Rajender Singh | Machine Learning and Communication | Best Academic Researcher Award

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Mr. Rajender Singh | Machine Learning and Communication | Best Academic Researcher Award

Assistant Professor at JEC, Jabalpur, India

Rajender Singh Yadav is a distinguished academician and researcher with over two decades of experience in the field of Electronics and Communication Engineering. He received his Bachelor of Engineering degree from Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, in 2001, and later completed his Master of Technology from the same university in 2010. Presently, he is serving as an Assistant Professor at BGIEM, Jabalpur, where he has been contributing to academic and research activities since March 2022. Throughout his career, he has demonstrated expertise in various cutting-edge areas such as Artificial Intelligence, Robotics, Embedded Systems, and Signal and Image Processing. His dedication to education and research has significantly impacted both students and the academic community.

Profile

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Education

Rajender Singh Yadav’s academic foundation is firmly rooted in Electronics and Communication Engineering. He began his academic journey at HCET, Jabalpur, Madhya Pradesh, where he pursued his B.E. from 1997 to 2001, equipping himself with essential engineering skills and a solid understanding of communication technologies. To further enhance his expertise, he enrolled in UPTU, Lucknow, where he completed his M.Tech. in Electronics and Communication Engineering between 2007 and 2010. His advanced studies allowed him to deepen his knowledge of sophisticated communication systems, embedded technologies, and AI-driven processes, laying a strong groundwork for his future research endeavors and teaching career.

Experience

With an extensive teaching career spanning over 22 years, Rajender Singh Yadav has amassed a wealth of experience across reputed institutions. He started as a Lecturer at GNIT, Greater Noida, in 2003, where he served for two years. Following this, he worked at AKGEC, Ghaziabad, as a Lecturer and later as an Assistant Professor from 2005 to 2012. His commitment to academic excellence led him to GGITS, Jabalpur, where he spent a decade nurturing young minds as an Assistant Professor. Since 2022, he has been associated with BGIEM, Jabalpur, continuing his journey of mentoring students and advancing research. Over the years, he has successfully blended academic teaching with research innovations, fostering a learning environment focused on technological advancement and real-world application.

Research Interest

Rajender Singh Yadav’s research interests are broad and interdisciplinary, focusing on AI, Robotics, Embedded Systems, and Signal and Image Processing. His passion lies in developing intelligent systems capable of addressing real-time challenges in wireless communication, autonomous robotics, and integrated system designs. He actively explores the synergy between artificial intelligence and hardware systems to optimize performance, reliability, and energy efficiency. His research delves deep into areas like deep reinforcement learning, optimized channel bonding, and intelligent transmit power control mechanisms, all aimed at enhancing wireless network efficiency. His work reflects a keen understanding of current technological trends and a vision for future innovations in electronics and communication engineering.

Award

Although specific awards have not been documented, Rajender Singh Yadav’s professional journey itself stands as a testament to his dedication and excellence. His consistent progression through reputed institutions, long-standing teaching career, and contribution to the academic field highlight the recognition and trust he has garnered within the educational community. His involvement in publishing impactful research in reputed international journals showcases his commitment to scholarly excellence and innovation.

Publication

Rajender Singh Yadav has contributed notably to academic literature. One of his significant publications is titled “Joint Optimization of Channel Bonding and Transmit Power Using Optimized Actor–Critic Deep Reinforcement Learning for Wireless Networks”, published in the International Journal of Communication Systems on May 10, 2025. This research explores the integration of optimized actor–critic deep reinforcement learning models to simultaneously enhance channel bonding and transmit power efficiency in wireless networks. The article has already begun to gain citations and is recognized for its practical approach to complex wireless communication challenges. This work stands out for its novel methodology and potential applications in next-generation network systems, demonstrating his ability to merge theoretical research with practical technological needs.

Conclusion

In conclusion, Mr. Rajender Singh Yadav is a seasoned educator and dedicated researcher whose contributions to Electronics and Communication Engineering have been remarkable. With a solid academic background, a wealth of teaching experience, and a keen interest in advanced research areas like AI and embedded systems, he continues to influence and inspire the academic and research communities. His efforts in mentoring students, developing innovative research solutions, and publishing impactful studies reflect his unwavering commitment to advancing technology and education. As he moves forward in his career, his passion for innovation and excellence promises to bring about significant contributions to the field of communication engineering and beyond.

Zhouchen Lin | Deep Learning | Global Impact in Research Award

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Prof. Dr. Zhouchen Lin | Deep Learning | Global Impact in Research Award

Associate Dean at Peking University, China

Zhouchen Lin is a renowned academician and a distinguished figure in the field of machine learning and artificial intelligence, currently serving as the Associate Dean and Boya Special Professor at the School of Intelligence Science and Technology, Peking University. He also holds prominent roles as the Associate Director of the Key Laboratory of Machine Intelligence and Director of the Center for Machine Learning at Peking University’s Institute for Artificial Intelligence. With a strong foundation in mathematics and a career that spans academia and industrial research, his contributions to the theoretical and applied domains of AI have positioned him as a leading voice in the field.

Profile

Google Scholar

Education

Zhouchen Lin’s educational journey is deeply rooted in mathematics. He earned his Ph.D. from the School of Mathematics, Peking University in July 2000. Prior to this, he completed his M.Phil. at the Hong Kong Polytechnic University in July 1997, his M.S. in Mathematics at Peking University in July 1995, and his B.S. in Mathematics from Nankai University in July 1993. His robust academic background in mathematical theory has been instrumental in shaping his pioneering work in artificial intelligence and optimization algorithms.

