I am pursuing my doctorate under Professors Greg Ver Steeg and Aram Galstyan at the USC Information Sciences Institute. My contributions focus on the unwieldy contexts for statistical inference that demand efficient, robust, and thoughtful methods. In particular, I care about scenarios where our observations are not enough to tease out the intricacies of complicated interactions. I tailor my data-driven approaches to social, physiological, and economic systems.
09/12/2021 Toning down polarization in elections.
10/25/2020 Exponential smoothing, coupled with a primer on Bayesian inference.
I am actively seeking avenues for making a mark in our battle against climate change, either through a sociopolitical or an economic lens. Feel free to reach out with your wacky ponderations.
My entrepreneurial aspirations lie in the field of promoting long-term wellness and health through novel uses of comfortable noninvasive instruments.
During my undergraduate studies, I developed a technique for data-driven stochastic optimization in the context of additive manufacturing with the supervision of Professor Roger Ghanem. I also spent two summers at Bloomberg LP as a machine learning and software engineering intern, and worked part-time for a semester at Research Affiliates, LLC alongside the head of the Investment Systems team.
M. G. Marmarelis, G. Ver Steeg, and A. Galstyan,
Latent Embeddings of Point Process Excitations, arXiv:2005.02515. Submitted to NeurIPS 2020.
M. G. Marmarelis and R. G. Ghanem,
Data-driven Stochastic Optimization on Manifolds for Additive Manufacturing, Computational Materials Science 181 (2020) 109750.
Public source code.
A Logistic Point Process with Modulated Excitations. Associated preprint coming soon!
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.