Research Summary
(1) online social networks and distributed digital ecosystems, where adversaries exploit identities, human behavior, information structures, and algorithmic weaknesses to enable abuse, deception, manipulation, and privacy attacks; and (2) cyber-physical and critical infrastructures, including nuclear instrumentation and control (I&C), industrial IoT, and operational technology (OT) systems, where cyber compromise can lead to real-world physical and safety consequences.
I develop AI systems that remain secure, private, reliable, and robust under adaptive, coordinated, and AI-enabled attacks. My work integrates machine learning, adversarial modeling, privacy-enhancing technologies, graph intelligence, federated and distributed learning, behavioral analytics, and human-centered security. A central theme of my research is identifying learnable behavioral and system-level signatures of adversarial activity—ranging from temporal deception patterns, identity manipulation, and linguistic drift in online ecosystems to anomalous behavior in cyber-physical and OT environments. These signals enable AI systems that can anticipate, detect, and mitigate emerging threats rather than merely react after compromise. My research also investigates trustworthy AI for safety-critical environments, including AI-driven cybersecurity, intelligent monitoring, and resilient decision support for nuclear and other critical infrastructure systems.
My research vision is guided by a central question:How can we design AI systems that remain secure, private, trustworthy, and resilient when data, users, models, and system components are continuously evolving, potentially adversarial, and cannot be fully trusted?
I direct the Security and Privacy Enhanced Machine Learning (SUPREME) Lab at UTEP, where my group develops trustworthy and privacy-preserving AI systems that integrate adversarial learning, behavioral modeling, distributed intelligence, and human-centered defense for online and cyber-physical environments.
Research Interests
- Trustworthy & Adversarial AI
- Security & Privacy
- Online Abuse & Deception
- Online Social Networks
- Adversarial Machine Learning
- Federated & Distributed Learning
- Human-Centered AI Security
- Cyber-Physical Systems & Critical Infrastructure
- AI & Cybersecurity for Nuclear Systems