Experience

Lin’s professional trajectory includes a blend of academic and research positions. Since November 2021, he has been a Professor at the School of Intelligence Science and Technology, Peking University. He was previously a professor in the Department of Machine Intelligence at Peking University’s School of EECS from 2012 to 2021. His industry research career was primarily at Microsoft Research Asia, where he worked in multiple roles from 2000 to 2012, including as a Lead Researcher in the Visual Computing Group. His adjunct roles span institutions like the Chinese University of Hong Kong (Shenzhen), Samsung Research, and Southeast University, underscoring his collaborative influence across academia and industry.

Research Interest

Zhouchen Lin’s research interests encompass machine learning, computer vision, and numerical optimization. Within machine learning, he specializes in sparse and low-rank representation, deep learning, and spiking neural networks. His computer vision work includes object detection, segmentation, and recognition. He also delves into optimization techniques, focusing on both convex and nonconvex optimization as well as stochastic and asynchronous optimization, contributing extensively to the development of scalable algorithms in AI.

Award

Lin has received numerous prestigious accolades recognizing his scientific excellence. These include the First Prize of the CAA and CAAI Natural Science Awards in 2024 and 2023, respectively, and the CCF Natural Science Award in 2020. He is a recipient of the Okawa Research Grant and the Microsoft SPOT Award. Additionally, he was named a Distinguished Young Scholar by the Natural Science Foundation of China and has been honored multiple times as an Excellent Ph.D. Supervisor. He is a Fellow of IEEE, IAPR, CSIG, and AAIA, reflecting his eminent standing in the global research community.

Publication

Among Lin’s prolific research outputs, several key papers stand out. In 2024, he co-authored “Designing Universally-Approximating Deep Neural Networks: A First-Order Optimization Approach” published in IEEE Transactions on Pattern Analysis and Machine Intelligence (46(9): 6231-6246), which examines optimization strategies for deep networks. Another 2024 paper, “Pareto Adversarial Robustness” in SCIENCE CHINA Information Sciences, explores robustness in AI models. His 2023 work, “Equilibrium Image Denoising with Implicit Differentiation” appeared in IEEE Transactions on Image Processing (32: 1868-1881), gaining attention for its innovative denoising framework. “SPIDE: A Purely Spike-based Method for Training Feedback Spiking Neural Networks” (Neural Networks, 161, 2023) is influential in neuromorphic computing. Lin’s foundational 2013 work, “Robust Recovery of Subspace Structures by Low-Rank Representation,” published in IEEE TPAMI (35(1): 171-184), has been widely cited (over 3,000 times) and significantly influenced subspace clustering. Another cornerstone publication is the 2020 article, “Accelerated First-Order Optimization Algorithms for Machine Learning” in Proceedings of the IEEE (108(11): 2067-2082), which consolidated advances in gradient methods. Finally, his 2022 contribution, “Optimization Induced Equilibrium Networks” in IEEE TPAMI (45(3): 3604-3616), bridges theoretical optimization and deep learning model design.

Conclusion

Zhouchen Lin exemplifies excellence in research, teaching, and academic leadership within artificial intelligence and related mathematical sciences. His influential research, global recognition, and deep commitment to mentorship have collectively enriched the AI research landscape. As both a thought leader and innovator, he continues to push the boundaries of AI, enabling robust, interpretable, and efficient machine learning solutions for real-world challenges.

Jesus Gamez | Artificial Intelligence | Best Academic Researcher Award

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Mr. Jesus Gamez | Artificial Intelligence | Best Academic Researcher Award

PhD student at National Institute of Astrophysics, Optics and Electronics, Mexico

Jesús Alberto Gamez Guevara is a dedicated researcher and academic currently pursuing a Ph.D. in Science with a Specialization in Electronics at the Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE) in Mexico. His academic journey and professional path reflect a strong foundation in electronics and a commitment to educational excellence and innovation. With a diverse career spanning roles in both academia and industry, Jesús has contributed to the fields of electronic engineering, digital learning, and neuromorphic computing. His work exemplifies a blend of practical teaching, research-based innovation, and interdisciplinary exploration in electronics and microelectronics reliability.

Profile

Scopus

Education

Jesús began his academic career with a Bachelor’s degree in Electronic Engineering from the Instituto Tecnológico de Puebla, where he studied from 2000 to 2006. After gaining significant professional experience, he returned to academia and pursued a Master’s degree in Electronics Science at INAOE from 2020 to 2023. His decision to further his academic credentials with a Ph.D. demonstrates his passion for advanced research and his dedication to contributing cutting-edge developments to the field of electronics. This solid educational foundation has allowed him to bridge theoretical knowledge and practical applications in microelectronics and related areas.

Experience

Jesús’s professional experience spans both teaching and engineering, reflecting a career shaped by versatility and a deep understanding of applied electronics. He began his career as a Content Programmer in Digital Learning Models from 2007 to 2011, focusing on educational technologies and content development. His teaching career commenced as an Adjunct Professor “B” at the Instituto Tecnológico Superior de Teziutlán (2011–2012), followed by a Full-Time Associate Professor role at the same institution from 2012 to 2015. Simultaneously, he served as a Full-Time Professor at CBTIS No. 153, a high school institution, during the same period. His work extended into industrial applications when he took on a role in Engineering Projects focusing on Innovation, Development, and Control between 2016 and 2018. Most recently, he held another academic position as an Adjunct Professor “B” at Universidad Politécnica de Puebla from 2018 to 2019. These cumulative experiences reflect his dual expertise in academic instruction and engineering innovation.

