Dr. Nikolai Simonov | Artificial Intelligence | Best Researcher Award

Dr. Nikolai Simonov | Artificial Intelligence – Senior researcher at Valiev Institute of Physics and Technology, Russia

Dr. Nikolai Anatolievich Simonov is a distinguished senior scientist whose career reflects profound expertise in physics, mathematics, applied electromagnetics, and artificial intelligence. He has been instrumental in bridging fundamental science with advanced technologies, contributing to several internationally recognized institutions across Russia and South Korea. His interdisciplinary work ranges from microwave tomography and electromagnetic theory to cognitive modeling and neuromorphic system development. With over 80 publications and a widely cited body of work, he stands as a leading authority in modern radio-electronics and intelligent systems.

🎓Academic Profile

Orcid | Scopus | Google Scholar

🎓 Education

Dr. Simonov completed his Master of Science degree in 1978 from Moscow State University, Faculty of Physics, with a specialization in radio-physics. He subsequently earned a Ph.D. in Physics and Mathematics in 1986 from the same university, focusing on radio-physics and radio-electronics. His academic training laid a robust theoretical foundation that continues to guide his scientific innovations today.

💼 Experience

His career trajectory began in 1978 as an Engineer-Physicist at the Scientific and Research Institute of Radio Engineering in Moscow. He advanced to senior scientific roles at Scientific and Industrial Company Vzlet, Research and Development Company Modus, and the Institute of Theoretical and Applied Electromagnetism (ITAE), part of the Russian Academy of Sciences. Internationally, he held leadership roles in South Korea, including positions at Credipass Co., Ceyon Technology Co., the Electronics and Telecommunications Research Institute (ETRI), and Yonsei University. Currently, he serves as a Senior Scientist at NRC “Kurchatov Institute” – Valiev IPT, continuing his pioneering research in AI-driven semantic modeling and electromagnetic theory.

🔬 Research Interest

Dr. Simonov’s early research revolved around applied electromagnetics, radio-frequency imaging, and microwave scattering. Over time, he expanded into millimeter-wave measurements and developed high-resolution microwave tomography systems with biomedical applications. His recent focus includes the conceptualization of a novel “Model of Spots” for mental imagery representation—an approach that blends cognitive psychology, mathematical modeling, and artificial intelligence to support neuromorphic system development. He actively explores mathematical foundations for AI semantic structures and inverse problem-solving in sensor systems.

🏅 Award

In recognition of his scientific excellence, Dr. Simonov was honored with the IEEE Antennas and Propagation Society’s Piergiorgio L. E. Uslenghi Letters Prize Paper Award in 2020. This prestigious international award was granted for his innovative research on human-body electromagnetic scattering models, underscoring his impact in both theoretical and applied electromagnetic science.

📚 Selected Publications

📡 “Method for Scattering of Electromagnetic Waves from the Human Body…” IEEE Antennas and Wireless Propagation Letters, 2019 – Cited in clinical and wearable device research.

🧠 “Spots Concept for Problems of Artificial Intelligence…” Russian Microelectronics, 2020 – Influential in neuromorphic system modeling.

🧬 “Advanced Fast 3D Electromagnetic Solver for Microwave Tomography Imaging” IEEE Transactions on Medical Imaging, 2017 – Widely cited for algorithmic advancement in medical diagnostics.

🧲 “Overcoming Insufficient Microwave Scattering Data in Microwave Tomographic Imaging” IEEE Access, 2021 – Applied in imaging systems where data resolution is limited.

💡 “Application of the Model of Spots for Inverse Problems” Sensors, 2023 – Bridging cognitive modeling with AI-driven sensor development.

🧘 “Mental Imagery Representation by Model of Spots in Psychology” Natural Systems of Mind, 2023 – Cited in cognitive neuroscience and AI literature.

🤖 “Development of an Apparatus of Imaginative Information Representation for Neuromorphic Devices” Russian Microelectronics, 2024 – Gaining attention in the neuromorphic computing community.

🧾 Conclusion

Dr. Nikolai Simonov’s scientific journey is marked by persistent curiosity, interdisciplinary mastery, and a passion for innovation. From foundational electromagnetic theory to AI-inspired cognitive modeling, his research contributions continue to shape modern science and technology. His award-winning publications, global research engagements, and visionary approach to complex problems make him a compelling and deserving nominee for the Best Researcher Award. His ongoing work not only pushes the boundaries of scientific understanding but also opens transformative pathways for the future of intelligent systems.

Assoc Prof. Dr. Majdi Khalid | Machine learning | Best Researcher Award

Associate Professor at Umm Al-Qura University

Assoc. Prof. Dr. Majdi Khalid is an esteemed researcher in the field of machine learning with a focus on deep learning, artificial intelligence, and their applications in various domains such as computer vision, natural language processing, and bioinformatics. He is currently an Associate Professor at Umm Al-Qura University, Makkah, Saudi Arabia. Dr. Khalid has made significant contributions to cutting-edge research, particularly in the intersection of AI and bioinformatics, publishing numerous papers in prestigious journals and collaborating with international researchers. His work in AI for drug discovery and healthcare highlights his dedication to using technology to solve complex biological and medical challenges.

Profile:

ORCID

Education:

Dr. Khalid holds a Ph.D. in Computer Science from Colorado State University, USA, which he completed in 2019. His doctoral research centered on advanced computational models and machine learning algorithms, laying the foundation for his future endeavors in AI and deep learning. Prior to his Ph.D., Dr. Khalid earned his Master of Computer Science (M.C.S.) from the same institution in 2013, and a Bachelor of Science (B.S.) in Computer Science from Umm Al-Qura University in 2006. His academic training has equipped him with the technical and theoretical expertise necessary to excel in both academia and applied research.

