Chris is a machine learning research and engineering leader at Intuition Machines, where he builds bot and fraud mitigation solutions.Previously, he worked on APTA Technologies, a TU Delft spin-off he cofounded, where he and his team made real impact with machine-learning for log-data and cyber-data. APTA Technologies extracted insightful and actionable visual descriptions from log data for fast root-cause analysis and deep insights for non-technical stakeholders. Prior to that, he was a postdoc researcher at the Cyber Security group at TU Delft, working on state machine learning algorithms to problems in both software engineering and computer networks.He stayed in close collaboration with the SEDAN-Lab group of the Interdisciplinary Centre for Security, Reliability and Trust at the University of Luxembourg, where he previously spent 4 years during his Ph.D. and postdoc. His research focuses on machine learning applications for security problems with discrete and tabular data in scenarios with little or no reliable labels. Examples are telecommunication fraud and financial transaction data. He is using methods from deep learning and probabilistic programming, andhas worked on problems in protocol and program synthesis as a form of representation learning to tackle application-driven problems in computer networking.