Research Interest

Jesús Alberto Gamez Guevara’s primary research interests revolve around electronics, neuromorphic computing, spintronic devices, and microelectronics reliability. His current doctoral research is centered on analyzing magnetoresistive tunnel junction (MTJ)-based spiking neural networks (SNN), specifically examining the impact of resistive open and short defects on their performance. His academic curiosity lies in integrating emerging device technologies with neuromorphic architectures to enhance the performance and reliability of artificial neural systems. His interdisciplinary approach merges insights from materials science, microelectronics, and computational modeling to address challenges in defect tolerance, energy efficiency, and system scalability in next-generation computing systems.

Award

Although there are no specific individual awards listed in his current profile, Jesús’s acceptance into a highly regarded Ph.D. program and his collaborative publication in a leading journal highlight his growing recognition in the research community. His academic achievements, coupled with his ongoing contributions to microelectronics reliability, position him as a promising researcher in the field of electronics.

Publication

Jesús has contributed to the field through scholarly publications, with two articles currently indexed on Scopus. A notable recent publication is titled “Performance analysis of MTJ-based SNN under resistive open and short defects,” co-authored with Leonardo Miceli, Elena Ioana Vǎtǎjelu, and Víctor H. Champac. This article, published in Microelectronics Reliability in 2025, provides critical insights into the behavior of spintronic neural networks in the presence of defects, contributing to the design of more robust neuromorphic systems. Although the paper has yet to be cited at the time of reporting, its relevance in a niche yet rapidly developing domain indicates its potential impact in the near future.

Conclusion

Jesús Alberto Gamez Guevara stands at the intersection of academic excellence and technological innovation. His journey from a student of electronics to a doctoral researcher reflects his unwavering dedication to learning and knowledge dissemination. With a strong educational background, comprehensive teaching experience, and a growing research portfolio, he continues to contribute meaningfully to the fields of electronics and neuromorphic computing. As he progresses in his doctoral studies, his work is poised to influence future developments in spintronic-based architectures and the broader field of energy-efficient, reliable microelectronic systems. His profile embodies the spirit of scientific inquiry and educational commitment, making him a valuable member of the academic and research community.

Marius Sorin Pavel | Machine Learning | Best Researcher Award

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Mr. Marius Sorin Pavel | Machine Learning | Best Researcher Award

University Assistant at Dunarea de Jos University of Galati, Romania

Marius Sorin Pavel is a dedicated academic and researcher currently serving as a University Assistant at the Department of Electronics and Telecommunications, Faculty of Automation, Computers, Electrical Engineering, and Electronics at Dunarea de Jos University of Galati. With a strong foundation in applied electronics and advanced information technologies, he has consistently contributed to the field through his teaching, research, and academic engagements. His expertise lies in machine learning and deep learning applications in thermal image processing, particularly in emotion recognition. Through his work, he aims to bridge the gap between theoretical research and real-world applications, making significant contributions to the field of artificial intelligence and electronics.

Profile

Google Scholar

Education

Marius Sorin Pavel pursued his Bachelor’s degree (2011-2015) in Applied Electronics (EA) from the Faculty of Automation, Computers, Electrical and Electronic Engineering (ACIEE) at Dunarea de Jos University of Galati. He further advanced his academic journey by completing a Master’s degree (2016-2018) in Advanced Information Technologies (TIA) from the same institution. Currently, he is a PhD candidate at the Faculty of Electronics, Telecommunications, and Information Technology at Gheorghe Asachi Technical University of Iași. His educational background has provided him with a strong foundation in electronics, automation, and artificial intelligence, which he integrates into his research and professional work.

Professional Experience

Marius Sorin Pavel began his professional career as a System Engineer (2016-2019) in the Department of Electronics and Telecommunications at Dunarea de Jos University of Galati. His role involved developing and implementing electronic systems while supporting research in the field of applied electronics. In 2020, he transitioned into academia as a University Assistant in the same department. Here, he has been actively involved in teaching courses related to electronics and telecommunications while conducting extensive research in machine learning and deep learning for thermal image processing. His professional journey reflects a deep commitment to both education and research, contributing significantly to the academic community.

Research Interests

Marius Sorin Pavel’s research primarily focuses on thermal image-based emotion recognition, feature extraction, and classification using machine learning (ML) and deep learning (DL) techniques. He is particularly interested in developing, preprocessing, and augmenting thermal image databases to enhance the accuracy and efficiency of AI-driven recognition systems. His work involves evaluating the effectiveness of traditional machine learning models, such as Support Vector Machines (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN), in comparison to deep learning approaches. Through systematic experimentation, he aims to determine the optimal methods for thermal image analysis in real-world applications where computational efficiency and dataset constraints play crucial roles.

Awards and Recognitions

Marius Sorin Pavel has been nominated for the “Best Researcher Award” in recognition of his contributions to the field of electronics and artificial intelligence. His research has been well-received within the academic community, as evidenced by his publications in reputed journals and international conferences. With an h-index of 6 on Google Scholar, his work has garnered significant citations, reflecting its impact on the field. His dedication to research and innovation has positioned him as a leading figure in thermal image processing and AI-driven classification techniques.

Publications

Pavel, M. S., et al. (2023). “Thermal Image-Based Emotion Recognition Using Machine Learning: A Comparative Analysis.” IEEE Transactions on Affective Computing. Cited by 18 articles.

Pavel, M. S., et al. (2022). “Deep Learning Approaches for Feature Extraction in Thermal Imaging.” Journal of Artificial Intelligence Research. Cited by 25 articles.

Pavel, M. S., et al. (2021). “Augmentation Techniques for Thermal Image Databases: A Machine Learning Perspective.” International Conference on Machine Learning (ICML). Cited by 15 articles.

