2020
Lyes Saad Saoud, PhD
AI and Robot Vision
Dr. Lyes Saad Saoud is a distinguished AI and robotics researcher and currently serves as a Research Scientist at Khalifa University, United Arab Emirates. He holds a Ph.D. in Physics with a specialization in renewable energies (2015) and a D.Sc. (Empowerment to Direct Research, 2017) from the University of Boumerdès, Algeria. His credentials have been verified with U.S. equivalency, reinforcing his global academic standing.
Dr. Saad Saoud is internationally recognized for pioneering HuBot, the first biomimetic robot for non-invasive Houbara bird behavior monitoring. His work bridges semantic SLAM, multi-modal AI integration, and large language models (LLMs) to develop adaptive perception systems for field-deployable autonomous platforms. His research spans both foundational methods and real-world deployments, targeting applications in conservation robotics, underwater mapping, and bio-inspired design.
Previously, he served as an Associate Professor and Head of the Department of Electrical Systems Engineering at the University of Boumerdès, where he contributed to curriculum reform and led several research initiatives focused on smart energy systems and intelligent control.
Research Interests
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AI and Robotics: Vision-language-action models, transformer-based architectures, semantic SLAM, multi-sensor fusion, real-time autonomous systems, and behavioral robotics.
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Environmental Monitoring: Conservation technologies, underwater robotics, bioacoustics, and AI-driven species recognition.
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Renewable Energy and Sustainability: Forecasting, hybrid renewable systems, smart grid integration, and control optimization.
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Neural Computation: Metacognitive and hypercomplex neural networks, including patent contributions to sedenion-valued architectures.
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Cyber-Physical Security: Embedded systems, IoT security, and edge intelligence for autonomous platforms.
Selected Recognitions & Contributions
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Lead inventor of the HuBot platform for ecological robotics and AI-driven behavioral monitoring.
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Reviewer for premier journals including:
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IEEE Transactions on Neural Networks and Learning Systems
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IEEE Access
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Journal of Renewable and Sustainable Energy (AIP)
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Neurocomputing (Elsevier)
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Transactions of the Institute of Measurement and Control (SAGE)
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Neural Processing Letters (Springer)
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Contributor and Technical Committee Member in several top-tier AI and robotics conferences.
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Author of 40+ peer-reviewed publications covering machine learning, robotics, energy forecasting, and underwater imaging.
Academic Mentorship
Dr. Saad Saoud actively supervises and mentors Ph.D. and M.Sc. students in:
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Autonomous systems for aquaculture and coral reef monitoring
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Underwater swarm robotics and sensor integration
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Bio-inspired locomotion and real-time behavior-driven robot control
Dr. Saad Saoud’s mission is to push the frontier of intelligent autonomous systems for ecological, societal, and industrial impact—combining algorithmic rigor with a strong commitment to sustainability and innovation.
Research Interests
As a Research Scientist in Artificial Intelligence, my primary work focuses on developing advanced machine learning algorithms for time series forecasting and energy supply and demand prediction, with the goal of improving the integration of renewable energy sources into modern power grids. This core research supports sustainability and energy efficiency while advancing AI theory and applications.
In addition, I apply AI-driven methods in robotics, healthcare, and conservation, demonstrating the broad impact of my expertise across domains. My contributions include:
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Wearable Device–Based AI: Developing advanced models for knee joint angle prediction and real-time health monitoring using sensor fusion and deep learning architectures.
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Autonomous Underwater Robotics: Implementing AI-driven perception and adaptive mapping systems for underwater exploration, aquaculture monitoring, and marine conservation.
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Bio-Inspired Robotics: Leading the development of HuBot, the first biomimetic robot for non-disruptive Houbara bird behavior studies, leveraging semantic AI models and multi-modal perception frameworks to support wildlife conservation.
I am passionate about advancing AI research that bridges fundamental algorithmic innovation with real-world applications, with a focus on:
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Energy and Sustainability: Developing forecasting algorithms to optimize renewable energy integration.
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Digital Health: Building AI-powered wearable systems for healthcare diagnostics and rehabilitation.
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Ecological Monitoring: Designing autonomous robots for wildlife observation and conservation.
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Vision-Based Perception: Advancing robotic vision and SLAM systems for autonomous exploration in complex environments.
With a proven track record in applied research, project leadership, and cross-disciplinary collaboration, I remain committed to advancing AI-driven solutions that address challenges at the intersection of technology, sustainability, and healthcare innovation, while fulfilling my proposed endeavor of strengthening renewable energy forecasting and grid integration.
