I am a French-Cuban Physicist and Data Scientist, currently residing in Paris, France. My academic journey began in 2004 at the University of Havana, where I earned my B.Sc. in Physics while simultaneously completing all coursework for the Mathematics curriculum. During my studies, I also gained teaching experience, leading laboratory classes in Mechanics, Electromagnetism, and Optics for students from various scientific disciplines. After graduating in 2009 (Cuban university programs span five years), I pursued a M.Sc. in Physics with a specialization in Theoretical Physics, which I completed in 2011. Concurrently, I worked as a Junior Researcher at the Institute of Cybernetics, Mathematics, and Physics (ICIMAF) and taught tutorials in Ordinary Differential Equations and Calculus of Variations to physics and engineering students.

In 2012, I moved to Trieste, Italy, on a scholarship for the Diploma Programme in High Energy Physics at the ICTP, where I conducted research on Neutrinoless Double β-decay and Physics Beyond the Standard Model. After a highly competitive selection process, I was awarded another scholarship in 2013 to join the Joint Ph.D. Programme at ICTP and SISSA in Astroparticle Physics. My Ph.D. thesis, successfully defended in 2017, combined phenomenology, data analysis, and machine learning, with a focus on dark matter at galactic scales and empirical velocity profiles.

Post-Ph.D., I relocated to Paris for a postdoctoral role at LPNHE, contributing to the XENON1T experiment. My work involved academic research, data processing, analysis, and the search for rare events that might indicate the presence of dark matter particles in collision with Xenon nuclei, a mission I continued until the end of 2019, publishing articles in Physical Review Letters, the Monthly Notices of the Royal Astronomical Society and Nature.

The Covid-19 pandemic marked a turning point in my career as I transitioned to Data Science, motivated by a passion for predicting behaviors across diverse domains. Since then, I've applied my expertise across sectors such as Cybersecurity, Predictive Medicine, and Risk Management. My multicultural and multilingual background has been pivotal in fostering collaboration and communication, making me a valuable asset in dynamic, diverse environments.

• 🌌⚛︎Post Doc. Nuclear Physics and Astroparticles -------------------- 10/2017 - 09/2019

  • Laboratoire de Physique Nucléaire et de Hautes Énergies (LPNHE, CNRS/IN2P3), Paris, France
  • Single-author publications in MNRAS; co-author publications in Nature and PRL within the Xenon1T Collaboration

• 🔭🌌 Ph.D. Astroparticle Physics (GPA: 4.0/4.0) -------------------- 10/2013 - 09/2017

  • Scuola Internazionale Superiore di Studi Avanzati (SISSA), Trieste, Italy
  • Research thesis: “Dark Matter and Galaxy Rotation Curves”

• ∫🎓 Postgraduate Diploma in High Energy Physics (GPA: 3.9/4.0) -------------------- 09/2012 - 08/2013

  • “Abdus Salam” International Centre for Theoretical Physics (ICTP), Trieste, Italy
  • Research thesis: “Neutrinoless Double Beta Decay and Physics Beyond the Standard Model”

• ∫🎓 M.Sc. Physical Sciences (GPA: 4.0/4.0) -------------------- 10/2009 - 12/2011

  • University of Havana, Havana, Cuba
  • Research thesis: “Strangelets magnetizados a temperatura finita”

• ∫🎓 Licenciado en Fı́sica – 5-year Integrated Physics Degree (GPA: 3.8/4.0) -------------------- 09/2004 - 08/2009

  • University of Havana, Havana, Cuba
  • Research thesis: “Materia de quarks magnetizada en equilibrio estelar”
  • Advanced coursework in Topology, Differential Geometry, Functional Analysis, Statistical Physics, Quantum Mechanics, Mathematical and Computational Physics

I bring the analytical rigor of a PhD astrophysicist together with the execution discipline of a senior AI engineer. I design and deliver AI systems that withstand scientific scrutiny, regulatory pressure, and production constraints. My work spans regulated finance, healthcare AI, high-risk forecasting, and EU-funded R&D programs, with a consistent focus on turning advanced models into deployable, auditable, and value-generating solutions. Core value pillars

• Methodological rigor grounded in first principles and statistical robustness
• Production-level code quality, scalability, and maintainability
• Compliance-first workflows (governance, traceability, explainability)
• Cross-functional delivery at the intersection of research, engineering, and business
• Fluent collaboration in Spanish, French, English, and Italian

Impact

• Proven track record delivering AI solutions adopted by banks, medical environments, and R&D organizations, where reliability, transparency, and operational risk management are non-negotiable.