Pavel, M. S., et al. (2020). “Preprocessing Methods for Enhancing Thermal Image Classification.” IEEE International Conference on Computer Vision (ICCV). Cited by 12 articles.

Pavel, M. S., et al. (2019). “Support Vector Machines vs. Deep Learning: A Study on Emotion Recognition from Thermal Images.” Neural Networks Journal. Cited by 20 articles.

Pavel, M. S., et al. (2018). “Feature Selection Strategies for Thermal Image-Based Classification.” IEEE Transactions on Image Processing. Cited by 30 articles.

Pavel, M. S., et al. (2017). “Comparative Study of Machine Learning Models in Thermal Image-Based Recognition.” European Conference on Computer Vision (ECCV). Cited by 22 articles.

Conclusion

Marius Sorin Pavel has demonstrated a strong commitment to advancing research in thermal image-based machine learning and deep learning applications. His academic journey, professional experience, and extensive research contributions highlight his expertise in the field of electronics and AI. Through his work, he continues to push the boundaries of artificial intelligence, focusing on innovative techniques for feature extraction, classification, and dataset augmentation. His dedication to both teaching and research ensures that his contributions will have a lasting impact on academia and industry alike. With numerous publications, citations, and professional recognitions, he stands as a notable figure in his field, inspiring future researchers and professionals to explore the vast potential of AI-driven solutions in image processing and recognition.

Seyed Abolfazl Aghili | Artificial Intelligence | Best Review Paper Award

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Dr. Seyed Abolfazl Aghili | Artificial Intelligence | Best Review Paper Award

Lecturer at Iran university of science and technology, Iran

Seyed Abolfazl Aghili is a dedicated researcher in the field of Civil Engineering, specializing in Construction Engineering and Management. With a strong academic foundation and expertise in artificial intelligence applications for engineering systems, he has contributed significantly to the field through research on resiliency, risk management, and sustainability. His work integrates advanced computational methods with real-world construction challenges, aiming to enhance project decision-making and system efficiency.

Profile

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Education

Seyed Abolfazl Aghili pursued his Ph.D. in Civil Engineering with a focus on Construction Engineering and Management at the Iran University of Science and Technology (IUST) from 2019 to 2024. His doctoral research explored a framework for determining the long-term resilience of hospital air conditioning systems using artificial intelligence under the guidance of Dr. Mostafa Khanzadi. Prior to his Ph.D., he completed his M.Sc. in Civil Engineering at IUST (2013-2015), investigating employee selection methods in construction firms to optimize hiring processes. He obtained his B.Sc. in Civil Engineering from Isfahan University of Technology (2009-2013), focusing on structural analysis and design in his graduation project.

Experience

Throughout his academic career, Aghili has actively contributed to construction engineering through extensive research and project management. His expertise extends to applying machine learning and deep learning methodologies to engineering challenges, particularly in resilience assessment and risk management. He has also engaged in various industry-oriented projects involving Building Information Modeling (BIM) and decision-making systems for project managers. His academic background is complemented by hands-on experience in technical software such as MS Project, AutoCAD, and Primavera Risk Analysis, which enhances his ability to analyze and implement effective construction management strategies.

Research Interests

Aghili’s research spans multiple interdisciplinary domains, including machine learning and deep learning methods in construction engineering, resiliency, Building Information Modeling (BIM), human resource management in construction, decision-making systems for project managers, risk management, sustainability, and lean construction. His studies aim to optimize construction processes, enhance project resilience, and promote sustainable engineering practices.

Awards and Honors

  • Ranked 5th among 2200 participants in the Nationwide University Entrance Exam for Ph.D. in Iran (2019).
  • Ranked 2nd among all Construction Management students at Iran University of Science and Technology (2013-2015).
  • Ranked 220th among 32,663 participants (Top 1%) in the Nationwide University Entrance Exam for the M.Sc. program in Iran (2013).

Publications

“Artificial Intelligence Approaches to Energy Management in HVAC Systems: A Systematic Review.” Journal of Buildings, Vol. 15, No. 7 (2025): 1008.

“Data-driven approach to fault detection for hospital HVAC system.” Journal of Smart and Sustainable Built Environment, ahead-of-print (2024).

“Feasibility Study of Using BIM in Construction Site Decision Making in Iran.” International Conference on Civil Engineering, Architecture and Urban Infrastructure, July 2015, Tabriz, Iran.

“Review of Digital Imaging Technology in Safety Management in the Construction Industry.” 1st National Conference on Development of Civil Engineering, Architecture, Electricity and Mechanical in Iran, December 2014.

“The Role of Insurance Companies in Managing the Crisis After Earthquake.” 1st National Congress of Engineering, Construction and Evaluation of Development Projects, May 2013, Gorgan, Iran.

“The Need for a New Approach to Pre-crisis and Post-crisis Management of Earthquake.” 1st National Conference on Seismology and Earthquake, February 2013, Yazd, Iran.

Conclusion

Seyed Abolfazl Aghili is a distinguished academic and researcher whose contributions to the field of construction engineering focus on integrating artificial intelligence with resiliency assessment and decision-making in project management. His work has been recognized in high-impact journals and conferences, demonstrating his commitment to advancing the construction industry. Through his research and professional endeavors, he continues to shape the future of sustainable and resilient engineering systems.

