Matt Ho

Matthew Ho

Carnegie Mellon Physics Ph.D. Student


About Me

I'm a third-year CMU Physics PhD candidate working at the McWilliams Center for Cosmology. I'm interested in applying various methods of statistics and machine learning to advance studies in computational and observational cosmology.

In addition to my thesis work, I am an active member in the LSST Dark Energy Science Collaboration, where I develop analysis pipelines for cluster mass measurements and weak lensing maps. I also organize the CMU Physics Industry Speaker Series and play on the department soccer team, Manfred United. Check out my Github for a comprehensive and up-to-date list of my current projects.

Research Highlights


A Robust and Efficient Deep Learning Method for Dynamical Mass Measurements of Galaxy Clusters (ApJ, 887, 1)

We demonstrate the ability of Convolutional Neural Networks (CNNs) to mitigate systematics in the virial scaling relation and produce dynamical mass estimates of galaxy clusters with remarkably low bias and scatter. We present two models, CNN1D and CNN2D, which leverage this deep learning tool to infer cluster masses from distributions of member galaxy dynamics.


Materials Search

Materials Search is a web-scraping and data-mining tool to aid researchers in finding new candidates for superconductivity. The tool searches crystal databases and paper records for information regarding the properties of possible crystal configurations. It processes this information using statistical analysis to provide useful data at a glance.

Materials Search is useful for consolidating information in order to draw inferences on a particular material’s magnetic and electronic properties.

Selected Talks

Galaxy Cluster Mass Estimation Using Deep Learning

Weak Lensing Seminar, Universitaets-Sternwarte der Ludwig-Maximilians-Universitaet, June 2019

A Robust and Efficient Deep Learning Method for Dynamical Mass Measurements of Galaxy Clusters

Artificial Intelligence Methods in Cosmology Workshop, Ascona, Switzerland, June 2019

Improving Mass Measurements of Galaxy Clusters through Applications of Machine Learning

Machine Learning in Science and Engineering Conference, CMU, May 2018

Dynamic Particle Control and Simulation

NCSA Students Pushing Innovation Seminar, National Center for Supercomputing Applications, April 2015

Gestural Recognition of Human Expression

NCSA Students Pushing Innovation Seminar, National Center forSupercomputing Applications, December 2014, Awarded top three presenter

Course Notes

I've written and published detailed notes for each of my core physics courses throughout my graduate studies. The full list can be viewed here.

Work Experience

Quantitative Trading Intern - Virtu Financial, KCG Holdings LLC (2016 - 2017)

Applied machine learning and data mining techniques to signal research in ETF, Eurodollar future, and US commodity future markets.

Undergraduate Researcher - UIUC Physics (2015 - 2017)

Developed data mining software to gather, parse, and analyze published results regarding magnetic and electronic properties of known superconductors. Identified new potential superconductors based on structural patterns of known materials.

Students Pushing Innovation Fellow - NCSA (2014 - 2015)

Developed a machine learning algorithm to interpret expressive human movement in an artistic performance. Implemented a simulation control system to visualize physical expression in live performance.