Research
Research Overview
My research lies at the intersection of generative AI, network security, wireless systems, game theory, blockchain, and computational biology.
I develop intelligent, secure, and adaptive systems that integrate data-driven learning with theoretical foundations, with a particular focus on adversarial environments, multimodal learning, and strategic interaction.
My work bridges foundational modeling and real-world applications, aiming to advance trustworthy and robust AI systems across cyber, physical, and biological domains.
Research Themes
Generative AI for Wireless and Security
I study how generative models can enhance the design, robustness, and evaluation of wireless and security systems. This includes synthesizing realistic data, modeling multimodal environments, and enabling learning in adversarial settings.
Representative directions include:
- multimodal generative models for intrusion detection
- synthetic RF signal and coverage-map generation
- rare-event and attack-data augmentation
- trustworthy multimodal representation learning
Game-Theoretic Modeling in AI Systems
A central theme of my research is the use of game theory to model strategic interactions in intelligent systems. I study how incentives, equilibrium behavior, and adversarial adaptation shape modern AI systems.
Representative directions include:
- strategic learning in adversarial environments
- game-theoretic analysis of generative models
- signaling, evolutionary, and Stackelberg games
- incentive design for coordination and security
Network Security, Privacy, and IoT
I work on security and privacy in communication networks and cyber-physical systems, combining formal reasoning with data-driven approaches to improve resilience against attacks.
Representative directions include:
- intrusion detection and attack synthesis
- privacy-preserving and federated learning systems
- deception and adversarial defense mechanisms
- security of resource-constrained and IoT systems
Wireless Networking and Communication Systems
My research in wireless systems focuses on intelligent resource allocation, spectrum sharing, and cooperative communication in modern networks.
Representative directions include:
- spectrum sharing and cognitive radio
- resource allocation and mechanism design
- cooperative and device-to-device communication
- performance optimization under uncertainty
Blockchain and Decentralized Systems
I study the security and incentive structures of decentralized systems, focusing on how strategic behavior impacts efficiency, robustness, and fairness.
Representative directions include:
- shard-based blockchain security
- transaction fees and incentive compatibility
- decentralized coordination and committee formation
- adversarial behavior in consensus protocols
Computational Biology and AI for Science
I explore how AI and generative modeling can help understand complex biological systems, particularly in cancer and cellular processes.
Representative directions include:
- cancer progression and therapy modeling
- tumor microenvironment and evolutionary dynamics
- gene expression and phenotype modeling
- AI-driven biological interaction modeling
Current and Recent Projects
Some recent projects include:
- MAGE-ID: a multimodal generative framework for intrusion detection systems
- generative AI methods for RF signal and coverage-map synthesis
- topology-aware generative models for financial time-series forecasting
- game-theoretic modeling of generative adversarial systems
- incentive-aware security analysis of blockchain protocols
Collaboration
I welcome collaborations with students and researchers interested in generative AI, security, wireless systems, game theory, blockchain, and computational biology.