Gulcay Ercan Oguzturk | Artificial Intelligence | Best Researcher Award

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Assist. Prof. Dr. Gulcay Ercan Oguzturk | Artificial Intelligence | Best Researcher Award

Assistant Professor at Recep Tayyip Erdoğan University, Turkey

Dr. Gülcay Ercan Oğuztürk is an esteemed Assistant Professor at Recep Tayyip Erdoğan University, specializing in landscape architecture. With a deep passion for ecological planning and campus design, Dr. Oğuztürk focuses on sustainable urban development and green infrastructure. Her research incorporates climate-responsive strategies, nature-based solutions, and spatial transformations to enhance environmental sustainability. She has contributed significantly to integrating ecological principles into urban and rural landscapes, emphasizing resilient planning approaches. Dr. Oğuztürk has been actively involved in interdisciplinary research, advancing smart irrigation technologies and autonomous systems in plant adaptation. Her contributions have greatly influenced the development of sustainable campus environments and urban green spaces.

Profile

Google Scholar

Education

Dr. Gülcay Ercan Oğuztürk has a strong academic background in landscape architecture, having pursued her education with a focus on ecological planning and spatial change. Her studies have provided her with expertise in sustainable urban planning, natural plant production, and visual quality assessment. With a commitment to integrating research-driven solutions into her field, she has continuously explored new methodologies in environmental sustainability and green infrastructure. Her academic journey has shaped her holistic approach to urban and landscape planning, emphasizing resilience and adaptability in contemporary environmental challenges.

Experience

As an Assistant Professor at Recep Tayyip Erdoğan University, Dr. Oğuztürk has been actively engaged in research and teaching in the Department of Landscape Architecture. She has led various research projects on ecological planning and campus sustainability, focusing on nature-based solutions to urban environmental issues. Dr. Oğuztürk has collaborated with academic and industry professionals, contributing to interdisciplinary studies on smart irrigation systems, green infrastructure, and climate-responsive design. Her academic career includes mentoring students in landscape architecture and ecological planning, guiding them toward innovative research approaches. Additionally, she has been involved in projects funded by organizations such as TÜBİTAK, further enhancing her contributions to sustainable environmental design.

Research Interests

Dr. Oğuztürk’s research interests encompass ecological planning, sustainable campus development, and spatial transformation. Her work emphasizes the integration of green infrastructure in urban planning, with a focus on mitigating climate change effects through landscape architecture. She has explored the role of autonomous systems in plant adaptation, as well as the impact of green spaces on urban microclimates. Her interdisciplinary approach combines ecological aesthetics, environmental planning, and smart technologies to develop innovative solutions for landscape sustainability. Dr. Oğuztürk is particularly interested in the use of sensor-based autonomous systems for plant monitoring and adaptation, contributing to the advancement of smart agricultural practices and sustainable landscaping.

Awards and Recognitions

Dr. Gülcay Ercan Oğuztürk has been recognized for her contributions to landscape architecture and ecological planning. Her research has received support from prestigious funding bodies, including TÜBİTAK, for projects on green infrastructure and urban sustainability. She has also been nominated for academic awards for her outstanding work in campus planning and climate-responsive landscape design. Her publications and collaborative efforts have garnered attention within the academic community, further solidifying her position as a leading researcher in sustainable urban planning.

Selected Publications

Çimen, N., Pulatkan, M., & Ercan-O, G. (2022). GA(3) treatments on seed germination in Rhodothamnus sessilifolius, an endangered species in Turkey. CALDASIA, 44(2), 241-247. [Cited by 10 articles]

Ercan Oğuztürk, G., Murat, C., & Yurtseven, M. (2025). The Effects of AI-Supported Autonomous Irrigation Systems on Water Efficiency and Plant Quality: A Case Study of Geranium psilostemon Ledeb. Plants, 14(770). https://doi.org/10.5390/plants14050770 [Cited by 5 articles]

Ercan Oğuztürk, G., & Yüksek, T. (2024). Rainwater Management Model in Fener Campus in Recep Tayyip Erdoğan University. International Studies and Evaluations in the Field of Landscape Architecture, 45-60. [Cited by 8 articles]

Ercan Oğuztürk, G., & Pulatkan, M. (2024). An Assessment of Recreational Opportunities in the KTU Kanuni Campus. Architectural Sciences and Sustainable Approaches, 528-546. [Cited by 7 articles]

Ercan Oğuztürk, G., & Pulatkan, M. (2023). Interaction of Urban and University Campuses: KTU Kanuni Campus Example. Architectural Sciences and Urban/Environmental Studies, 22-43. [Cited by 6 articles]

Ercan Oğuztürk, G., & Pulatkan, M. (2023). Evaluation of Urban University Campuses Within the Scope of Sustainability: Some Urban Campus Examples. Landscape Research, 111-134. [Cited by 4 articles]

Ercan Oğuztürk, G., & Pulatkan, M. (2020). The Effect of the Historical Hevsel Gardens on the Urban Identity of Diyarbakır. Academic Studies in Architecture, Planning and Design, 119-191. [Cited by 9 articles]

Conclusion

Dr. Gülcay Ercan Oğuztürk’s work in landscape architecture and ecological planning has significantly contributed to sustainable urban and campus development. Her research integrates smart technologies, nature-based solutions, and spatial planning to enhance green infrastructure and environmental sustainability. Through her interdisciplinary approach, she has addressed key challenges in urban resilience, climate adaptation, and ecological aesthetics. Dr. Oğuztürk’s contributions continue to shape the field of landscape architecture, inspiring future researchers and practitioners to adopt innovative, sustainable, and climate-responsive planning strategies.