Experience:

Dr. Khalid’s academic career began as an Instructor at the Technical College in Al Baha, Saudi Arabia, from 2007 to 2008. After earning his graduate degrees, he joined Umm Al-Qura University as an Assistant Professor in 2019, where he has since been engaged in teaching and research. Throughout his academic journey, Dr. Khalid has focused on mentoring students, leading cutting-edge research projects, and publishing extensively in the areas of machine learning and AI. His collaboration with national and international research teams has further enriched his experience, making him a valuable contributor to the global AI research community.

Research Interests:

Dr. Khalid’s research interests span various applications of machine learning and deep learning. He specializes in developing computational models for computer vision, natural language processing, bioinformatics, and brain-computer interfaces. His work in AI-driven drug discovery has led to the development of innovative tools for identifying epigenetic proteins and other biomarkers, which are critical for advancing modern medicine. Dr. Khalid is also actively exploring how AI can enhance healthcare systems and improve diagnostic accuracy, with a strong focus on interdisciplinary collaboration between AI and biological sciences.

Awards:

Dr. Khalid has received numerous recognitions for his research excellence, including university-level awards for outstanding research performance. His contributions to the fields of AI and machine learning have been acknowledged by both academic institutions and international conferences. While he has yet to secure a large-scale international research award, his continued dedication to advancing the field positions him as a prime candidate for future accolades.

Publications:

Ali, Farman, Abdullah Almuhaimeed, Majdi Khalid, et al. (2024). “DEEPEP: Identification of epigenetic protein by ensemble residual convolutional neural network for drug discovery.” Methods.
- Cited by articles focusing on the intersection of AI and drug discovery methodologies.
  Read the article here
Khalid, Majdi, Farman Ali, et al. (2024). “An ensemble computational model for prediction of clathrin protein by coupling machine learning with discrete cosine transform.” Journal of Biomolecular Structure and Dynamics.
- Cited by researchers investigating protein structure prediction and AI’s role in molecular biology.
  Read the article here
Alsini, Raed, Abdullah Almuhaimeed, et al. (2024). “Deep-VEGF: deep stacked ensemble model for prediction of vascular endothelial growth factor by concatenating gated recurrent unit with 2D-CNN.” Journal of Biomolecular Structure and Dynamics.
- Cited by scholars working on AI-based predictive modeling for biological data.
  Read the article here
Alohali, Manal Abdullah, et al. (2024). “Textual emotion analysis using improved metaheuristics with deep learning model for intelligent systems.” Transactions on Emerging Telecommunications Technologies.
- Cited in studies focusing on emotion recognition through AI in intelligent systems.
  Read the article here
Majdi Khalid (2023). “Advanced Detection of COVID-19 through X-ray Imaging using CovidFusionNet with Hybrid CNN Fusion and Multi-resolution Analysis.” International Journal of Advanced Computer Science and Applications.
- Cited by works exploring AI-based solutions for medical imaging and diagnostics.
  Read the article here

Ali, Muhammad Umair, Majdi Khalid, et al. (2023). “Enhancing Skin Lesion Detection: A Multistage Multiclass Convolutional Neural Network-Based Framework.” Bioengineering, 10(12): 1430.
- Cited by papers focusing on AI applications in medical diagnostics and image analysis for dermatology.
  Read the article here
Alghushairy, Omar, Farman Ali, Wajdi Alghamdi, Majdi Khalid, et al. (2023). “Machine learning-based model for accurate identification of druggable proteins using light extreme gradient boosting.” Journal of Biomolecular Structure and Dynamics, 2023: 1-12.
- Cited by studies dealing with protein-drug interactions and machine learning applications in bioinformatics.
  Read the article here
Obayya, Marwa, Fahd N. Al-Wesabi, Rana Alabdan, Majdi Khalid, et al. (2023). “Artificial Intelligence for Traffic Prediction and Estimation in Intelligent Cyber-Physical Transportation Systems.” IEEE Transactions on Consumer Electronics, 2023.
- Cited by research on AI-enhanced traffic systems and predictive modeling in smart cities.
  Read the article here
Alruwais, Nuha, Eatedal Alabdulkreem, Majdi Khalid, et al. (2023). “Modified Rat Swarm Optimization with Deep Learning Model for Robust Recycling Object Detection and Classification.” Sustainable Energy Technologies and Assessments, 59: 103397.
- Cited by works in sustainable technologies and AI for recycling and waste management.
  Read the article here
Adnan, Adnan, Wang Hongya, Farman Ali, Majdi Khalid, et al. (2023). “A Bi-Layer Model for Identification of piwiRNA using Deep Neural Learning.” Journal of Biomolecular Structure and Dynamics, 2023: 1-9.

Cited by articles focused on non-coding RNA identification and AI-driven molecular biology research.
Read the article here

Conclusion

Assoc. Prof. Dr. Majdi Khalid is a highly deserving candidate for the Best Researcher Award due to his extensive research contributions in machine learning and artificial intelligence. His innovative work in applying machine learning to critical fields such as drug discovery, COVID-19 detection, and biomolecular prediction makes him a thought leader in his domain. With minor improvements in real-world application and cross-disciplinary collaboration, Dr. Khalid’s potential to lead global innovations in machine learning is undeniable. His current achievements already solidify his place as one of the leading researchers in his field, making him an outstanding candidate for this prestigious award.

Nikolai Simonov | Artificial Intelligence | Best Researcher Award