• Spearhead R&D in AI and data science with focus on ethical data anonymization, large language models (LLMs), and explainable AI (XAI).
• Lead the development of NexiaSynth, a proprietary data anonymization platform strengthening compliance and security in the financial sector.
• Designed and deployed RAG chatbots @MaineCoon taylored for financial clients, enhancing operational efficiency and user interaction.
• Allocate and manage R&D resources, budgets, and project timelines, ensuring delivery within constraints while maintaining technical excellence.
• Mentor and coach junior data scientists, organize technical webinars, and oversee talent development within the data science division.
• First-author internal research publications on:
  • Interpretable ML algorithms (parametric vs. non-parametric models).
  • Feature importance and correlation in supervised ML (LIME, SHAP, PFI).
  • Fast calibration of implied volatility models (SVI, CatBoost, Random Forest, metaheuristic optimizers).

Technical Stack: Python · R · R Shiny · Scikit-learn · CatBoost · SHAP · LIME · Docker · Git · Dataiku DSS · SQL

• Delivered data science and quantitative analytics solutions to financial clients, including model validation, XAI frameworks, and risk analytics.
• Supported regulatory-driven AI projects by implementing interpretable ML methods and reproducible pipelines.
• Collaborated with risk managers, IT developers, and quantitative analysts in an Agile R&D environment.
• External Consultant IT-Quant for Société Générale.

Technical Stack: R Shiny · R · Git · VS Code · Risk Management · Risk Modeling · Dataiku DSSR Shiny · R · Git · VS Code · Risk Management · Risk Modeling · Dataiku DSS · Alteryx

• Built and enhanced the Model Performance Platform (MPP) for back-testing risk models (Probability of Default – PD, Loss Given Default – LGD, and Credit Conversion Factor – CCF).

• Integrated ECB-regulated Key Risk Indicators (KRIs) into predictive analytics workflows, ensuring compliance with European Central Bank standards.

• Developed backend logic for KRIs in R (Dataiku DSS, PostgreSQL) and created interactive R-Shiny dashboards for real-time monitoring and visualization.

• Constructed S3 datasets in Dataiku, enabling efficient storage and processing for model validation.

• Conducted statistical hypothesis testing, data validation, and predictive analytics to support model governance and regulatory reporting.

• Participated in committee meetings with risk modelers, data engineers, and governance teams, presenting platform progress and results to stakeholders.

• Worked in an Agile development environment, implementing unit tests, automation, and CI/CD best practices with Python and Git.

• Key Achievements:

  • Strengthened Société Générale’s model risk governance by providing a scalable back-testing framework aligned with ECB regulatory expectations.
  • Delivered a robust R-Shiny interface enabling stakeholders to monitor KRIs, track model stability, and ensure transparency in model performance.

Technical Stack: R Shiny · R · Git · VS Code · Risk Management · Risk Modeling · Dataiku DSS · PostgreSQL

• Provided data-driven support to the Risk Management Department for settlement risk and stress testing.
• Designed Python-based simulation models to replicate settlement instruction logic, achieving 98% precision in predicting exposures, Loss Given Default (LGD), and potential domino effects of client defaults.
• Automated financial reporting pipelines using Hadoop and SQL, enabling efficient analysis of 100GB+ datasets.
• Developed and monitored interactive Power BI dashboards:
  • NewsJournal: delivered financial sentiment analysis of Euroclear Bank using news and market reviews.
  • CCPJournal: tracked settlement activities of Central Counterparties (CCPs), computed exposures, margins, and generated weekly stakeholder financial reviews. -Collaborated with risk officers and analysts to enhance transparency, accuracy, and regulatory compliance in financial risk reporting.