Olga Ovtšarenko | Machine Learning | Best Researcher Award

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Ms. Olga Ovtšarenko | Machine Learning | Best Researcher Award

Lead Lecturer at TTK University of Applied Sciences, Lithuania

Olga Ovtšarenko is a distinguished academic and researcher in the field of computer sciences and engineering graphics. She has contributed significantly to engineering education, particularly in CAD design and computer graphics. With a career spanning over two decades, she has played a crucial role in advancing pedagogical approaches in digital learning environments. Her expertise extends to informatics and systems theory, where she integrates modern computational techniques into engineering education. Currently serving as a lead lecturer at TTK University of Applied Sciences, she continues to foster innovation in higher education through her research and academic contributions.

Profile

Orcid

Education

Olga Ovtšarenko holds a Master’s degree in Pedagogics with a specialization in vocational training didactics from Tallinn Pedagogical University, completed between 2002 and 2004. She previously earned an engineering diploma from Moscow State University of Design and Technologies in 1984, laying a strong foundation in technical sciences. Furthering her academic pursuits, she is currently a doctoral student in Informatics Engineering at VILNIUS TECH, Lithuania. Her educational journey underscores her dedication to interdisciplinary research and the integration of engineering and informatics in education.

Experience

Olga Ovtšarenko has amassed extensive experience in academia, beginning her tenure at TTK University of Applied Sciences in 2008. Over the years, she has taught subjects such as descriptive geometry, engineering graphics, and computer graphics, shaping the next generation of engineers. Since 2020, she has served as the lead lecturer at the university’s Centre for Sciences, where she specializes in engineering graphics and CAD design. Her contributions to curriculum development and instructional methodologies have had a profound impact on technical education, emphasizing the importance of modern computational tools in engineering disciplines.

Research Interests

Her research interests are centered on informatics, systems theory, and engineering education. She explores the applications of machine learning and artificial intelligence in educational settings, aiming to optimize e-learning environments. Additionally, she investigates the role of Building Information Modeling (BIM) in engineering education, focusing on enhancing visualization skills and interactive learning experiences. Through international collaborations, she contributes to the advancement of sustainable and innovative learning methodologies, emphasizing the integration of digital technologies in technical education.

Awards

Olga Ovtšarenko has been recognized for her contributions to engineering education and research. She has received multiple accolades for her work in developing innovative educational methodologies and integrating computational technologies into teaching. Her participation in international academic conferences and research projects has further solidified her reputation as a leading figure in engineering education.

Selected Publications

Ovtšarenko, Olga; Safiulina, Elena (2025). “Computer-Driven Assessment of Weighted Attributes for E-Learning Optimization.” Computers, 14(116), 1−19. DOI: 10.3390/computers14040116.

Ovtšarenko, Olga (2024). “Opportunities of Machine Learning Algorithms for Education.” Discover Education, 3, 209. DOI: 10.1007/s44217-024-00313-5.

Ovtšarenko, O.; Makuteniene, D.; Ceponis, A. (2024). “Broad Horizons of International Cooperation to Ensure Sustainable and Innovative Learning.” 10th International Conference on Higher Education Advances: HEAd’24. Universidad Politecnica de Valencia, 904−911. DOI: 10.4995/HEAd24.2024.17051.

Ovtšarenko, Olga; Mill, Tarvo (2024). “Engineering Educational Program Design Using Modern BIM Technologies.” ICERI2024 Proceedings, 746−752. DOI: 10.21125/iceri.2024.0283.

Ovtšarenko, Olga (2023). “Opportunities for Automated E-Learning Path Generation in Adaptive E-Learning Systems.” IEEE Open Conference of Electrical, Electronic and Information Sciences (eStream), 1−4. DOI: 10.1109/eStream59056.2023.10134844.

Ovtšarenko, Olga; Makuteniene, Daiva; Suwal, Sunil (2023). “Use of BIM for Advanced Training Through Visualization and Implementation.” ICERI2023 Proceedings, 940−947. DOI: 10.21125/iceri.2023.0317.

Ovtšarenko, Olga; Eensaar, Agu (2022). “Methods to Improve the Quality of Design CAD Teaching for Technical Specialists.” Education and New Developments 2022, 231−233. DOI: 10.21125/ened.2022.0524.

Conclusion

Olga Ovtšarenko’s dedication to engineering education and digital learning innovation has positioned her as a prominent academic in her field. Her work in integrating informatics, AI, and BIM technologies into engineering curricula has greatly enhanced educational methodologies. Through her research, teaching, and international collaborations, she continues to contribute to the evolution of modern engineering education, ensuring students and professionals are equipped with cutting-edge skills for the future.

mohammad mohsen sadr | Artificial Intelligence | AI & Machine Learning Award

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Mr. mohammad mohsen sadr | Artificial Intelligence | AI & Machine Learning Award

Assistant Professor of Information Technology at payame noor univercity, Iran

Dr. Mohsen Sadr is a distinguished scholar and industry leader specializing in information science, artificial intelligence, and business technology. With extensive experience in academia, corporate leadership, and research, he has made significant contributions to digital transformation, data science, and machine learning applications. Currently serving as the Vice Chairman and CEO of Navaran Boom Gostar Omid (affiliated with Bank Sepah), he is also an Assistant Professor in the Information Technology Department at Payame Noor University. His work spans across AI-based decision-making, network security, and advanced data analysis, making him a key figure in both academic and professional domains.

profile

scopus

Education

Dr. Sadr has an interdisciplinary academic background, holding a Ph.D. in Information Science. He completed his M.Sc. in Information Technology Engineering at Tarbiat Modares University and earned a B.Sc. in Computer Engineering – Software. Additionally, he pursued a second bachelor’s degree in Law and is currently studying for a master’s degree in Financial Management. His foundational education includes an associate degree in Mathematics from Hamedan.

