Matthew Beveridge

PhD Student at Columbia University

CV // google scholar


I am an incoming doctoral student at Columbia University in the Columbia Imaging and Vision Lab (CAVE) with Prof. Shree Nayar and a Visiting Researcher at the University of Colorado Boulder with Prof. Morteza Karimzadeh. My research focuses on computer vision, computational imaging, and machine learning for robust perception and its application to the science of the physical environment. In addition to research, I have been involved with startups in the field of autonomy, organized community events around energy and climate, and worked on spaceflight at NASA. I am also a recipient of the Greenwoods Fellowship and the LEAP Momentum Fellowship at Columbia. Prevously, I completed my Master of Engineering (M.Eng.), advised by Prof. Daniela Rus, and Bachelor of Science (B.S.) in Electrical Engineering and Computer Science (EECS) at the Massachusetts Institute of Technology (MIT), with a double major in Mathematics and minor in Theater Arts.


July 2023: I was awarded the Greenwoods Fellowship to support my studies at Columbia.

June 2023: I joined Prof. Morteza Karimzadeh as a Visiting Researcher at the University of Colorado Boulder to develop automated methods of Arctic sea ice mapping from satellite imagery.

May 2023: I received the NSF LEAP Momentum Fellowship to study the optical properties of atmospheric aerosols using graph-based methods. I will be working with Prof. Carl Vondrick and Dr. Kara Lamb. [press]

Apr 2023: This Fall, I will be attending Columbia University to pursue a Ph.D. with Prof. Shree Nayar. My focus will be on computer vision, computational imaging, and machine learning.

Oct 2022: A proposal for explainable forecasting of Arctic sea ice concentration was accepted to the NeurIPS 2022 Workshop on Tackling Climate Change with Machine Learning. [project]

Sep 2022: Our work on motion tracking to identify and track turbulent filaments in fusion plasmas (via Gas Puff Imaging diagnostics) was accepted to Scientific Reports. [paper] [press]

Jan 2022: A paper on bayesian optimization of droplet generating hardware at varying scales (e.g., for photovoltaic cells and drug delivery) was accepted to ACS Applied Materials and Interfaces! [paper]

Dec 2021: Two new papers accepted to the NeurIPS 2021 Workshop on Tackling Climate Change with Machine Learning on the topics of the Atlantic Multidecadal Variability [project] and biogeochemistry of the Southern Ocean [project]. The former was awarded best paper!

Nov 2021: Generalizing Imaging through Scattering Media with Uncertainty Estimates was accepted to the WACV 2022 Workshop on Applications of Computational Imaging. [paper]

Aug 2021: Our work on using generative models to speed up computational fluid dynamics simulation, with application to urban wind modeling, was accepted to ACML 2021 for a long oral spotlight. [project]

Jul 2021: Our paper on image-based audio augmentation was accepted to ICCV 2021. [project] I also began work as a Computer Vision Engineer at NODAR.

May 2021: I completed my Master's thesis on consistent monocular depth estimation in data-driven simulation for reinforcement learning-based autonomous driving. [dspace]

Last updated: July 19th, 2023.