Mehnaz Tabassum | Computer Science and Artificial Intelligence | Women Researcher Award

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Dr. Mehnaz Tabassum | Computer Science and Artificial Intelligence | Women Researcher Award

University of Sydney | Australia

Dr. Mehnaz Tabassum is an accomplished researcher in Computational Neurosurgery and Health Innovation, with core expertise in medical image analysis, artificial intelligence, and brain tumor diagnostics. Her research integrates deep learning, radiomics, and neuroimaging to enhance the precision of tumor segmentation, classification, and recurrence prediction in neuro-oncology. Her scholarly contributions include 14 Scopus-indexed publications, with a total of 87 citations and an h-index of 4 (Scopus metrics). She has published in prestigious journals such as Cancers, European Radiology, and Neuro-Oncology Advances, and has presented her findings at leading international conferences including IEEE EMBC and IEEE ISBI. Dr. Mehnaz Tabassum’s recent research explores cross-modality medical image synthesis, MRI-to-PET generation using diffusion and GAN-based models, and meta transfer learning for brain tumor segmentation. Her innovative work advances computational solutions for precision medicine and AI-assisted neuroimaging. She has received multiple distinctions, including the Pro-Vice Chancellor’s Research Excellence Scholarship and the Henry Sutton Postgraduate Research Scholarship, alongside a Best Paper Award for excellence in scientific contribution. Her interdisciplinary research continues to impact the fields of AI-driven diagnostics, eye-tracking in medical imaging, and computational modeling for neurosurgical innovation, reflecting her commitment to advancing data-driven healthcare and translational neuroscience.

Profiles: Scopus | Google Scholar | ORCID | ResearchGate | Staff Page

Featured Publications

  • Tabassum, M., Suman, A. A., Suero Molina, E., Pan, E., Di Ieva, A., & Liu, S. (2023). Radiomics and machine learning in brain tumors and their habitat: A systematic review. Cancers, 15(8), Article 2034. https://doi.org/10.3390/cancers15082034

  • Ghose, P., Alavi, M., Tabassum, M., Ashraf Uddin, M., Biswas, M., Mahbub, K., … & Hassan, M. (2022). Detecting COVID-19 infection status from chest X-ray and CT scan via single transfer learning-driven approach. Frontiers in Genetics, 13, 980338. https://doi.org/10.3389/fgene.2022.980338

  • Moradizeyveh, S., Tabassum, M., Liu, S., Newport, R. A., Beheshti, A., & Di Ieva, A. (2024). When eye-tracking meets machine learning: A systematic review on applications in medical image analysis. arXiv preprint arXiv:2403.07834. https://arxiv.org/abs/2403.07834

  • Tabassum, M., Suman, A. A., Russo, C., Di Ieva, A., & Liu, S. (2023). A deep learning framework for skull stripping in brain MRI. Neurocomputing (Under review).

  • Afrin, F., Al-Amin, M., & Tabassum, M. (2015). Comparative performance of using PCA with K-means and fuzzy C means clustering for customer segmentation. International Journal of Scientific and Technology Research, 4(8), 70–74.

Yongyi Yan | Computer Science and Artificial Intelligence | Best Researcher Award

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Prof. Dr. Yongyi Yan | Computer Science and Artificial Intelligence | Best Researcher Award

Henan University of Science and Technology | China

Prof. Dr. Yongyi Yan, Professor of Control Science at Henan University of Science and Technology, has devoted over 15 years to advancing robust control and intelligent optimization for industrial and autonomous systems, demonstrating a rare blend of theoretical excellence and practical impact. He has authored 65 publications, including influential articles in IEEE Transactions on Automation Science and Engineering and SCIENCE CHINA Information Sciences, accumulating 506 citations with an h-index of 10, reflecting his work’s high recognition and application in both academia and industry. Professor Yan has led multiple National Natural Science Foundation of China projects (U1804150, 62073124, 12571584), supervised three PhD graduates, and served as associate editor for Control Engineering Practice. His pioneering contributions include an adaptive robust control algorithm that reduces oscillation by 40% in high-precision machining and an AI-driven optimization framework (patents ZL2021 1 0419210.4 and ZL202110418903.1) that decreases energy consumption by 18% in automotive assembly lines, now adopted by leading manufacturers. His collaborations with Luoyang Sanwu Cable Group have resulted in significant breakthroughs in high-conductivity aluminum stranded wire cable production, integrating multi-machine coordination, dynamic compensation, and laser-guided visual positioning to enhance efficiency, product quality, and equipment control. By bridging fundamental research with industrial applications, Professor Yan has advanced smart manufacturing, autonomous navigation, and process optimization. His sustained innovation, leadership, and scholarly achievements exemplify research excellence, making him highly deserving of the Best Researcher Award for transformative contributions to control science and engineering.