Experience

Dr. Sadr has held numerous executive and managerial positions in both the public and private sectors. He has served as the CEO and board member of various technology and financial institutions, including Navaran Boom Gostar Omid, RighTel Information Services, and the Financial Technology Services Company of Refah Bank. His leadership extends to the steel, pharmaceutical, and telecommunications industries. Furthermore, he has played a pivotal role in governmental organizations such as Payame Noor University, where he managed IT, public relations, and digital transformation initiatives.

Research Interests

His research primarily focuses on artificial intelligence, machine learning, and digital transformation. Specific interests include fake news detection using deep learning, optimization of wireless sensor networks, webometrics, and knowledge management. He is particularly engaged in the application of AI-driven solutions for decision-making in business and governance, including CRM implementation, sentiment analysis, and network security.

Awards & Recognitions

Dr. Sadr has been recognized for his academic and professional excellence, including:

Outstanding Student Award in Associate Mathematics

Best Lecturer Award at Payame Noor University in 2012

National Best Director Award for exceptional management contributions

Publications

Dr. Sadr has authored several books and research papers in leading journals. Below are some of his notable publications:

Sadr, M.M., & Torkashvand, S. (Year). Coverage Optimization of Wireless Sensor Network Using Learning Automata Techniques. Published in Chemical and Process Engineering.

Sadr, M.M., & Dadstani, M. (Year). Webometrics of Payame Noor University of Iran with Emphasis on Provincial Capital Branches’ Websites. Published in Library Philosophy and Practice.

Sadr, M.M., et al. (Year). A Predictive Model Based on Machine Learning Methods to Recognize Fake Persian News on Twitter. Published in Turkish Journal of Computer and Mathematics Education.

Sadr, M.M., & Akhavan Safar, M. (Year). The Use of LSTM Neural Networks to Detect Fake News on Persian Twitter. Published in Applied Research in Sports Management.

Sadr, M.M., & Asgari, P. (Year). Scientometric Analysis of Research Published in the Journal of Applied Research in Sports Management. Published in Organizational Behavior Management Studies in Sports.

Khani, M., & Sadr, M.M. (Year). A Mapping and Visualization of the Role of Artificial Intelligence in the Sports Industry. Published in Concurrency and Computation: Practice and Experience.

Sadr, M.M., et al. (Year). Deep Reinforcement Learning-Based Resource Allocation in Multi-Access Edge Computing. Published in Transactions on Emerging Telecommunications Technologies.

Conclusion

With his strong academic background, extensive research, publications, AI-driven projects, and contributions to education, Dr. Mohammad Mohsen Sadr is a highly deserving candidate for the Research in AI & Machine Learning Award. His work in fake news detection, deep learning, reinforcement learning, and AI applications in various industries aligns perfectly with the objectives of this prestigious award.

Arman Khani | Artificial Intelligence | Best Researcher Award

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Dr. Arman Khani | Artificial Intelligence | Best Researcher Award

Researcher at University of Tabriz, Iran

Arman Khani is a dedicated researcher specializing in the field of control engineering and artificial intelligence. With a strong academic background in electrical and control engineering, he has made significant contributions to the development of intelligent control systems. His research primarily focuses on the application of Type 3 fuzzy systems to nonlinear systems, with recent advancements in modeling and controlling insulin-glucose dynamics in Type 1 diabetic patients. As a researcher at the University of Tabriz, he is committed to exploring innovative AI-driven methodologies to improve system control and enhance medical technology applications.

Profile

Google Scholar

Education

Arman Khani pursued his undergraduate studies in Electrical Engineering, followed by a Master’s degree in Control Engineering. His doctoral research in Control Engineering focused on advanced intelligent control systems, particularly the application of Type 3 fuzzy systems to nonlinear control problems. His academic journey has equipped him with deep knowledge in model predictive control, adaptive fuzzy control, and fault detection systems, which are critical in modern AI-driven control solutions.

Experience

With a robust foundation in control engineering, Arman Khani has engaged in multiple research projects, contributing to the advancement of intelligent control systems. Post-PhD, he has been collaborating with leading experts in the field of intelligent control and has worked extensively on the theoretical and practical applications of Type 3 fuzzy systems. His expertise spans across nonlinear control, AI-driven predictive modeling, and the development of adaptive control mechanisms for real-world applications, particularly in medical and industrial automation.

Research Interests

Arman Khani’s research interests encompass intelligent control, nonlinear system control, model predictive control, Type 3 fuzzy systems, and adaptive control strategies. His work emphasizes the development of robust control systems that are independent of traditional modeling constraints, making them highly adaptable to complex, real-world problems. A key focus of his research is the control of insulin-glucose dynamics in diabetic patients using AI-driven fuzzy control mechanisms, which have shown promising results in medical applications.

Awards

Arman Khani has been nominated for the prestigious AI Data Scientist Awards under the Best Researcher category. His pioneering work in intelligent control systems and the application of AI in nonlinear system management has gained recognition in the academic and scientific communities. His contributions to the field, particularly in the development of AI-driven medical control systems, highlight his dedication to advancing technology for societal benefit.

Publications

Arman Khani has authored multiple high-impact research papers in reputed journals. Below are some of his key publications:

Khani, A. (2023). “Application of Type 3 Fuzzy Systems in Nonlinear Control.” Journal of Intelligent Control Systems, 12(3), 45-59. Cited by 15 articles.

Khani, A. (2022). “Adaptive Model Predictive Control for Nonlinear Systems.” International Journal of Control Engineering, 29(4), 98-112. Cited by 10 articles.

Khani, A. (2021). “AI-Based Control Mechanisms for Medical Applications: A Case Study on Insulin-Glucose Dynamics.” Biomedical AI Research Journal, 7(2), 21-35. Cited by 20 articles.

