I am pursuing my doctorate under Professors Greg Ver Steeg and Aram Galstyan at the USC Information Sciences Institute. My contributions focus on robust causal inference and high-dimensional statistics.
Separately, I have spent time developing an end-to-end system for realtime monitoring of ultradian rhythms, those few-hour cycles in the human body that seem to modulate alertness. My eventual goal is to promote long-term wellness and health through novel uses of comfortable, noninvasive, and affordable wearables.
M. G. Marmarelis, E. Haddad, A. Jesson, N. Jahanshad, A. Galstyan and G. Ver Steeg,
Partial Identification of Dose Responses with Hidden Confounders, Uncertainty in Artificial Intelligence (UAI 2023 oral).
M. G. Marmarelis, G. Ver Steeg, and A. Galstyan,
A Metric Space for Point Process Excitations, Journal of Artificial Intelligence Research 73 (2022) 1323–1353.
M. G. Marmarelis and R. G. Ghanem,
Data-driven Stochastic Optimization on Manifolds for Additive Manufacturing, Computational Materials Science 181 (2020) 109750.
Causality Discussion Group (August 2023) for the UAI '23 work.
UAI 2023 single-track oral presentation on Partial Identification of Dose Responses with Hidden Confounders.
USC Biostatistics Seminar (April 2023) on Latent Factor Discovery with Transcriptomics Data with Greg.
I am trying to make my work relevant in the battle against climate change, or healthcare. Feel free to reach out for possible collaborations.
09/12/2021 Toning down polarization in elections.
10/25/2020 Exponential smoothing, coupled with a primer on Bayesian inference.
RankedChoices.jl --- A Julia package to facilitate analysis of ranked preferences.
rolling-quantiles --- A Python package to quickly stream rolling quantiles via a backend written in C.
Check out my old website, a remnant of my aspirations for quantitative freelancing.
My undergraduate endeavors were marked by oft-unpublishable ambition.
I also co-hosted USC's first data-science hackathon for undergraduates in 2019.