We focus on optimizing post-quantum algorithms for constrained environments, including low-power IoT devices and embedded systems where computational and memory resources are limited.

We design and evaluate end-to-end secure communication protocols, integrating lattice-based, code-based, and multivariate cryptographic schemes tailored for future networks, including 5G/6G and satellite communications.

Our team contributes to PQC standardization efforts and investigates practical challenges such as key management, certificate infrastructure adaptation, and hybrid cryptographic systems combining classical and quantum-safe mechanisms.

Develop and deploy machine learning algorithms to accelerate scientific simulations, optimize complex system designs, and enable predictive modeling in physics, engineering, and environmental science.

Design AI models for perception, reasoning, planning, and control, empowering autonomous systems — from aerial drones to ground robots — to operate effectively in complex, uncertain, and adversarial environments.

Apply deep learning, time-series analysis, and anomaly detection to analyze environmental sensor data, predict pollution trends, and support smart urban management initiatives.

Automate the collection, processing, and analysis of cyber threat intelligence; deploy AI agents capable of autonomously predicting, preventing, and responding to sophisticated attacks across enterprise and national cyber infrastructures.

Engineer AI architectures that prioritize robustness, interpretability, fairness, and accountability, ensuring that AI systems deployed in critical sectors operate transparently and resist both accidental failure and malicious exploitation.

Develop lightweight AI models optimized for edge devices, enabling real-time local decision-making, anomaly detection, and energy-efficient operation across massive, distributed IoT deployments.

Investigate methods to enhance human decision-making through AI-assisted reasoning, explanation generation, and intelligent recommendation systems, especially in complex cybersecurity and system operations environments.

Design specialized hardware accelerators that efficiently handle the computational demands of reasoning algorithms.

Develop low-power, high-performance hardware solutions enabling reasoning AI applications on edge devices for real-time decision-making.

Build scalable and energy-efficient infrastructure to support the training and deployment of large-scale reasoning AI models.

Develop machine learning models capable of identifying novel attacks, including zero-day exploits, ransomware activities, and insider threats, across cloud, edge, and enterprise environments.

Investigate vulnerabilities of AI systems against adversarial examples and poisoning attacks; propose defensive techniques to ensure reliability under adversarial conditions.

Focus on making cybersecurity-focused AI systems interpretable and accountable, ensuring security analysts can trust and validate AI-driven decisions in critical threat response scenarios.

Design AI agents capable of autonomously analyzing security incidents and recommending or executing containment, eradication, and recovery procedures.

Systematically map and categorize potential cyber threats unique to robotic systems, from sensor spoofing to control system hijacking.

Research techniques to safeguard critical robotic modules, ensuring that data fusion, navigation planning, and actuation pipelines are resilient to malicious interference.

Design lightweight, real-time cryptographic solutions for secure inter-robot communication, ensuring integrity and confidentiality in collaborative missions.

Develop fail-safe behaviors and recovery strategies that allow robots to maintain operational capabilities even under cyber attack.

Architect dynamic trust management systems, secure boot processes, firmware integrity verification, and distributed ledger technologies for device authentication and lifecycle management.

Integrate lightweight AI models on IoT edge devices to enable real-time anomaly detection and predictive threat identification without reliance on centralized cloud services.

Develop federated learning and encrypted aggregation protocols to protect sensitive data in distributed IoT ecosystems, while still enabling effective model training and inference.

Adapt and deploy quantum-resistant cryptographic primitives for ensuring confidentiality, integrity, and availability across constrained networks.

Engineer multi-sensor nodes capable of measuring PM2.5, PM10, NO2, SO2, O3, CO, and volatile organic compounds, optimized for power efficiency and long-term deployment.

Implement flexible architectures that combine static stations with mobile sensors deployed on vehicles, drones, and public transportation systems, enabling dynamic, high-resolution environmental mapping.

Use deep learning and statistical models to predict air quality trends, identify pollution hotspots, and provide actionable insights for policymakers and urban planners.

Ensure the integrity and authenticity of environmental data streams using cryptographic techniques and blockchain-based audit trails to prevent falsification or unauthorized tampering.

Explore LoRaWAN, NB-IoT, and emerging ultra-low-power communication protocols to connect widespread sensor deployments securely and efficiently.

Develop secure frameworks for smart grids, transportation networks, healthcare systems, and public services, ensuring continuous and safe operation even under cyber-physical attacks.

Apply machine learning and edge AI to optimize traffic flow, resource management, public services, and environmental monitoring, enabling real-time, adaptive city operations while safeguarding privacy.

Engineer end-to-end protection for connected factories, logistics chains, and industrial control systems, embedding PQC and AI-based anomaly detection into critical automation layers.

Create secure digital twins for real-time simulation, monitoring, and predictive maintenance of urban and industrial assets, enhancing operational efficiency while minimizing vulnerabilities.

Design privacy-preserving data exchange protocols and trusted identity frameworks to maintain citizen trust, corporate confidentiality, and regulatory compliance in smart environments.