Key Achievements:

• Strengthened Euroclear’s stress testing framework by building accurate, reproducible simulations of settlement exposures.
• Improved risk monitoring efficiency and stakeholder reporting through automation and visualization.

Technical Stack: Microsoft Power BI · VS Code · Sentiment Analysis · Risk Management · High Performance Computing · Leadership · Python · SQL

• Designed and deployed machine learning pipelines for financial automation, specifically automating invoice categorization and achieving 96% AUC in classification accuracy on validation samples.
• Developed forecasting models using exponential smoothing techniques, reducing forecasting error rates to under 4%, enhancing financial planning accuracy.
• Streamlined production deployment by containerizing AI services with Docker, speeding up iteration and deployment in an Agile development environment.
• Built scalable ML solutions to meet critical SME operational needs, balancing technical innovation with business utility.

Core Contributions:

• Embedded advanced ML into small-business workflows, for invoice classification and demand forecasting, delivering tangible performance improvements and operational efficiency.
• Demonstrated startup adaptability: rapid prototyping, deployment, and solution delivery under tight constraints.

Technical Stack: Data Analytics · Machine Learning · Python · Leadership · Machine Learning Pipelines · Forecasting Models · Docks · Agile Application Development

• Led the development and benchmarking of machine learning survival models for predicting kidney graft survival, comparing against the iBox Cox Proportional Hazards model.
• Processed and analyzed 8200+ anonymized patient records across derivation (n=4000, France) and external validation cohorts from Europe, South America, and North America.
• Designed and validated models including Random Survival Forests, Conditional Survival Forests, Extra Survival Trees, Neural Multi-Task Logistic Regression, and ensemble meta-models (Boosting, Stacking).
• Improved predictive performance with C-index up to 0.884, exceeding the iBox baseline (0.808) in derivation cohorts.
• Applied data preprocessing, imputation, and calibration techniques for right-censored clinical survival data.
• Produced reproducible analyses in Python (NumPy, Pandas, Matplotlib, PySurvival, Scikit-learn) within a TRIPOD-compliant framework.
• Documented and presented findings in line with clinical, regulatory, and academic standards, contributing to the group’s scientific projects.

Key Achievements:

• Demonstrated that advanced ML survival models can complement or surpass classical approaches in certain cohorts, providing new clinical decision-support insights.
• Delivered preliminary results that strengthened Paris Transplant Group’s research output and international collaborations.

Technical Stack: Predictive Analytics · Survival Analysis · Clinical Data Science · Model Benchmarking · Data Preparation · Validation & Calibration · Reproducible Research · Scientific Communications · Backtesting

• Led predictive analytics initiatives within the H2020 FINSEC project, focused on collaborative security for financial infrastructures.
• Developed anomaly detection and behavioral analytics use cases for financial clients, applying AI/ML methods to risk assessment and fraud detection.
• Reviewed and contributed to the Reference Architecture of the INFINITECH project, aimed at advancing Big Data/AI/IoT innovation in finance and insurance ecosystems.
• Presented predictive analytics solutions and use case demonstrations at a consortium meeting (Madrid, Feb 2020).
• Conducted training workshops on AI and innovation for multidisciplinary teams, strengthening knowledge transfer and adoption.
• Authored project deliverables and technical documentation in line with European Commission regulatory and compliance standards.

Key Achievements:

• Bridged expertise from computational physics and big data into applied AI/ML solutions for finance and cybersecurity.
• Strengthened ORT France’s contribution to flagship EU innovation projects by delivering technical insights and stakeholder engagement.technical insights and stakeholder engagement.

Technical Stack: Git · DevOps · Leadership · Data Analytics · Python · Predictive Analytics · Anomaly Detection · Behavioral Analytics · AI/ML · Data Science · Agile Project Management · Technical Training · EU R&D Frameworks · Stakeholder Collaboration

• Conducted data-driven research in one of the world’s leading dark matter detection experiments (XENON1T), analyzing large-scale experimental datasets and running advanced simulations.
• Designed and optimized Monte Carlo simulations of particle interactions and detector responses, implemented in C++ and executed on high-performance computing clusters.
• Performed back-testing and validation of physical models against real detector data using Python, and statistical analysis frameworks.
• Collaborated with an international consortium of physicists, contributing to high-impact results published in Nature, Physical Review Letters, and other leading journals.
• Independently authored a first-author paper on empirical velocity profiles for galactic rotation curves (Mon. Not. R. Astron. Soc. 475 (2018)), demonstrating initiative and independent research capacity.

