Fall 2021

Math 415: Partial Differential Equations

Spring 2021

Math 450: Optimization Methods in Machine Learning.
GitHub repo: https://github.com/scaomath/wustl-math450

Fall 2020

Math 449: Numerical Applied Mathematics.
GitHub repo: https://github.com/scaomath/wustl-math449

Spring 2020

Math 130C: Stochastic Processes

Winter 2020

Math 10: Introduction to Programming in Data Science.
GitHub repo: https://github.com/scaomath/UCI-Math10
Python starter: getting ready for Math 10

Math 130B: Multivariate Probability

Fall 2019

Math 112A: Partial Differential Equations
Math 290C: Calculus of Variation



Mentoring Kaggle machine learning competition

During summers and winter break, I will be dedicated to mentor (a few) students in the Kaggle machine learning competition: general cross-validation and data analysis, Python workflow, advanced tricks in Pandas, how to write quality code and debug complex Python scientific computing codes, etc. If you are interested, please contact me near the end of a semester.

Kaggle in-class competitions

To get started with competing with data scientists from all over the world, taking a class and then participating an in-class Kaggle competition is a great way to learn and practice. Here is a list of in-class competitions I have hosted in the past.

Some project guideline:

Jane Street Market Prediction Competition Dec 2020

Place: Bronze medal, 241 out of 4245 teams; GitHub repo: https://github.com/scaomath/kaggle-jane-street; Student mentored: Ethan Zheng, currently a software engineer at Amazon.

Los Alamos National Lab Earthquake Prediction Competition June 2019

Place: Bronze medal, 381 out of 4516 teams; Student mentored: Ziteng Pang, currently a grad student at UMich.