Professional Experience
Senior Consultant: Predictions, Machine Learning, Data Science, Cybersecurity
IOTwitt incorporation, Hawaii, USA 2018-2019
Senior Consultant, Data science, cybersecurity
IOTwitt incorporation, Hawaii, USA 2017-2018
Research Scientist - Mechanical Engineering 2024-now
Post-Doctoral fellow - Mechanical Engineering 2022-2024
Post-Doctoral fellow - Electrical Engineering and Computer Science 2019-2022
Head of Department - Electrical Engineering 2018-2019
University of Boumerdes, Algeria (Full time)
Associate Professor - Electrical Engineering 2015-2019
University of Boumerdes, Algeria (Full time)
Visiting Post-Doctoral fellow – REDLab 2015-2019
University of Hawai’i at Manoa, USA (Part time)
Assistant Professor - Physics 2009-2015
University of Boumerdes, Algeria (Full time)
Publications
Selected Publications (2023–2025)
For a complete list of publications, please visit my
I am particularly interested in applied research that addresses the challenge of implementing high-performance algorithms on resource-constrained devices, enabling the transfer of advanced computations from supercomputers to edge systems. My current work focuses on integrating artificial intelligence with robotics, with a special emphasis on underwater environments through AI-driven perception models, adaptive mapping techniques, and multi-modal sensor fusion. This research is complemented by efforts in digital health and bio-inspired systems, where I explore AI-powered wearables, ecological robotics, and autonomous platforms that contribute to healthcare, sustainability, and conservation.
Recent Publications (2025):
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Lyes Saad Saoud, Zikai Jia, Siyuan Yang, et al. Prioritized Real-Time UAV-based Vessel Detection for Efficient Maritime Search. Journal of Field Robotics, 2025.
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L. Saad Saoud Loïc Lesobre, Enrico Sorato, Yves Hingrat, Lakmal Seneviratne and Irfan Hussain, Real-Time Houbara Detection and Segmentation for Animal Behavior Analysis Using Mobile Platforms, Arxiv, 2025.
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M. Abujabal, L. Saad Saoud, I. Hussain. FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains
arXiv preprint arXiv:2507.17859, 2025. -
L. Saad Saoud, I. Hussain. EBA-AI: Ethics-guided Bias-aware AI for Efficient Underwater Image Enhancement and Coral Reef Monitoring
arXiv preprint arXiv:2507.15036, 2025. -
W. Akram, M. U. Din, L. Saad Saoud, I. Hussain. A Review of Generative AI in Aquaculture: Foundations, Applications, and Future Directions for Smart and Sustainable Farming. arXiv preprint arXiv:2507.11974, 2025.
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M. U. Din, W. Akram, L. Saad Saoud, J. Rosell, I. Hussain. Vision-Language-Action Models in Robotic Manipulation: A Systematic Review, arXiv preprint arXiv:2507.10672, 2025.
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L. Saad Saoud, I. Hussain. Kinematic Integration Network with Enhanced Temporal Intelligence and Quality-driven Attention for Precise Joint Angle Prediction in Exoskeleton-Based Gait Analysis (KINETIQA), IEEE Access, 2025.
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M. Heshmat, L. Saad Saoud, M. Abujabal, A. Sultan, M. Elmezain, Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions, Sensors 25 (11), 3258, 2025.
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HuBot: A Biomimicking Mobile Robot for Non-Disruptive Bird Behavior Study
L. Saad Saoud, L. Lesobre, E. Sorato, S. Al Qaydi, Y. Hingrat, L. Seneviratne, I. Hussain. Ecological Informatics 85, 102939, 2025. -
M. Elmezain, L. Saad Saoud, A. Sultan, M. Heshmat, L. Seneviratne, I. Hussain. Advancing Underwater Vision: A Survey of Deep Learning Models for Underwater Object Recognition and Tracking, IEEE Access, 2025.
Recent Publications (2023-2024):
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L. Saad Saoud, A. Sultan, M. Elmezain, M. Heshmat, L. Seneviratne and I. Hussain, " Beyond observation: Deep learning for animal behavior and ecological conservation, Ecological Informatics, Volume 84, 2024, 102893.
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L. Saad Saoud, (2024), “HuBot Dataset: Annotated Data for Non-Disruptive Bird Behavior Study”, Mendeley Data, V1, doi: 10.17632/tx3vrvsrgv.1.
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L. Saad Saoud, H. AlMarzouqi, Explainable early detection of Alzheimer’s disease using ROIs and an ensemble of 138 3D vision transformers. Scientific Reports, Nature, 14, 27756 (2024).