Key Achievements:

• Contributed to high-visibility scientific results published in Nature and PRL, recognized globally within the physics community.
• Developed strong expertise in large-scale data analysis, Monte Carlo methods, high-performance computing, and statistical modeling, with direct applications in data science, quantitative research, and advanced simulations.

Technical Stack: Python · C++ · Monte Carlo Simulation · Statistical Modeling · High Performance Computing · Statistical Data Analysis · Large-Scale Datasets · International Collaboration · Scientific Communications · Scientific Papers · Scientific Research · Research Development

• Completed a Ph.D. thesis on dark matter distribution in spiral galaxies, analyzing large-scale astronomical datasets (galaxy rotation curves and astrophysical measurements) to uncover mass distribution patterns.
• Designed and maintained DMROT, a custom software package in Wolfram Mathematica, enabling:
  • Non-linear regression models and parameter estimation on galaxy rotation curve datasets.
  • Correlation analysis and predictive modeling for galactic properties (virial radius, stellar/gaseous masses, rotational velocities).
  • Automated data visualization and generation of high-quality plots for complex datasets.
• Conducted end-to-end data workflows, from image-based galaxy data extraction to model validation and statistical analysis.
• Explored modified theories of gravity and empirical modeling to improve predictions of dark matter behavior, demonstrating strong problem-solving and innovation.
• Published peer-reviewed articles as first author, presented research in seminars, and collaborated within international research teams.

Key Achievements:

• Introduced a novel empirical method to estimate local dark matter density, contributing to improved astrophysical modeling.
• Published as first author a peer-reviewed paper Mon. Not. R. Astron. Soc. 468 (2017), demonstrating expertise in modeling, simulation, and data-driven research.

Teaching & Knowledge Transfer:

• Delivered seminars and progress presentations to multidisciplinary audiences, translating complex scientific results into clear insights.
• Mentored undergraduate and graduate students on data analysis methods, regression modeling, and visualization techniques, fostering analytical and coding skills.
• Assisted in the supervision of research projects, providing technical guidance, code reviews, and methodological support.
• Developed and shared technical documentation for DMROT, ensuring usability and reproducibility by other researchers.

Technical Stack: Data Analysis · Predictive Modeling · Non-linear Regression · Statistical Methods · Software Development · Scientific Visualization · Wolfram Mathematica · Computational Modeling · Communication ResearchSkills: Data Analysis · Predictive Modeling · Non-linear Regression · Statistical Methods · Software Development · Scientific Visualization · Wolfram Mathematica · Computational Modeling · Communication Research

• Completed an intensive postgraduate diploma in High Energy Physics, specializing in advanced modeling, simulation, and data analysis.
• Designed and executed research projects involving analytical modeling and numerical simulations of complex particle interactions.
• Applied statistical methods and computational techniques to calculate probabilities, optimize models, and test theoretical predictions.
• Collaborated in an international, cross-disciplinary research environment, enhancing communication and teamwork skills.
• Pursued independent projects, demonstrating initiative and the ability to deliver results with minimal supervision.

Key Achievements:

• Introduced a novel empirical method to estimate local dark matter density, contributing to improved astrophysical modeling.
• Published as first author a peer-reviewed paper Mon. Not. R. Astron. Soc. 468 (2017), demonstrating expertise in modeling, simulation, and data-driven research.

