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 am a statistician and data scientist specializing in practical and innovative probabilistic modeling. With a background spanning statistics, data science, and earth sciences, I’m dedicated to bridging the gap between theoretical statistical methods and practical solutions for complex environmental challenges.

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.

My Approach

Innovative Statistical Solutions

Leverage a robust foundation in probability theory, extreme value modeling, and machine learning to craft novel methods tailored to real-world challenges.

Comprehensive Uncertainty Analysis

Isolate and communicate multiple sources of uncertainty using a tailored mix of approaches.

Practical Data Science

Ensure that all models are grounded in reality, considering data availability, domain expertise, and end-user needs, avoiding over-engineered solutions.

Reproducible and Efficient Code

Uphold best practices in computation, using version control, defensive programming, and clear documentation to deliver projects that are both reproducible and scalable.

Dynamic Communication and Collaboration

Foster knowledge exchange with domain experts to understand the problem space deeply, ensuring that model results are interpretable, actionable, and presented clearly.

Cutting-Edge Software Development

Design intuitive R packages and Shiny applications, making complex analyses accessible and empowering teams to conduct probabilistic modeling seamlessly.

Scholarly Publications

Articles in scientific journals, proceedings, and preprints.

(2024). Copula-based conditional tail indices. Journal of Multivariate Analysis.
(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.
(2023). Non-Crossing Dual Neural Network: Joint Value at Risk and Conditional Tail Expectation Regression with Non-Crossing Conditions. Available at SSRN 4351877.
(2022). Non-Crossing Dual Neural Network: Joint Value at Risk and Conditional Tail Expectation estimations with non-crossing conditions. IREA–Working Papers, 2022, IR22/15.
(2021). Tail Behavior for Bivariate Distributions Based on Pareto Mixtures. Advances in Statistics - Theory and Applications: Honoring the Contributions of Barry C. Arnold in Statistical Science.

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.

Email Me