Vitalie Spinu
As a machine learning scientist at Adyen, my current focus lies in the development of models and explainability tooling for monitoring of Adyen's payment processes. My primary areas of interest comprise Bayesian probabilistic modeling, machine learning explainability, and causality. In my past roles as a freelance professional, employed data scientist and university researcher, I have engaged with a wide array of applied modeling challenges, such as churn modeling, matching engines, anomaly detection, recommender systems and modeling of human behavior under risk.
Session
This talk presents a principled methodology for partitioning item-level data into homogeneous time-series, with the objective of maximizing monitoring coverage and improving the detection of anomalies and drifts. We discuss the theoretical underpinnings of clustering algorithms for this task and describe practical algorithms enabling efficient search for optimal partitioning. We exemplify our approach with a real-world application in large-scale monitoring environments from the online payment domain.