Ayrton Almada

Ayrton Pablo Almada Jimenez

Ph.D. Candidate in Applied Mathematics | Complex Systems & Energy Networks

I am a Ph.D. candidate in Applied Mathematics at the University of Arizona, advised by Dr. Michael Chertkov and Dr. Laurent Pagnier. My research lies at the intersection of mathematical modeling, probability, stochastic processes, dynamical systems, and scientific computingstrong>. I develop theoretical and computational methods for analyzing complex dynamical and networked systems under uncertainty, with a particular focus on modern energy infrastructures. My work includes probabilistic risk assessment, rare-event analysis, Monte Carlo and importance-sampling methods, surrogate and reduced-order modeling, stability analysis, and large-scale simulation of transmission-level power networks and coupled energy systems.

My research experience spans academic, national laboratory, and international energy-system environments, including collaborative work at Los Alamos National Laboratory and provided consulting services for NOGA, the Power System Operator of Israel. I am currently a Research Aide Technical – PhD at Argonne National Laboratory in Lemont, Illinois, with an appointment running from September 2026 to March 2027. Within ESI Grid Reliability and Resilience, I work on real-time dynamic-contingency assessment and scientific software for fast screening of stability issues in large-scale power grids. I develop Python- and Julia-based research software and scalable computational workflows to translate mathematical ideas into practical simulation, inference, and decision-support tools. More broadly, I am interested in the intersection of applied mathematics, machine learning, artificial intelligence, optimization, and complex systems. I am particularly motivated by problems where rigorous mathematical modeling and modern data-driven methods can be combined to better understand, predict, and manage large-scale engineered systems.

Applied Mathematics & Complex Systems

Dynamical systems, stochastic processes, differential equations, network science, spectral methods, and mathematical modeling of complex systems.

Energy Systems

Power-grid dynamics, transient stability, resilience, interdependent energy networks, contingency analysis, and probabilistic risk assessment.

Probability & Uncertainty Quantification

Rare-event simulation, Monte Carlo methods, importance sampling, stochastic differential equations, and statistical inference.

Artificial Intelligence & Machine Learning

Machine learning, data-driven modeling, scientific machine learning, natural language processing, generative models, and AI for scientific discovery.

Optimization & Control

Optimization, control theory, operator-theoretic methods, and computational decision-making for large-scale systems.

Scientific Computing

Numerical methods, high-performance computing, parallel algorithms, simulation-based modeling, and reproducible computational research.

Research Aide Technical – PhD

–

Argonne National Laboratory, Lemont, Illinois

ESI Grid Reliability and Resilience

Current internship
  • Conduct research on real-time assessment of dynamic power-system contingencies, with emphasis on fast screening of stability issues in large-scale power grids.
  • Develop and improve computational methods for identifying potentially critical dynamic contingencies under real-time operational requirements.
  • Review existing approaches for dynamic contingency assessment and investigate methodological improvements where needed.
  • Design and implement algorithms as scientific software for scalable and computationally efficient grid-stability screening.
  • Perform computational analysis and validation of dynamic contingency-screening methodologies across large-scale power-system scenarios.
  • Collaborate with researchers in ESI Grid Reliability and Resilience on power-system modeling, algorithm development, and scientific software implementation.

Computer Science Graduate Internship Program

June 2023 – August 2023
January 2025 – August 2025

Los Alamos National Laboratory, New Mexico

Center for Nonlinear Studies

  • Developed computational frameworks for probabilistic assessment of complex systems using large-scale stochastic simulations.
  • Developed N1Plus, a computational framework for probabilistic dynamic assessment of transmission-level systems.
  • Applied Monte Carlo simulation and importance sampling to estimate probabilities of rare and high-impact events.
  • Developed Python- and Julia-based software for simulation, analysis, and visualization.
  • Collaborated with interdisciplinary researchers on mathematical modeling, algorithm development, and computational analysis.

Case Studies in Applied Mathematics and Data Science/Machine Learning

August 2023 – Present

University of Arizona

Program in Applied Mathematics and Department of Mathematics

  • Develop mathematical and statistical models for analyzing complex dynamical systems under uncertainty.
  • Apply probability, stochastic processes, differential equations, linear algebra, numerical methods, and optimization to large-scale quantitative problems.
  • Develop Monte Carlo and importance-sampling methods for estimating rare-event probabilities and quantifying system risk.
  • Design simulation-based methodologies for evaluating model behavior across stochastic scenarios.
  • Develop surrogate and reduced-order models to accelerate computationally intensive analyses.
  • Implement scalable computational algorithms in Python and Julia for simulation, statistical analysis, and model evaluation.

