Ilya Verenich

Ilya Verenich is a Researcher at Queensland University of Technology (QUT), known for his work in Business Process Management and Predictive Analytics. He completed his PhD in 2018 with a thesis titled 'Explainable predictive monitoring of temporal measures of business processes.' His research focuses on developing machine learning and AI-driven tools for predictive process monitoring, such as the Apromore platform and Nirdizati web tool. Collaborations with international experts like Marlon Dumas and Marcello La Rosa highlight his contributions to process mining, workflow optimization, and predictive modeling. His work bridges computer science, data mining, and business systems, addressing challenges in real-time process analysis, resource allocation, and anomaly detection.

Key areas of expertise include temporal process analysis, LSTM neural networks for sequential data, and minimizing operational waste through predictive activity ordering. He has published extensively in top-tier conferences like CAiSE and BPM, with topics ranging from white-box process performance prediction to symbolic sequence clustering for process monitoring. His interdisciplinary approach combines theoretical frameworks with practical applications in industries requiring efficient workflow management.