The fundamental shift of biology into a quantitative science has driven the development of intricate mechanistic models and hybrid modeling approaches. However, a major hurdle remains: parameter identifiability-the ability to uniquely determine model parameters from experimental data. When parameters are non-identifiable, the mechanistic insights gained from the model become ambiguous, and predictions can be overconfident. This challenge is amplified in modern stochastic, hybrid, and multiscale biological models where existing deterministic computational tools fall short. This workshop addresses this critical barrier by uniting mathematicians, statisticians, and biologists to establish a unified workflow for model validation. Key focus areas include re-examining identifiability as a foundational constraint on inference, developing new methodologies to assess identifiability in stochastic and multiscale settings, and creating algorithmic tools for “identifiability-aware” experimental design. By fostering this dialogue and developing robust computational tools, the workshop aims to set new standards for uncertainty quantification, ultimately improving the credibility of biological models in vital areas such as precision medicine and synthetic biology.