Researchers studying trust across autonomous agents, blockchains, and connected infrastructure.

Davor Svetinovic studies the security and resilience of autonomous agents, multi-agent systems, programmable blockchains, and cyber-physical infrastructure. His work uses software and requirements engineering to examine trust, failure, and control in complex digital systems.

Dr. Shraddha M. Naik develops machine-learning methods for noisy and imbalanced data, with work on probabilistic neural networks, regularized extreme learning machines, and generative models. Her blockchain research examines Ethereum and DeFi security, including how different methods detect and mitigate maximal extractable value.

Dr. Huned Materwala studies blockchain systems, applied machine learning, and optimization. His work spans MEV detection and mitigation in DeFi, blockchain-based health-data management, and energy-aware computation across edge, cloud, and connected systems.

Dr. Sajjad Khan develops secure and privacy-preserving federated and decentralized learning systems. His work combines machine learning, blockchain interoperability, and distributed computing to address adversarial participants, data-poisoning attacks, heterogeneous data, and trust in collaborative learning.

Dr. Habib Rehman develops privacy-preserving federated learning systems for medical imaging and healthcare applications. His work combines distributed machine learning with differential privacy, homomorphic encryption, and secure computation to support trustworthy collaboration across institutions.

Nikita Karetnikov designs and evaluates LLM-based agents that model human behaviour and social phenomena. His research spans computational social science, machine behaviour, and large language models, with an interdisciplinary focus on the relationship between humans and machines and the societal challenges they create.

Aisha AlQahtani is a PhD student at Khalifa University researching agentic AI pipelines for cybersecurity. Building on prior industry experience as a Cloud Security Architect in the banking sector, she studies anomaly detection and the use of AI agents to strengthen cybersecurity operations.

Syahirul Faiz is a PhD student at Khalifa University studying agentic AI for decentralized finance. His research uses deep reinforcement learning and large language model agents to optimize MEV arbitrage under blockchain constraints, with interests in multi-agent learning, adversarial and cooperative AI, and practical AI systems for decentralized markets.

Essey Amanuel is a PhD student at Khalifa University researching artificial intelligence, blockchain systems, and decentralized finance. His work investigates reinforcement learning, large language models, and multi-agent learning for decision-making in blockchain markets, with particular interests in market mechanisms, decentralized protocols, and autonomous agents in complex financial environments.

Mohamed Alremeithi is a PhD student whose research focuses on agentic AI for finance and investments.

Safa is a Master's student at Khalifa University researching agentic AI systems for decentralized finance security. Her work develops an ML-driven transaction routing system to protect Ethereum DeFi swaps from MEV sandwich attacks, activating agentic reasoning specifically in cases of ambiguous risk. Her broader interests span agent orchestration architectures, blockchain security, and applied machine learning for real-time financial decision-making.

Asma Alshebli is a Master's student at Khalifa University investigating security and privacy threats arising from the use of large language models. Her project develops a red-team testing harness for LLM-based applications and evaluates ways to mitigate the threats it uncovers. Her broader interests include the implementation and ethical implications of LLMs across different fields.