Research Assistant – Energy Systems

January 2024 – July 2024

University of Arizona

Program in Applied Mathematics and Department of Mathematics

  • Developed numerical solvers and stochastic models for analyzing complex networked systems under uncertainty.
  • Developed quantitative measures of system stress and damage.
  • Performed stochastic and ergodic analysis using Importance Sampling, the Cross-Entropy Method, and Kullback-Leibler-based sampling techniques.
  • Conducted sensitivity and uncertainty analyses to evaluate robustness under different scenarios.

  • Análisis espectral de procesos de difusión cambiantes
    Universidad Nacional Autónoma de México, Facultad de Ciencias, 2022
    [PDF]
  • Real-Time Stochastic Assessment of Dynamic N-1 Grid Contingencies
    Program in Applied Mathematics and Department of Mathematics, University of Arizona, 2025
    [PDF]
  • Real-Time Dynamic N-1 Screening: Identifying High-Risk Lines and Transformers After Common Faults
    Program in Applied Mathematics and Department of Mathematics, University of Arizona, 2026
    [PDF]
  • Risk-Aware Co-Simulation of Integrated Power and Gas Transients: Sampling Fuel-Switching Decisions During Supply Disruptions
    Pipeline Simulation Interest Group Annual Meeting, Amsterdam, Netherlands, 2026
    [Publication]

Real-Time Stochastic Assessment of Dynamic N-1 Grid Contingencies

Development of a computational framework for analyzing the dynamic response of transmission networks under N-1 contingency scenarios. The project combines stochastic modeling, transient stability analysis, Monte Carlo simulation, importance sampling, and analytical methods to evaluate system behavior and identify vulnerable network components.

Project Repository →

System-Agnostic Localization of Oscillations (SALO)

Implementation of the SALO algorithm for identifying the origin and frequency of forced oscillations in complex dynamical networks. The framework combines spectral analysis, Fourier transforms, maximum likelihood estimation, and optimization to infer external forcing from observable time-series data.

Project Repository →

N1Plus_SciML: Probabilistic Dynamic Grid Assessment

Development of computational methodologies for real-time probabilistic assessment of transmission-level power systems under faults and contingencies. The framework combines importance sampling, rare-event analysis, stochastic simulation, and dynamic stability indicators.

Project Repository →

2025 Grid Science Winter School and Conference

Los Alamos National Laboratory, Santa Fe, New Mexico

2026 Pipeline Simulation Interest Group Annual Meeting

Amsterdam, Netherlands, 2026

2026 Champéry Power Conference

Champéry, Switzerland, 2026

2026 Power Systems Computation Conference

Limassol, Cyprus, 2026

2026 PowerUp Conference

Boulder, Colorado, 2026

2027 Grid Science Winter School and Conference

Los Alamos National Laboratory, Santa Fe, New Mexico

Analysis of Partial Differential Equations Summer Graduate School

Okinawa Institute of Science and Technology, Japan, 2024

Dynamical Systems for Machine Learning and AI Summer Graduate School

IBM Research, Yorktown Heights, New York, 2026

Ph.D. in Applied Mathematics

Program in Applied Mathematics, University of Arizona

August 2022 – May 2027 (Expected)

Relevant Coursework: Methods for Applied Mathematics, Theoretical Foundations of Applied Mathematics, Algorithms

Research focuses on stochastic modeling, dynamical systems, uncertainty quantification, computational mathematics, and complex energy networks.

B.S. in Applied Mathematics

School of Sciences, Universidad Nacional Autónoma de México

August 2016 – January 2021

Relevant Coursework: Applied Mathematics Analysis, Statistics I–III, Stochastic Processes I–II, Numerical Optimization, Multivariate Analysis, Stochastic Simulation, and Partial Differential Equations.

Thesis: The Spectral Analysis of Switching Diffusion Processes and their Applications

Email: p.ayrtonalmada@gmail.com

LinkedIn: Ayrton Almada

GitHub: AyrtonAlmada

Resume: Ayrton Almada Resume

Google Scholar: Profile