Yuanyin Zhang | Energy | Pioneer in Scientific Advancement Award

Dr. Yuanyin Zhang | Energy | Pioneer in Scientific Advancement Award 

Professor level Senior Engineer at Peking University | China 

Dr. Yuanyin Zhang is a geophysicist recognized for his extensive contributions to seismic inversion, reservoir characterization, and emerging advances in natural hydrogen exploration, with a scholarly record that includes 492 citations by 410 documents, 61 publications, and an h-index of 11, reflecting his growing impact on the global geoscience community. He earned his bachelor’s, master’s, and doctoral degrees in geophysical exploration and geological engineering from leading Chinese institutions, building a strong foundation in data processing, inversion theory, and reservoir prediction. Over the course of his career, Dr. Yuanyin Zhang has served in research and senior engineering roles across major academic, industrial, and national geological survey organizations, consistently contributing to high-level scientific programs in deep hydrocarbon exploration, shale reservoir assessment, and complex carbonate system analysis. His work includes leadership in inversion algorithm development, multi-parameter seismic modeling, and integrated geophysical methodologies that have supported significant breakthroughs in the Tarim, Sichuan, Junggar, and Qaidam Basins. In recent years, he has advanced into the frontier field of natural hydrogen, leading multiple national and industry-supported projects focused on understanding its genesis, enrichment, and exploration models, thereby contributing to future-oriented energy research. His research interests encompass seismic inversion techniques, non-linear algorithm development, fracture and pore-pressure prediction, reservoir heterogeneity analysis, deep subsurface geological processes, and the emerging science of natural hydrogen accumulation. Dr. Yuanyin Zhang has authored influential monographs, delivered peer-reviewed publications across international journals, and contributed to numerous technical patents that bridge scientific innovation with real-world geological applications. His cross-disciplinary approach integrates geophysics, rock physics, sedimentology, and structural geology, enabling comprehensive solutions for complex exploration challenges. Through continued leadership in both theoretical and applied research, Dr. Yuanyin Zhang remains committed to advancing high-resolution subsurface imaging, improving geophysical predictive capability, and supporting sustainable energy development. His body of work underscores a sustained dedication to scientific excellence and positions him as an influential contributor to the evolution of modern geoscience and energy exploration.

Profile: Scopus | Researchgate

Featured Publications

  • Zhang, Y. (2025). Multi-field coupling controls the formation and evolution of deep reservoirs. Advances in Geo-Energy Research.

  • Zhang, Y. (2025). Structural sedimentary characteristics and oil & gas exploration potentials of Paleogene System in Sanmenxia Basin of Henan Province. Geological Survey of China.

  • Zhang, Y. (2024). Fault structural characteristics of the Early Paleozoic in the Keping Fault-Uplift, Tarim Basin: A case study in the southern Keping area. Geoscience.

  • Zhang, Y. (2024). Oil and gas exploration breakthroughs and insights in the western segment of the Silurian Shajingzi structural belt, Tarim Basin. Geological Survey of China.

  • Zhang, Y. (2024). Characteristics of source rocks in the Cambrian Xiaoerbulake Formation in the northwestern Tarim Basin. Oil and Gas Geology.

  • Zhang, Y. (2024). Geochemical characteristics of the Paleozoic marine source rocks and ultra-deep hydrocarbon accumulation mode of the Awati Sag. Energies.

  • Zhang, Y. (2025). Oil-gas conditions of the Proterozoic metamorphic basement reservoirs in the Wensu Salient, Tarim Basin, China. Petroleum Science.

  • Zhang, Y. (2020). Pre-stack hybrid nonlinear inversion for P-wave data. EAGE Expanded Abstracts.

  • Zhang, Y. (2018). Pre-stack seismic density inversion in marine shale reservoirs in the southern Jiaoshiba area, Sichuan Basin, China. Petroleum Science.

  • Zhang, Y. (2016). The comparison between full-stack data and pure P-wave data on deeply buried Ordovician paleokarst reservoir prediction. Earth Science Research.

Nordine Quadar | Energy | Best Researcher Award

Mr. Nordine Quadar | Energy | Best Researcher Award

Researcher at Royal Military College of Canada, Canada.

