Matt Ho

Matthew Ho

Carnegie Mellon Physics Ph.D. Student


About Me

I'm a second-year CMU Physics PhD student 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.

Outside of my research, I'm an avid swimmer and an amateur chess player. I'm also a member of the CMU physics department soccer team, Manfred United. You can check out my Github for a comprehensive and up-to-date list of my current projects.



A Robust and Efficient Deep Learning Method for Dynamical Mass Measurements of Galaxy Clusters (arXiv:1902.05950; submitted to ApJ)

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

Improving Mass Measurements of Galaxy Clusters through Applications of Machine Learning

Machine Learning in Science and Engineering Conference, CMU, 2018

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.