Profile: Scopus

Featured Publications

  • Li, X., Yan, Y., Yue, J., & Zhang, S. (2025, September). Algebraic insight into universal logic functions and implications for logical system modeling.

  • An, Z., Yan, Y., Yue, J., & Li, X. (2025, May). Construction of automaton observer based on matrix semi-tensor product. In Conference proceedings.

  • Wang, X., Yan, Y., Yue, J., & An, Z. (2025, May). Construction and synchronization analysis of state power set automata based on algebraic methods. In Conference proceedings.

  • Dong, C., Yan, Y., Li, H., & Yue, J. (2024, November). Semi-tensor product approach to controllability, reachability, and stabilizability of extended finite state machines.

  • Zhang, S., Yan, Y., Hao, P., & Yue, J. (2024, October). Structural simplification of finite state machines using pruning operators based on semi-tensor product of matrices. In Conference proceedings.

  • Zhang, S., Yan, Y., Wang, C., & Yue, J. (2024, October). Implementation of automaton product networks: From formal language to algebraic models. In Conference proceedings.

  • Yan, Y., Hao, P., Yue, J., & Feng, J. (2024, October). An STP look at logical blocking of finite state machines: Formulation, detection, and search.

  • Yan, Y., Dong, C., Li, H., & Yue, J. (2024, July). Algebraic implementation of extended finite state machine networks.

Wenbo Shen | Computer Science and Artificial Intelligence | Best Researcher Award

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Assoc. Prof. Dr. Wenbo Shen | Computer Science and Artificial Intelligence | Best Researcher Award

Zhejiang University | China

Dr. Wenbo Shen is a tenured Associate Professor at the College of Computer Science and Technology, Zhejiang University. He is a highly regarded researcher in computer software and system protection, with expertise in program analysis, operating system security, and energy-efficient system defense. With a career spanning academia and industry, Dr. Shen has established himself as an innovative scholar whose work has advanced the field of computer security on a global scale.

Professional Profile

Scopus

Google Scholar

ORCID

Education

Dr. Wenbo Shen earned his Ph.D. in Computer Science from North Carolina State University, where his dissertation focused on wireless security and physical layer signal design under the supervision of distinguished professors. Prior to this, he obtained his Bachelor of Science degree in Software Engineering from the Harbin Institute of Technology, graduating in the top tier of his class. This solid academic foundation has shaped his excellence in research and teaching.

Experience

Dr. Wenbo Shen’s professional journey reflects a balance of academic leadership and industrial innovation. He began his career as a Senior Research Scientist at Samsung Research America, where he contributed to advanced security solutions for operating systems. He later joined Zhejiang University, first as an Assistant Professor and then progressing to a tenured Associate Professor. His teaching and supervision experience has nurtured the next generation of scholars, while his leadership in funded research projects has strengthened collaborations between academia and industry.

Research Interest

Dr. Wenbo Shen’s research focuses on protecting computer software and systems through advanced program analysis techniques. His contributions span detecting software bugs in major operating systems such as Linux, FreeBSD, and Fuchsia, while also developing lightweight protection mechanisms for mobile systems, containerized environments, and edge computing. He is particularly known for creating resource- and energy-friendly defense strategies, bridging theoretical innovation with practical application in real-world computing systems.

Awards

Dr. Wenbo Shen has been recognized with multiple Distinguished Paper Awards from top international security conferences, including the European Symposium on Research in Computer Security, the Annual Computer Security Applications Conference, ACM Asia Conference on Computer and Communications Security, and the Network and Distributed System Security Symposium. These honors highlight the significance and impact of his research contributions in advancing secure computing.

Publication

Dr. Wenbo Shen has contributed extensively to high-impact journals and conferences in computer security, with his works widely cited by international researchers.

  • Title: Hypervision across worlds: Real-time kernel protection from the arm trustzone secure world
    Published on: 2014
    Citation: 400

  • Title: SKEE: A lightweight Secure Kernel-level Execution Environment for ARM
    Published on: 2016
    Citation: 126

  • Title: Ally friendly jamming: How to jam your enemy and maintain your own wireless connectivity at the same time
    Published on: 2013
    Citation: 101

  • Title: {PeX}: A permission check analysis framework for linux kernel
    Published on: 2019
    Citation: 98

  • Title: Is link signature dependable for wireless security?
    Published on: 2013
    Citation: 75

  • Title: Ptrix: Efficient hardware-assisted fuzzing for cots binary
    Published on: 2019
    Citation: 68

  • Title: NORAX: Enabling execute-only memory for COTS binaries on AArch64
    Published on: 2017
    Citation: 65

Conclusion

Dr. Wenbo Shen’s career reflects excellence in scholarship, innovation in security research, and dedication to advancing computer system protection. His pioneering contributions in operating system defense, his recognition through prestigious international awards, and his consistent high-impact publications make him a deserving nominee for the Best Researcher Award. His work continues to influence the field globally, shaping both academic research and practical applications in secure computing.

