Serg Masis has been building software and working with data for almost two decades. After a long career in web and app development and entrepreneurship, he realized his passion lay in turning large amounts of information into knowledge: identifying patterns, uncovering relationships, solving problems, and formulating predictions. To deepen that craft, he went back to school for a graduate degree in data science.

Today Serg combines extensive experience across computer science, data science, decision science, and business thinking to bridge the gap between data and decisions. His particular focus is interpretability: helping teams and stakeholders understand why models make the predictions they do, so that machine learning can be trusted in high-stakes settings.

He is the author of Interpretable Machine Learning with Python, a hands-on book that shows practitioners how to build, inspect, and explain machine learning models. Through his writing and his talks, Serg advocates for models that are not only accurate, but understandable and fair.