Dr. Pablo Moriano is a staff scientist in the Computer Science and Mathematics Division at Oak Ridge National Laboratory. He received Ph.D. and M.S. degrees in Informatics from Indiana University Bloomington. Previously, he received M.S. and B.S. degrees in Electrical Engineering from Pontificia Universidad Javeriana in Colombia. His research bridges artificial intelligence, machine learning, and network science to enhance the security and reliability of large-scale interconnected and cyber-physical systems.
At ORNL, Dr. Moriano leads efforts on adversarial anomaly detection, intelligent automation, and decision-support systems for critical scientific infrastructure. He serves as Principal Investigator of a multi-year, $1.5 million Laboratory Directed Research and Development project on next-generation security for interconnected systems and as Co-Principal Investigator of a $14 million DOE Mathematical Multifaceted Integrated Capability Center (MMICC) focused on sparse computations in science and engineering.
His research develops data-driven and computational frameworks that uncover hidden patterns in dynamic networks, with applications that span automotive cybersecurity, high-performance computing telemetry, and software reliability. These methods combine statistical modeling, graph representation learning, and uncertainty quantification to detect emerging threats and improve system resilience.
Dr. Moriano has authored more than 40 peer-reviewed publications in journals such as IEEE Transactions on Information Forensics and Security, IEEE Internet of Things Journal, Physical Review E, Scientific Reports, PLOS ONE, and Computers & Security. He holds several U.S. patents related to cybersecurity analytics and signal-based intrusion detection and has received Best Paper Awards at leading security workshops. His work has also been recognized with ORNL’s Innovation Awards and the MGB–SIAM Early Career Fellowship.
Beyond his research, Dr. Moriano mentors undergraduate, graduate, and postdoctoral researchers through programs that connect data science, applied mathematics, and cybersecurity. He is committed to building interdisciplinary collaborations across national laboratories, universities, and industry to advance trustworthy AI and predictive maintenance for critical systems. He has served as a reviewer for more than 30 leading journals in data science, machine learning, and cybersecurity, providing expert evaluations for over 50 manuscripts.
He is a senior member of IEEE and ACM and a member of SIAM.
Research Interests
- Artificial Intelligence and Machine Learning
- Anomaly Detection and Adversarial Learning
- Network and Data Science
- Cybersecurity for Cyber-Physical Systems
- Predictive Maintenance and Digital Twins
Publications and Talks
- See attached CV and Google Scholar