Khani, A. (2020). “Advancements in Intelligent Fault Detection Systems.” Journal of Advanced Control Techniques, 18(1), 77-89. Cited by 12 articles.

Khani, A. (2019). “Type 3 Fuzzy Logic and Its Application in Robotics.” Robotics and Automation Journal, 14(3), 36-49. Cited by 8 articles.

Khani, A. (2018). “Neural Network-Based Predictive Control Systems.” Artificial Intelligence & Control Systems Journal, 10(2), 50-65. Cited by 9 articles.

Khani, A. (2017). “A Review of Nonlinear Control Strategies in Industrial Automation.” International Journal of Industrial Automation Research, 5(4), 112-127. Cited by 6 articles.

Conclusion

Arman Khani’s contributions to the field of intelligent control systems and artificial intelligence reflect his dedication to advancing knowledge and technology. His pioneering research in Type 3 fuzzy systems has opened new avenues for AI-driven control mechanisms, particularly in medical and industrial applications. Through his collaborations, publications, and ongoing research initiatives, he continues to push the boundaries of innovation in control engineering. His nomination for the AI Data Scientist Awards underscores his impact in the field, solidifying his position as a leading researcher in intelligent control and AI applications.

Anvesh Reddy Minukuri | Artificial Intelligence | Data Scientist of the Year Award

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Mr. Anvesh Reddy Minukuri | Artificial Intelligence | Data Scientist of the Year Award

Senior Lead at Jpmorgan Chase, United States

Anvesh Reddy Minukuri is a highly experienced data science and artificial intelligence professional with over twelve years of experience in IT, specializing in full-stack modeling, data mining, marketing analytics, big data, AI/ML, and visualization. With a keen focus on developing advanced AI-driven solutions, he has played a pivotal role in optimizing large-scale machine learning models, particularly in the domain of large language models (LLMs). His expertise spans across predictive modeling, customer retention frameworks, deep learning applications, and AI-driven decision-making. Currently, he serves as a Senior Lead, VP-LMM Machine Learning at JPMorgan Chase, where he is at the forefront of implementing AI-based solutions to enhance business intelligence and customer interactions.

Profile

Google Scholar

Education

Anvesh holds a Master of Science in Management Information Systems from the Spears School of Business at Oklahoma State University, where he graduated in December 2014 with a GPA of 3.82. He also earned a Bachelor of Technology in Computer Science from Jawaharlal Nehru Technological University, Hyderabad, India, in April 2011 with a GPA of 3.8. His academic background laid a strong foundation in data analytics, machine learning, and business intelligence, which have been instrumental in his career advancements.

Experience

With a career spanning over a decade, Anvesh has held key roles in leading financial and telecommunications companies. As a Senior Lead, VP at JPMorgan Chase, he has driven AI adoption by consolidating LLM architectures, optimizing Q&A retrieval systems, and integrating AI-powered analytics into financial decision-making. Prior to this, he served as a Principal Data Scientist at Comcast Corporation, where he spearheaded predictive modeling for customer segmentation, retention strategies, and AI-driven business insights. His expertise in cloud-based AI solutions, deep learning frameworks, and real-time analytics has positioned him as a thought leader in the field of AI-driven business intelligence.

Research Interest

Anvesh’s research interests lie in the domains of large-scale machine learning, AI governance, deep learning, and natural language processing. He is particularly focused on the deployment of LLMs, model interpretability, and AI-driven customer engagement strategies. His work in AI ethics and bias mitigation further demonstrates his commitment to responsible AI development. Additionally, he has contributed significantly to anomaly detection, predictive analytics, and AI model performance optimization, ensuring that AI systems remain fair, transparent, and effective.

Awards

Anvesh has received multiple recognitions for his contributions to AI and data science. His work has been acknowledged with industry awards, including commendations for excellence in AI innovation, predictive modeling impact, and contributions to AI adoption in financial services. His expertise in AI model governance and strategic AI implementation has earned him nominations in leading industry forums.

Publications

Minukuri, A. R. (2023). “Optimizing LLMs for Financial Decision Making: A Case Study on Model Governance.” Journal of AI & Finance. Cited by 25 articles.

Minukuri, A. R. (2022). “Bias Mitigation in AI-Driven Customer Retention Strategies.” International Journal of Machine Learning Applications. Cited by 18 articles.

Minukuri, A. R. (2021). “Enhancing AI Explainability: A Framework for Transparent Deep Learning Models.” Journal of Computational Intelligence. Cited by 22 articles.

Minukuri, A. R. (2020). “AI-Powered Marketing Analytics: Leveraging Predictive Models for Customer Insights.” Journal of Business Analytics and AI. Cited by 30 articles.

Minukuri, A. R. (2019). “Anomaly Detection in Financial Transactions Using Deep Learning.” Journal of Financial Data Science. Cited by 27 articles.

Minukuri, A. R. (2018). “Improving AI Efficiency through Hybrid Clustering Techniques.” Journal of Big Data and Analytics. Cited by 15 articles.

Minukuri, A. R. (2017). “Predictive Modeling for Churn Prediction in Telecom Services.” Telecommunications and Data Science Review. Cited by 20 articles.

Conclusion

Anvesh Reddy Minukuri stands out as a distinguished expert in AI and machine learning, with a strong academic foundation, extensive industry experience, and a deep commitment to AI innovation and governance. His research contributions, coupled with his leadership roles in AI strategy and development, highlight his dedication to advancing the field of artificial intelligence. With a passion for data-driven solutions and AI ethics, he continues to shape the future of AI-driven decision-making and business intelligence.