Vincenzo Coia, Ph.D., P.Stat.

Vincenzo Coia, Ph.D., P.Stat.

Statistician, combining research and practice for probabilistic and risk modelling in the earth sciences.

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About Me

I’m a statistician working at the intersection of probability, data, and real-world systems. My work focuses on problems where uncertainty is high, data is imperfect, and the consequences of modeling decisions are significant — particularly in climate and hydrology.

I’m drawn to a single question that runs through everything I do: what’s possible, not just what’s most likely? That question leads naturally to compound risk, dependence between hazards, and the behavior of extremes — the regimes where standard statistical methods tend to break down.

For the full record, the CV above has the details.

Innovation & Forward Thinking

My work focuses on expanding how we answer the question, “What’s possible?"—not just “What’s most likely?” I draw on the strengths of both classical statistics and machine learning, building probabilistic models of complex, interdependent systems. My work often centers on understanding extremes in climate and hydrology, where I bridge theoretical innovation with practical application.

The following are initiatives I’m leading.

Deciphering compound rain-on-snow flood pathways as observed from satellites
Deciphering compound rain-on-snow flood pathways as observed from satellites

A research activity under the European Space Agency's (ESA) Open Space Innovation Platform (OSIP), from September 2025 - August 2027.

Sep 15, 2025
Probaverse
Probaverse

The probaverse is a suite of tools in R for exploring the full space of possible outcomes in an analysis. It elevates probability distributions as tangible objects that realistically represent your system.

Jan 30, 2020
Scholarly Publications

Articles in scientific journals, proceedings, and preprints.

Aron Zahradka, Vincenzo Coia, Patrick Grover, Alex Mitchell, Michael Porter, Troy Adair (2025). Applying Machine Learning to Geohazard Management: Efficient Assessment of Pipeline Watercourse Geohazards by Learning from 3 Million Kilometer-Years of Geohazard Field Inspections. Proceedings of the 20th Pipeline Technology Conference.
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Hebatallah M. Abdelmoaty, Simon Michael Papalexiou, Antonios Mamalakis, Shivam Singh, Vincenzo Coia, Melissa Hairabedian, Pascal Szeftel, Patrick Grover (2025). Generative Adversarial Networks for Downscaling Hourly Precipitation in the Canadian Prairies. Journal of Geophysical Research: Machine Learning and Computation.
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Philip LeSueur, Sophia Zubrycky, Scott McDougall, Vincenzo Coia, Alex Strouth, Michael Porter (2025). Vulnerability of People in Wood-Frame Buildings to Rapid Flow-Type Landslides. Natural Hazards.
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Vincenzo Coia, Harry Joe, Natalia Nolde (2024). Copula-based conditional tail indices. Journal of Multivariate Analysis.
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Stephanie Bale, Vincenzo Coia, Brad Russell, Daley Clohan (2024). Probability Distributions of Tailings Dam Breach Volumes by Failure Mode as Part of a Risk Screening-Level Tool. Proceedings of Tailings and Mine Waste 2024.
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Experience

  1. Research Sabbatical

    Politecnico di Milano

    Responsibilities include:

    • Compound event modelling for climate extremes using satellite data.
    • Developing probabilistic model for rain-on-snow flooding in the Alps.
    • Partnership with the European Space Agency.

  2. Senior Data Scientist

    BGC Engineering Inc.

    Responsibilities include:

    • Probabilistic modelling lead for advanced earth science applications.
    • Collaborate with Engineers and Geoscientists.
    • Produce robust and reproducible codebases using R, git, and friends.

  3. Assistant Professor of Teaching (Dept. of Statistics)

    The University of British Columbia

  4. Lecturer (Dept. of Statistics)

    The University of British Columbia

  5. Postdoctoral Teaching and Learning Fellow

    The University of British Columbia

    Responsibilities include:

    • Develop and deliver the new Master in Data Science program.
    • Teach content in an engaging and understandable way.
    • Manage teams of teaching assistants and student needs.

Education

  1. PhD Statistics

    The University of British Columbia
    Thesis on predicting extremes using copulas for dependence modelling. Supervised by Prof Harry Joe Smith and Prof Natalia Nolde.
    Read Thesis

  2. MSc Mathematical Statistics

    Brock University

  3. BSc Mathematics (Honours)

    Brock University

  4. BSc Biology (3-year)

    Brock University
Let’s Connect

Let’s Connect

I’m always interested in connecting with professionals working in statistics, earth sciences, and related fields. Feel free to reach out if you have questions about my approach or want to discuss potential collaborations.

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