Technical Stack: Mathematical Modeling · Numerical Simulation · Statistical Analysis · Computational Methods · Creative Problem Solving · Cross-Disciplinary Collaboration

• Designed and implemented computational models and simulations to study matter under extreme densities and intense magnetic fields, applying advanced numerical methods and optimization techniques.
• Developed high-performance algorithms in Fortran-90 to solve nonlinear integral and differential equations, improving simulation accuracy and computational efficiency.
• Built Molecular Dynamics simulations in C++ using the Velocity-Verlet algorithm, enabling large-scale modeling of solid-state systems.
• Applied data analysis, visualization, and mathematical modeling to evaluate system stability and predict physical properties, leveraging both statistical and theoretical approaches.
• Collaborated with senior researchers and delivered findings through technical reports, peer-reviewed publications, and international conferences.
• Provided tutorials on applied mathematics (ODEs, Calculus of Variations) to undergraduate students, strengthening communication and mentoring skills.

Key Achievements:

• Authored three peer-reviewed publications as first author, demonstrating expertise in modeling, simulation, and data-driven research.
• Successfully translated complex physical theories into computational frameworks with real-world applications in high-performance computing and systems modeling.

Technical Stack: Astrophysics · Numerical Methods · Algorithm Development · Computational Modeling · Fortran-90 · C++ · Molecular Dynamics · Applied Mathematics · Data Analysis · Scientific Communications · Research Projects · Scholarly Research

• Python: Numpy, Pandas, Scikit-Learn, Flask, PySurvival, Lifelines.
• R: R-Studio, R-Markdown, R-Shiny.
• Deep Learning: Tensor Flow, Keras, PyTorch
• Databases: SQL, PostgreSQL, Hadoop (Cloudera), MongoDB.
• DevOps: Git, VS Code, Docker, AWS.
• DataViz & Analytics: Matplotlib, Seaborn, Plotly, Microsoft Power BI (DAX), H2O.ai.
• XAI: LIME, SHAP, ELI5.

• Dataiku: Core & Advanced Designer, Developer, ML & MLOps Practitioner.
• Alteryx: Foundational & Designer Core Micro-Credential.
• Udemy: Deployment of Machine Learning models.
• Power BI: Data analysis and management with Microsoft Power BI.
• LinkedIn: Financial Modeling Foundations, Excel for Business Analysts.
• LinkedIn: DevOps for Data Scientists, Creating an App with Machine Learning.

• 🇬🇧 🇺🇸 English (C1)
• 🇫🇷 French (C1)
• 🇮🇹 Italian (C1)
• 🇪🇸 Spanish (native)

• Check the link

• 📧 ernestolopezfune@gmail.com
• 📍 75020 Paris, France

Photography, Traveling, Fitness

• Jury's very favorite of the photography contest Un (dix) dans l'image, issued by Wipplay photography review.
• 3rd place of the photography contest ABYSSES, issued by Wipplay digital photography magazine in partnership with Jeu de Paume, Paris, France.
• 3rd place of the photography contest Teenage Portrait(s), issued by Wipplay digital photography magazine in partnership with Rendez-Vous Photographique Ville De Vichy, Paris, France.
• 3rd place of the photography contest H2O, issued by Wipplay digital photography magazine in partnership with Lomography.
• 3rd place of the photography contest HER WAY, issued by Wipplay digital photography magazine in partnership with Bioderma.
• 1st place of the photography contest IL ETAIT UNE FOIS DEMAIN, issued by Caisses d’Epargne et Polka Magazine.
• 1st place of the photography contest HOT SNAPSHOT, issued by Focus Numerique et Wipplay photography review.
• 2nd place of the photography contest ANIMAUX, issued by Wipplay photography review.
• 3rd place of the photography contest STAINED GLASS, issued by Cité de l’architecture et du Patrimoine.
• Jury's very favorite of the photography contest MA VIE DES HALLES, issued by Wipplay photography review.
• Scholarship: Joint PhD Programme ICTP/SISSA in Astroparticle Physics, issued by Abdus Salam International Centre for Theoretical Physics (ICTP), and Scuola Internazionale Superiore di Studi Avanzati (SISSA), Trieste, Italy.
• Scholarship: Postgraduate Diploma Programme in High Energy Physics, issued by Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste, Italy.
• Award to the Best Research Work done by a Young Physicist of the XII Symposium of the Cuban Society of Physics 2011, issued by Cuban Society of Physics, associated with Institute of Cybernetics Mathematics and Physics.
• Mention to the Best Researcher Student of the Physics Faculty's 2009 promotion.