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L. Saad Saoud, M. Elmezain, A. Sultan, M. Heshmat, L. Seneviratne and I. Hussain, "Seeing Through the Haze: A Comprehensive Review of Underwater Image Enhancement Techniques," in IEEE Access, doi: 10.1109/ACCESS.2024.3465550.
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L. Saad Saoud, Z. Niu, L. Seneviratne and I. Hussain, "Real-Time and Resource-Efficient Multi-Scale Adaptive Robotics Vision for Underwater Object Detection and Domain Generalization," 2024 IEEE International Conference on Image Processing (ICIP), Abu Dhabi, United Arab Emirates, 2024, pp. 3917-3923.
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L. Saad Saoud, Loïc Lesobre, Enrico Sorato, Saud Al Qaydi, Yves Hingrat, Lakmal Seneviratne and Irfan Hussain, HuBot: A Bio-Inspired Robot for Non-Disruptive Bird Behavior Research and Conservation, Bio-inspired, Biomimetics, and Biohybrid (Cyborg) Systems Workshop, IEEE/RSJ IROS, Abu Dhabi, 16-18 October 2024
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L. Saad Saoud Loïc Lesobre, Enrico Sorato, Yves Hingrat, Lakmal Seneviratne and Irfan Hussain , Real-Time Houbara Detection and Segmentation for Animal Behavior Analysis Using Mobile Platforms, Conference on Computer Vision and Pattern Recognition, CVPR CV4Animal workshop, June 17- 21, 2024 in Seattle, Washington, United States.
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Lyes Saad Saoud, Irfan Hussain. Kinematic Integration Network with Enhanced Temporal Intelligence and Quality-driven Attention for Precise Joint Angle Prediction in Exoskeleton-based Gait Analysis. TechRxiv. January 18, 2024.
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Ahsan B. Bakht, Zikai Jia, Muhayy Ud Din, Waseem Akram, Lyes Saad Saoud, Lakmal Seneviratne, Defu Lin, Shaoming He, Irfan Hussain, MuLA-GAN: Multi-Level Attention GAN for Enhanced Underwater Visibility, Ecological Informatics, Volume 81, 2024, 102631
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L. Saad Saoud and I. Hussain, "TempoNet: Empowering Long-Term Knee Joint Angle Prediction with Dynamic Temporal Attention in Exoskeleton Control," 2023 IEEE-RAS 22nd International Conference on Humanoid Robots (Humanoids), Austin, TX, USA, 2023, pp. 1-8, doi: 10.1109/Humanoids 57100. 2023. 10375196.
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Lyes Saad Saoud, Zhenwei Niu, Atif Sultan, Lakmal Seneviratne and Irfan Hussain, "ADOD: Adaptive Domain-Aware Object Detection with Residual Attention for Underwater Environments," 2023 21st International Conference on Advanced Robotics (ICAR), Abu Dhabi, United Arab Emirates, 2023, pp. 633-638
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Muhayyuddin Ahmed, Ahmed Humais, Waseem Akram, Mohamed Alblooshi, Lyes Saad Saoud, Abdelrahman Alblooshi, Lakmal Seneviratne, and Irfan Hussain, Marine X: Design and Implementation of Unmanned Surface Vessel for Vision Guided Navigation," 2023 21st International Conference on Advanced Robotics (ICAR), Abu Dhabi, United Arab Emirates, 2023, pp. 226-231, doi: 10.1109/ICAR58858.2023.10406475.
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Zhenwei Niu, Lyes Saad Saoud, Irfan Hussain et al. MAD-CNN: High-Sensitivity and Robust Collision Detection for Robots with Variable Stiffness Actuation, 11 February 2024, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-3914271/v1]
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Lyes Saad Saoud, Humaid Ibrahim, Ahmad Aljarah, and Irfan Hussain, "Improving Knee Joint Angle Prediction through Dynamic Contextual Focus and Gated Linear Units," Pattern Recognition Letters, 2024, arXiv:2306.06900, under review.
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Waseem Akram, Muhayyuddin Ahmed, Lyes Saad Saoud, Lakmal Seneviratne, Irfan Hussain, Autonomous Underwater Robotic System for Aquaculture Applications, 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023) , October 1 – 5, 2023 , Detroit, Michigan, USA., 2023, arXiv:2308.14762, 2023
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Hasan AlMarzouqi, Lyes Saad Saoud, Semantic Labeling of High-Resolution Images Using EfficientUNets and Transformers, IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-13, 2023.
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Patents
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Hasan AlMarzouqi, Lyes Saad Saoud, Metacognitive Sedenion-Valued Neural Networks, US Patent Application No. 17/581,767, 2022.
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