🌍Nordine Quadar is an accomplished researcher and technical leader specializing in electrical and computer engineering, cybersecurity, machine learning, and wireless communications. His expertise spans developing optimized algorithms for hardware-restricted devices, advancing AI capabilities in natural language processing, and enhancing cybersecurity for UAVs and IoT systems. He has a proven ability to balance high-quality research with leadership roles, including project and team management. His contributions are well-documented through an extensive list of publications in high-impact journals and conferences, emphasizing practical applications of machine learning and cybersecurity.

Profile👤

Education 🎓

🎓PhD in Computer Science (2022–2025) – Royal Military College of Canada, focusing on enhancing UAV cybersecurity with edge AI. MASc in Electrical & Computer Engineering (2015–2018) – University of Ottawa, with a thesis on spatial modulation in MIMO-CDMA systems. BASc in Electrical Engineering (2011–2014) – University of Ottawa, specializing in advanced electronic systems and coding.🧬🎓

Experience💼

🩺Cybersecurity Researcher (2023–Present) – Mitacs/Thales: Developed RF fingerprinting techniques for UAV security. Regional Technical Manager (2017–Present) – DC Group: Led teams in high-stakes electrical projects, achieving exceptional client satisfaction. Research Assistant (2022–2023) – Royal Military College of Canada: Advanced automotive network security. Data Scientist Intern (2022–2023) – Xlscout: Enhanced algorithms using NLP for semantic search. 👨‍🔬🌍

Research Interests 🔬

🔬Specializes in Cybersecurity for IoT, UAVs, and AI agents, emphasizing machine learning, NLP, and physical layer security. His work integrates RF Signal Analysis and advanced algorithms to optimize security and communication systems.​ 🌿🧪

Awards and Honors 🏆

🏆Mitacs Accelerate Grant (60,000 CAD) – 2024/2025. Mitacs Accelerate Grant (30,000 CAD) – 2023. DC Group Employee of the Month – 2020. University of Ottawa Excellence Scholarship – 2015-2017​​ 🏆🎉

Conclusion 🔚 

Nordine Quadar is a highly suitable candidate for the Best Researcher Award. His multidisciplinary expertise, combined with his leadership skills and impactful research contributions, make him a strong contender. By continuing to seek recognition and expanding his research collaborations, Nordine can further solidify his status as a leading researcher in his field.

Publications Top Notes 📚

Smart Water Distribution System Based on IoT Networks, a Critical Review

Authors: N. Quadar, A. Chehri, G. Jeon, A. Ahmad

Citations: 23

Year: 2021

Communication and Localization Techniques in VANET Network for Intelligent Traffic System in Smart Cities: A Review

Authors: A. Chehri, N. Quadar, R. Saadane

Citations: 15

Year: 2020

Survey on Localization Methods for Autonomous Vehicles in Smart Cities

Authors: A. Chehri, N. Quadar, R. Saadane

Citations: 14

Year: 2019

Cybersecurity Issues of IoT in Ambient Intelligence (AmI) Environment

Authors: N. Quadar, A. Chehri, G. Jeon, M. M. Hassan, G. Fortino

Citations: 11

Year: 2022

Visual Analytics Methods for Eye Tracking Data

Authors: N. Quadar, A. Chehri, G. Geon

Citations: 6

Year: 2021

IoT-AI/Machine Learning Experimental Testbeds: The Missing Piece

Authors: N. Quadar, M. Rahouti, M. Ayyash, S. K. Jagatheesaperumal, A. Chehri

Citations: 4

Year: 2024

Intrusion Detection Systems in Automotive Ethernet Networks: Challenges, Opportunities, and Future Research Trends

Authors: N. Quadar, A. Chehri, B. Debaque, I. Ahmed, G. Jeon

Citations: 2

Year: 2024

Wireless Security and IoT Device Identification Using RF Fingerprinting and Deep Learning

Authors: N. Quadar, A. Chehri, B. Debaque

Citations: Not yet available

Year: 2024

Permutation Spreading Technique Employing Spatial Modulation for MIMO-CDMA Systems

Authors: N. Quadar, C. D’Amours

Citations: Not yet available

Year: 2018