A complete and up-to-date list is also available on my Google Scholar profile.
A comprehensive review of agricultural spraying drones, covering drone architectures, control mechanisms, droplet deposition, and path planning techniques.
A. Talaeizadeh, I. Sharifi, Sh. G. Samani, and A. Alasty. (2025). "Agricultural Spraying Drones: A Comprehensive Review." Smart Agricultural Technology, 2025.
A neuro-symbolic deep reinforcement learning framework integrating symbolic reasoning and neural control to ensure safety and interpretability in autonomous driving systems.
I. Sharifi, M. Yildirim, and S. Fallah. (2025). "Towards Safe Autonomous Driving Policies using a Neuro-Symbolic Deep Reinforcement Learning Approach." Transportation Research Record, 2026.
Introduces a symbolic imitation learning approach using inductive logic programming to extract explainable behavioral rules from human demonstrations in highway driving.
I. Sharifi, M. Yildirim, and S. Fallah. (2025). "Symbolic Imitation Learning: From Black-Box to Explainable Driving Policies." Applied Sciences.
A neuro-symbolic differentiable Inductive Logic Programming framework that learns first-order logic rules over a continuous rule space using attention-based conjunction/disjunction operators, enabling accurate, stable, and interpretable rule extraction from noisy, probabilistic data.
I. Sharifi, P. Wei, and S. Fallah. (2026). "ANDRE: An Attention-based Neuro-symbolic Differentiable Rule Extractor for Inductive Logic Programming." International Conference on Neurosymbolic Learning and Reasoning (NeSy), 2026.
Explores supervised fine-tuning and Group Relative Policy Optimization (GRPO) to adapt large language models for centralized conflict resolution and safe separation assurance in cooperative multi-agent sUAS operations.
I. Sharifi, A. Zongo, and P. Wei. (2026). "Fine-Tuning Large Language Models for Cooperative Tactical Deconfliction of Small Unmanned Aerial Systems." CVPR Workshops, 2026.
A decentralized-centralized multi-agent reinforcement learning framework for ensuring separation assurance and fairness among heterogeneous sUAS fleets in shared airspace.
I. Sharifi, H. T. Kim, M. H. Ahmed, M. Ghasemi, and P. Wei. (2026). "Separation Assurance between Heterogeneous Fleets of Small Unmanned Aerial Systems via Multi-Agent Reinforcement Learning." IEEE ITSC, 2026.
Comprehensive survey of cybersecurity challenges and solutions in UAS Traffic Management and small Unmanned Aerial Systems, with emphasis on communication, authentication, and operational vulnerabilities.
I. Sharifi, M. Ghazanfari, A. Taye, P. Wei, G. Biswas, B. Ward, X. Koutsoukos, M. Ghasemi, V. Gupta, A. Chen, A. Shirkhodaei, S. Hasan, I. Amundson, F. Fotiadis, U. Topcu, et al. (2026). "A Survey of Security Challenges and Solutions for UAS Traffic Management (UTM) and small Unmanned Aerial Systems (sUAS)." AIAA SciTech Forum, 2026.
Survey identifying and categorizing security vulnerabilities and mitigation strategies in Advanced Air Mobility (AAM) and eVTOL ecosystems.
M. Ghazanfari, I. Sharifi, A. Taye, P. Wei, G. Biswas, B. Ward, X. Koutsoukos, M. Ghasemi, V. Gupta, A. Chen, A. Shirkhodaei, S. Hasan, I. Amundson, F. Fotiadis, U. Topcu, et al. (2026). "A Survey of Security Challenges and Solutions for Advanced Air Mobility and eVTOL Aircraft." AIAA SciTech Forum, 2026.
A comprehensive survey of neurosymbolic artificial intelligence methods and their integration into Advanced Air Mobility (AAM) applications.
K. Acharya, I. Sharifi, M. Lad, L. Sun, and H. H. Song. (2025). "Integrating Neurosymbolic AI in Advanced Air Mobility: A Comprehensive Survey." IJCAI, 2025.
Introduces a hybrid actor-critic neural structure for self-tuning PID control of quadcopters, improving dynamic response and robustness.
I. Sharifi and A. Alasty. (2022). "Self-Tuning PID Control via a Hybrid Actor-Critic-Based Neural Structure for Quadcopter Control." ISME Conference, 2022.
A neuro-symbolic pipeline that fuses semantic, spatial, and shape features with a Soft First-Order Logic reward — extracting symbolic predicates from segmentation maps — to improve the robustness and safety of camera-based deep reinforcement learning for autonomous emergency braking, evaluated in CARLA.
V. Asodia, I. Sharifi, and S. Fallah. (2026). "Reinforcement Learning Enhancement Using Vector Semantic Representation and Symbolic Reasoning for Human-Centered Autonomous Emergency Braking." arXiv preprint arXiv:2602.05079.