Alex Rodriguez

Assistant Professor

University of Trieste

Italy

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About

Dr. Alex Rodriguez is an Assistant Professor at the University of Trieste, Italy. His research focuses on unsupervised learning, particularly in the context of quantum systems and big data. Recent publications include studies on aqueous electrolyte solutions, network science, and the complexities of quantum many-body systems. He is dedicated to exploring innovative methodologies in data-driven discovery and statistical relevance in various scientific fields.

Articles (19)

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2025

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2020

Collaborators (6)

irene conti

University of Bologna

ITALY

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Ivan Rivalta

Professor

University of Bologna

ITALY

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Hannah Williams

Assistant Professor

University of Durham

UNITED KINGDOM

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Hanlin Sun

Graduate Teaching Assistant

King’s College London

UNITED KINGDOM

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dario poletti

Singapore University of Technology and Design

SINGAPORE

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Alessandro LAIO

Full professor

Scuola Internazionale Superiore di Studi Avanzati

ITALY

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Alex Rodriguez

Research Interests

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About

Articles (19)

Density estimation via binless multidimensional integration

Unsupervised detection of semantic correlations in big data

Opportunities and Challenges in Unsupervised Learning: The Case of Aqueous Electrolyte Solutions

Wave-Function Network Description and Kolmogorov Complexity of Quantum Many-Body Systems

Network science: Ising states of matter

Beyond Local Structures in Critical Supercooled Water through Unsupervised Learning

Topological Kolmogorov complexity and the Berezinskii-Kosterlitz-Thouless mechanism

Data-driven discovery of statistically relevant information in quantum simulators

Zirconium Coordination Chemistry and Its Role in Optimizing Hydroxymate Chelation: Insights from Molecular Dynamics

High-Throughput Screening of Promising Redox-Active Molecules with MolGAT

Complexity of spin configuration dynamics due to unitary evolution and periodic projective measurements

ZundEig: The Structure of the Proton in Liquid Water from Unsupervised Learning

The carbonyl-lock mechanism underlying non-aromatic fluorescence in biological matter

Unsupervised Learning Methods for Molecular Simulation Data

Intrinsic Dimension of Path Integrals: Data-Mining Quantum Criticality and Emergent Simplicity

Automatic topography of high-dimensional data sets by non-parametric density peak clustering

Unsupervised Learning Universal Critical Behavior via the Intrinsic Dimension

Candidate Binding Sites for Allosteric Inhibition of the SARS-CoV-2 Main Protease from the Analysis of Large-Scale Molecular Dynamics Simulations

Explicit Characterization of the Free-Energy Landscape of a Protein in the Space of All Its Cα Carbons

Collaborators (6)

irene conti

Ivan Rivalta

Hannah Williams

Hanlin Sun

dario poletti

Alessandro LAIO

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