PhD candidate of Mathematics
Pennsylvania State University
mpw5326 [at] psu [dot] edu

Research Interests

My research interest is in the development and application of data analysis methods. I am particularly interested in constructing topological and geometrical features to understand the hidden structure of data. In short, my interest lies in the intersection of TDA (topological data analysis), machine learning, statistics and their real-life applications.

Publications

• (in preparation) P. Egger, V. Itskov, M. Wu, A. Yarosh - Topological detection of monotone rank, 2021

• (preprint) M. Wu, V. Itskov - A topological approach to inferring the intrinsic dimension of convex sensing data, 2020

• Master Thesis: Minimal Sullivan Models and Rational Homotopy Groups, 2014.

Invited Talks

• Jan 11, 2021: AATRN-ATMCS 2020 (postponed due to COVID-19)

• Oct 16 , 2020: Applied Topology Seminar, SUNY Albany

• July 7, 2020: (Poster) 2020 International Conference on Mathematical Neuroscience - Digital Edition

• Sep 29, 2018: Special Session on Applied Algebraic Topology, Fall Eastern Sectional Meeting

Programming Skills

I am familiar with Julia, Python, Matlab, C and C++ for general programming purposes.

• I wrote code (in Julia) on https://github.com/nebneuron/Simplicial.jl

• Several deep learning projects are completed in tensorflow (in Python) when taking the Deep Learning Specialization by Andrew Ng.

• Several Python libraries (e.g. numpy, pandas, matplotlib, scikit-learn, etc.) are learned on Udemy

• Matlab is used in homework when taking the Numerical Analysis course offered by Prof. John Harlem.

• C is learned and used when taking an IoT Specialization offered by UCSD.

• C++ is learned and used when taking an Algorithm course offered by UCSD.

• HTML, CSS, PHP, and SQL are learned and used when taking a Web Development course offered by UoM.

• Apache Spark (PySpark) is learned through LinkedIn Learning.