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Teaching

    Courses taught at Duke:
    • Fall 2023: Math 631 Measure and Integration [Syllabus]; Math 465 Introduction to High Dimensional Data Analysis [Syllabus] (co-taught with Dr. Jiajia Yu).
    • Spring 2022: Math 532 Basic Analysis II. [Syllabus]
    • Fall 2021: Math 790 Minicourse: An introduction to kernel methods in machine learning [Syllabus] (see this github repository for lecture notes).
    • Spring 2021: DKU Math 302 Numerical Analysis, Math 405 Methods in Data Analysis (7-week courses at Duke Kunshan University)
    • Spring 2020: Math 532 Basic Analysis II. [Syllabus]
    • Fall 2019: Math 561 Numerical Linear Algebra. [Syllabus]
    • Spring 2019: Math 532 Basic Analysis II. [Syllabus]
    • Fall 2018: Math 790 Minicourse: High dimensional probability in data analysis [Syllabus]
    • Fall 2018: Math 631 Real Analysis. [Syllabus]
    • Spring 2018: Math 532 Basic Analysis II. [Syllabus]
    • Fall 2017: Math 690 Topics in Data Analysis and Computation. [Syllabus] (see this github repository for scribed notes and demos).

    Courses taught at Yale:
    • Fall 2015: Math 225 Linear Algebra and Matrix Theory
    • Fall 2016: Math 260 Basic Analysis in Function Spaces
    • Spring 2016, 2017: Math 705 Topics in Machine Learning Theory and Computation (graduate student seminar)

Undergraduate Research Projects

    Mentored at Duke:
    • Brian Lee, Flora Shi, Nick Talati, "Spatial-temporal prediction on graphs by recurrent neural network", Domath 2022 project.
    • Bhrij Patel, "Neural network dimension reduction of data with topological constraint", Domath 2020 and independent study project.
    • Remy Kassem, "Symmetry detection of unknown volumes from projected observations", PRUV 2019 and senior Thesis project. See the report here.
    • Oscar Li, "Structured bases-learning convolutional neural networks", independent study project 2019. See the report here.
    • Tyler Lian, Inchan Hwang, Joseph Saldutti and Ajay Dheeraj, "Local affinity construction for dimension reduction methods", DoMATH 2018 project, co-supervised with Prof. Hau-tieng Wu. See the report here.

    Mentored at Yale:
    • Austin Wang: "Analysis of the learning process of a recurrent neural network on the last k-bit parity function". See the report here.