Website: https://sites.google.com/view/ryomoore/home

I obtained my doctoral degree in mathematics from the University of North Carolina at Chapel Hill (USA) under the supervision of Prof. Idris Assani. Since then, I have conducted postdoctoral research at Pontificia Universidad Católica de Chile and at Southern University of Science and Technology (China).

My research has focused on ergodic theory, a branch of dynamical systems that studies the statistical behavior of chaotic systems. I have worked on several topics in ergodic theory, including Wiener–Wintner systems, nonconventional ergodic averages, multifractal analysis, and partially hyperbolic diffeomorphisms.

Currently, I am interested in analyzing mathematical propervies of various information measures—such as entropy, divergence, and mutual information—drawing on my background in functional analysis, measure theory, and probability.

I am an applied mathematician with over a decade of experience in mathematical physiology and systems biology. I specialise in modelling complex biological processes, including calcium signalling, cellular electrophysiology, and cancer cell metabolism. My work includes four and a half years at the Institute of Metabolism and Systems Research at the University of Birmingham, England, where I focused on modelling cancer biology. Currently, I am pursuing a theoretical approach to cellular metabolism, aiming to develop a fundamental theory to improve model accuracy and provide deeper insights for oncometabolism.

Selected research: 

[1] Vera-Sigüenza, Elías, et al. "A mathematical model of fluid transport in an accurate reconstruction of parotid acinar cells." Bulletin of mathematical biology 81 (2019): 699-721.

[2] Vera-Sigüenza, Elías, et al. "Pyruvate from bone marrow mesenchymal stem cells supports myeloma redox homeostasis and anabolism." bioRxiv (2024): 2024-08.

[3] Vera-Sigüenza, Elías, et al. "A Mathematical Exploration of SDH-b Loss in Chromaffin Cells." Bulletin of Mathematical Biology 87.4 (2025): 1-36.

Ph.D. 2020 (University of Auckland New Zealand)

I am a postdoctoral researcher at the intersection of Statistics, Probability and Stochastic Processes.

I like to use probability and stochastic process theory to build capability in and understanding of statistical topics such as modelling, inference and estimation. In particular, I am interested in questions relating to dependent and/or discontinuous data, such as Markov chains, Markov processes and Stochastic Processes more widely.

I am currently working on error bounds and sample complexity for statistical estimation and mixing theory of classes of “structured” Markov chains.

Before joining OIST, I obtained my PhD from the University of Warwick (UK) and was a Research Fellow at the University of Birmingham (UK).

Selected articles:
[1] Judd, N. A. and Spanò, D. Subordinated Wright-Fisher priors. April 2026. arXiv: 2604.11363 [math.ST]. https://arxiv.org/abs/2604.11363

[2] Judd, N. A. et al. Forecasting hourly foodservice sales during geopolitical and economical disruption using zero-inflated mixed effects models. Journal of Applied Statistics 53, 372–390. eprint: https://doi.org/10.1080/02664763.2025.2519136. (2026)

[3] Judd, N. A. et al. Statistical Crime Linkage: Evaluating approaches within the Covenant for Using AI in Policing. October 2025. arXiv: 2510.03730 [stat.AP]. https://arxiv.org/abs/2510.03730

I am a third-year PhD student at EPFL in the Laboratory for Information in Networked Systems (LINX). Previously, I was an algorithms researcher at Entropica Labs, a quantum computing startup in Singapore. Before that, I completed both my Bachelor’s and Master’s degree at EPFL.

I am fortunate to be co-advised by Prof. Michael Gastpar (EPFL) and Prof. Amedeo Esposito (OIST). My research interests span a variety of topics, including Information Theory, Probability Theory and Statistics.

I am a Master’s student in Computer Science at Hamburg University of Applied Sciences, Germany, where I specialize in data science. Before joining OIST, I worked as a researcher on generative machine learning and synthetic tabular data.

Short Bio: My research interests are primarily in quantum physics and statistical physics, and I have worked on various aspects of these fields, including quantum thermodynamics, non-equilibrium and nonlinear dynamical systems. As part of my current rotation, I am taking my first steps into information theory and learning how it can be applied to problems in physics. I am particularly interested in understanding how probabilistic and statistical approaches can help us study complex physical systems, and in exploring the interplay between statistical physics, information theory, and related mathematical frameworks.

I got my Master degree of Physics from Sharif University of Technology, where I worked on Complex Systems and Stochastic processes with applications in Biology and Economy. After a research internship program at OIST, I am studying as a PhD student. In particular, I am interested in Stochasticity, Non-equilibrium Physics, Information Theory, and Manifolds. Beyond calculations, I enjoy philosophy, games, and arts.

I am a Computer Scientist. I received my Master’s degree in Computer Science from the University of Salerno, Italy, in 2022, and I am currently pursuing a Ph.D. there under the supervision of Prof. Ugo Vaccaro. 

My research interests include Information Theory, Data Structures, and Search Algorithms. 

Currently, my work focuses primarily on search procedures and on the construction and analysis of particular classes of binary prefix and/or alphabetic codes.

I am a master’s student in Machine Learning at MBZUAI, UAE, where I work on optimization for machine learning under the supervision of Dr. Samuel Horváth. Prior to this, I completed my undergraduate degree in Electrical Engineering at New York University Abu Dhabi. At OIST, my internship focuses on understanding the performance of gradient-based optimization in variational (Bayesian) inference, and on characterizing its convergence rate through the study of the underlying geometry.