#142 Uncertainty Quantification

Jose Marie Antonio Miñoza (Joma) will discuss Uncertainty Quantification. Uncertainty Quantification (UQ) is an interdisciplinary field of science and engineering that focuses on identifying, quantifying, characterizing, and managing uncertainties in computational models and real-world systems. Its primary goal is to determine how reliable a model's predictions are by providing a quantitative measure of doubt, often in terms of a probability or a range of possible outcomes (like a confidence interval), instead of just a single, deterministic answer. UQ helps answer the question: "How likely are certain outcomes if some aspects of the system are not exactly known?"

Joma is currently a Senior Data Scientist at the Center for AI Research (CAIR). He has more than 8 years of experience in development of models in both industry and academia. He has worked in various domains, including geoscience, sustainability, bioinformatics, and education.

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