Jiajie Gao | Computer Science | Best Researcher Award

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Mr. Jiajie Gao | Computer Science | Best Researcher Award

Hebei University of Architecture | China

Gao Jiajie is a highly driven and innovative researcher currently pursuing a master’s degree at Hebei University of Architecture. In just two years, he has demonstrated exceptional research prowess by independently publishing three SCI Q3 journal articles, authoring 16 software patents, and leading a national-level project. His commitment to bridging theoretical AI innovations with real-world applications places him at the forefront of emerging research talent in occupational safety and intelligent detection systems.

Professional profile👤

Scopus

Strengths for the Awards✨

Gao Jiajie demonstrates exceptional research performance, particularly notable given his status as a master’s student. His achievements include publishing three SCI Q3 journal articles, securing 16 authorized software patents, and acting as principal investigator on a national-level project—a feat typically rare at this early career stage. His research focuses on applied artificial intelligence in safety-critical systems, such as railway worker compliance, power grid integrity, and smart retail. Gao’s innovation in improving YOLOv5-based detection systems—including integrating CBAM and MPDIoU modules—shows deep technical insight and a solutions-oriented mindset. Moreover, his ability to translate research into real-world industrial collaborations speaks to his applied impact, with outputs in railway safety, insulator fault detection, and retail inventory management. His role as a reviewer for a peer-reviewed journal further highlights his growing reputation in the academic community. 🔍

🎓 Education

Gao Jiajie is presently a master’s student at Hebei University of Architecture, where he is actively involved in cutting-edge research in artificial intelligence and its applications in safety systems. His academic journey is marked by a strong focus on practical innovations and scholarly contributions, showcasing an excellent balance between theory and implementation.

💼 Experience

In his short academic career, Gao Jiajie has already led one national-level project as the principal investigator and served as a key contributor in multiple collaborative initiatives. His leadership resulted in eight authorized software patents and several AI-based detection systems. He also actively participates in academic review processes as a reviewer for the Journal of Electronic Imaging (JEI).

🧠 Research Interest On Computer Science

Gao’s research centers on Artificial Intelligence, particularly in the development of advanced detection algorithms that enhance occupational safety and industrial automation. His innovative approach integrates attention mechanisms (CBAM), improved loss functions (MPDIoU), and real-time object detection models (YOLOv5/YOLOv8), delivering significant accuracy improvements in challenging environments such as railways, power grids, and retail sectors.

🏅 Awards

While still early in his academic journey, Gao Jiajie’s work has garnered national recognition. His role as Principal Investigator of a national-level project, along with his multiple patents and peer-reviewed publications, highlight his merit for the Best Researcher Award. His work stands as a testament to innovation, application, and academic excellence. 🏆

📚 Publications

Gao Jiajie has published three peer-reviewed SCI Q3 journal articles, each making notable contributions to AI-based visual detection systems:

  1. Gao, Jiajie, et al. Safety equipment compliance analysis for occupational safety, Signal, Image and Video Processing, Vol. 19, Article 720, 2025.
    📈 Cited by: Article recently published; citation data forthcoming.

  2. Gao, Jiajie, et al. Enhanced YOLOv8 for high-precision retail cabinet product recognition, Signal, Image and Video Processing, Vol. 19.7, 2025.
    📈 Cited by: 2 articles.

  3. Gao, Jiajie, et al. Research on the algorithm of detecting insulators in high-voltage transmission lines using UAV images, Signal, Image and Video Processing, Vol. 18, Suppl 1, 2024.
    📈 Cited by: 5 articles.

These publications emphasize Gao’s skill in applying deep learning techniques to complex detection challenges in infrastructure and retail environments.

✅ Conclusion

Gao Jiajie exemplifies the qualities of a forward-thinking researcher, blending deep technical knowledge with practical implementation in AI-based safety systems. His rapid output of publications, patents, and leadership in a national project underscores a rare level of maturity, innovation, and commitment at an early academic stage. With outstanding contributions in detection algorithm design, academic reviewing, and cross-sector collaboration, Gao is a deserving nominee for the Best Researcher Award.

