlab, python or other programming language;

2. Implement most of the data analysis methods covered in the class;

3. Choose and apply a proper method to address real-world problem based on real data.

GitHub Repository: https://github.com/tools-for-data-science-master

Introduction to Astronomy (GSS001) 

Two things fill the mind with ever-increasing wonder and awe, the more often and the more intensely the mind of thought is drawn to them: the starry heavens above me and the moral law within me. By Immanuel Kant 有二事焉,恒然于心；敬之畏之,日省日甚：外乎者如璀璨星穹,内在者犹道德律令. 钱坤强译

Intended Learning Outcomes: 

This course is to provide basic knowledge in astronomy and to introduce scientific thinking method. At the end of this course, you will be able to:

1. Describe the scope of astronomy and astrophysics.

2. Explain how the scientific reasoning and quantification methods are applied in astronomy.

3. Tell the difference between science and fantasy in science fiction.

4. Present an argument and support it with evidence, proof, and/or examples.   

GitHub Repository: https://github.com/IntroductoryAstronomy

research skill training

Part of “Literature Survey and Thesis Proposal”

For fresh Master & PhD Students

Intended Learning Outcomes

This training program aims to help fresh Master and PhD students understand the academic career path and requirements at different stages of your Master and PhD programs. After the program, you will be able to:

1. Define the required skills/traits in an academic career;

2. Identify and value your skills, and revise your CV;

3. Learn how to write and communicate more effectively;

4. Understand the differences in academic and industrial work opportunities.

Linear Algebra (BAE1923)

If you want to learn about nature, to appreciate nature, it is necessary to understand the language that she speaks in. By Richard P. Feynman

Intended Learning Outcomes

(Please check ILOs mentioned in the beginning of each class)

You will learn basic concepts in linear algebra, including linear equation, matrix, vector space, and their operations. Through concrete examples, you will learn to apply linear algebra is in data analysis and optimization.

By the end of this course, you will be able to:

1.  Understand basic concepts and use the linear algebra language fluently.

2.  Remember by heart the steps to solve linear equation, conduct linear transformation and principal component analysis, etc.

3.  Convert expressions, properties, and operations under the linear equation, matrix, and vector space frameworks.

4.  Apply linear algebra in Least-Square problems, Markov chain, etc.

SUPERVISory experience

• X.-L. Cui (Master, PhD), Lunar crater identification and model age estimation using deep learning, 2019-present

• J.-L. He (Master), Formation of martian polar layered deposits based on ice sheet simulation, 2020-2022

Other teaching experience

• Understanding the Gravity Field of the Moon, in 3rd APSCO & ISSI-BJ Space Science School, Si Racha, Chon Buri, Thailand, October 2023 (Lecturer & Tutor)

• Exploration Techniques and Research Progress on the Geology of Terrestrial Planets, in Intelligent Ocean Survey, Southern University of Science and Technology, Spring 2022 (Guest Lecturer)

• Numerical Simulation of MOR Flow and Thermal Structure using Finite Difference Method, in Numerical Methods, MUST, Nov 12, 2021 (Guest Lecturer) 

• Academic Etiquette, in Scientific Research Writing and Academic Standards, Peking University, Spring 2019 (Guest Lecturer)

• Discover Earth, Atmospheric and Planetary Sciences, MIT EAPS Freshman Orientation Trip, Mt. Washington, New Hampshire, 28-30 August 2015 (Teaching Assistant)

• Earth Educators' Rendezvous, Sponsored by NAGT, Madison, WI, July 2016  (Attendance)

• Kaufman Teaching Certificate Program, MIT, 2016 (Attendance)