I transform complex, noisy data—from brain signals to environmental exposures—into robust models and actionable insights. With over a decade of experience analyzing non-stationary systems, I help organizations move beyond simple correlations to find genuine, predictive signals that drive evidence-based decisions.

I apply rigorous, peer-reviewed computational methods to solve messy, real-world problems with public health impact. Whether it's detecting early health warnings or quantifying environmental injustice, I build the mathematical bridge between raw data and strategic action.

• Advanced Time Series Analysis: Univariate & Multivariate Modeling, Spectral Analysis, Non-Stationary Signal Processing, Panel Data Econometrics.
• Network Science & Graph Theory: Physiological and anthropometric network inference, spatio-temporal interrelation patterns.
• Statistical Modeling & Machine Learning: Genuine cross-correlation measures, fixed effects panel regression, surrogate data testing, cluster-robust inference.
• Geospatial Analysis: Spatial epidemiology, environmental exposure assessment, GIS integration (geopandas, shapefiles).
• Languages & Tools: Python (Pandas, NumPy, SciPy, Statsmodels, Scikit-learn, NetworkX, Geopandas, Plotly), MATLAB, SQL.
• Big Neuroscience Data: Expert-level experience with MEG, fMRI, and EEG data (Human Connectome Project, 1,200+ subjects).

🌎 Geospatial Analysis of Air Pollution & Cancer Mortality in Mexico City (2026) Stack: Python (Statsmodels, Geopandas, Plotly) | Zenodo | INEGI Census Data

• Built a 19-year panel study across 16 alcaldías, integrating harmonized census data, mortality records, and 35 air quality monitoring stations to quantify the PM₂.₅–lung cancer mortality association.
• Result: Found that a 10 μg/m³ increase in PM₂.₅ is associated with a 2.10 per 100,000 increase in mortality (p=0.090), translating to approximately 193 preventable lung cancer deaths annually in CDMX. Identified traffic-related NO₂ as the strongest correlate and documented a 1.7× spatial inequity between northern and southern alcaldías. All data and code published open-access on Zenodo and GitHub.

🔬 Public Health Surveillance & Physiological Networks

• Built multivariate statistical models on heart rate, activity, and blood biomarkers to characterize physiological networks at individual and group levels.
• Result: Methods directly translatable to public health early-warning systems for detecting health deterioration.
• Key Publication: "Physiological network from anthropometric and blood test biomarkers" (Frontiers in Physiology, 2020).

🧠 Decoding the Brain with Genuine Correlations

• Led the development of novel time-series and graph-theoretical methods to detect hidden correlations in highly non-stationary brain signals (MEG/EEG), proving that stable patterns exist in chaotic recordings.
• Result: Advanced the fundamental understanding of epileptic networks and brain connectivity.
• Key Publications: "Stationary correlation pattern in highly non-stationary MEG recordings" (PLOS One, 2024); "Genuine cross-correlations" (Neural Networks, 2013).

🌱 R&D Strategy & Precision Agriculture

• Developed sensor-based automation and optimization models for greenhouse production. Synthesized complex regulatory data across Latin America into actionable market reports.
• Result: Successfully aligned multi-sectoral partnerships (government, private, academia) with national R&D investment priorities.

1. Marín-García, A. (2026). Geospatial Analysis of Air Pollution and Cancer Mortality in Mexico City [Data & Code]. Zenodo/GitHub.
2. Marín-García, A. et al. (2024). Stationary correlation pattern in highly non-stationary MEG recordings. PLOS One.
3. Marín-García, A. et al. (2013). Genuine cross-correlations: Which surrogate based measure reproduces analytical results best? Neural Networks.
4. Barajas-Martínez, A., Marín-García, A. et al. (2020). Physiological network from anthropometric and blood test biomarkers. Frontiers in Physiology.

I am open to consulting, research collaborations, and full-time roles where rigorous computational science meets critical public health, environmental, or societal challenges.

• Areas I'm passionate about: Environmental Epidemiology, Predictive Health Analytics, Climate & Health, Computational Neuroscience, and Data-Driven Policy.
• Looking for: Senior Data Scientist, Quantitative Researcher, or Environmental Health Data Scientist roles.
• Beyond Science: I also use visual mediums to tell stories, having recently collaborated with the Mexican Embassy in Finland on a cultural documentary photography project.

📫 How to reach me: [www.linkedin.com/in/arlex-marin-garcia]