Gokalp Çınarer | Artificial Intelligence | Best Researcher Award

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Assist. Prof. Dr. Gokalp Çınarer | Artificial İntelligence | Best Researcher Award

Yozgat Bozok University Computer Engineering Department | Turkey

Dr. Gökalp Çınarer is an Assistant Professor in the Department of Computer Engineering at Yozgat Bozok University. His expertise lies in artificial intelligence, machine learning, image processing, and deep neural networks. Over the years, he has contributed significantly to the academic community with applications of AI in diverse fields, including medicine, agriculture, food technologies, and environmental analysis.

Professional profile👤

Google Scholar

ORCID

Scopus

Strengths for the Awards✨

  • Diverse Research Portfolio: Dr. Çınarer has made significant contributions across multiple domains — medicine, agriculture, food technologies, and environmental analysis — leveraging artificial intelligence and machine learning, showcasing versatility and cross-disciplinary impact.
  • High Research Output: With over 50 publications indexed in SCI-Expanded, SSCI, and Scopus, his research output is impressive, indicating a consistent contribution to advancing knowledge in computer engineering and AI applications.
  • Innovative Work in Medical Imaging: His PhD thesis on brain tumor detection through image processing and classification algorithms reflects a critical application of AI in medical diagnostics, directly contributing to healthcare advancements.
  • Academic Leadership: As Head of the Department of Information Processing and Computer Engineering Software at Yozgat Bozok University, Dr. Çınarer plays a pivotal role in shaping academic programs, guiding research initiatives, and mentoring students.
  • Teaching Excellence: He teaches a broad range of courses, including Machine Learning, Deep Learning, and Artificial Intelligence Applications, fostering the next generation of AI researchers and practitioners.

🎓 Education

Dr. Çınarer earned his PhD in Computer Engineering from Kırıkkale University between 2017 and 2021. His doctoral research focused on “Detection of Brain Tumors with Image Processing Techniques and Analysis with Classification Algorithms.”

💼 Experience

Since 2021, Dr. Çınarer has been serving as the Head of the Department of Information Processing and the Head of the Department of Computer Engineering Software at Yozgat Bozok University. He teaches undergraduate and graduate courses, including Machine Learning, Deep Learning, Python Programming, Algorithm Analysis, and Artificial Intelligence Applications.

🔬 Research Interests On Artificial Intelligence

Dr. Çınarer’s research interests encompass artificial intelligence, machine learning, image processing, and deep neural networks. His work delves into AI applications across various fields such as medicine, agriculture, food technologies, and environmental analysis.

🏆 Awards

Dr. Çınarer has been recognized for his pioneering work in artificial intelligence and its applications across multiple disciplines, earning accolades for his contributions to AI-driven medical analysis and agricultural technologies.

📚 Publications

  • Classification of brain tumors by machine learning algorithms
    Authors: G Çınarer, BG Emiroğlu
    Year: 2019
    Citations: 74

  • Prediction of Glioma Grades Using Deep Learning with Wavelet Radiomic Features
    Authors: G Çınarer, BG Emiroğlu, AH Yurttakal
    Year: 2020
    Citations: 57

  • Öğretmenlerin Teknolojik Araçlarla Eğitime Yönelik Tutumlarının Çeşitli Değişkenlere Göre İncelenmesi Yozgat İli Örneği
    Author: G Çınarer
    Year: 2016
    Citations: 27

  • Classification of hazelnuts according to their quality using deep learning algorithms
    Authors: N Erbaş, G Çınarer, K Kiliç
    Year: 2022
    Citations: 21

  • A comparative study on segmentation and classification in breast mri imaging
    Authors: AH Yurttakal, H Erbay, T İkizceli, S Karacavus, G Çinarer
    Year: 2018
    Citations: 21

  • Brain Tumor Classification Using Deep Neural Network
    Authors: G Çınarer, BG Emiroğlu, RS Arslan, AH Yurttakal
    Year: 2020
    Citations: 16

  • Application of various machine learning algorithms in view of predicting the CO2 emissions in the transportation sector
    Authors: G Çınarer, MK Yeşilyurt, Ü Ağbulut, Z Yılbaşı, K Kılıç
    Year: 2024
    Citations: 11

  • Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation: Proceedings of the INFUS 2021 Conference
    Authors: G Çınarer, C Kahraman, S Cebi, SC Onar, B Oztaysi, AC Tolga, IU Sari
    Year: 2021
    Citations: 9

  • Classification of Diabetic Rat Histopathology Images Using Convolutional Neural Networks
    Authors: AH Yurttakal, H Erbay, G Çınarer, H Baş
    Year: 2021
    Citations: 8

  • A Deep Transfer Learning Framework For The Staging Of Diabetic Retinopathy
    Authors: G Çınarer, K Kılıç, T Parlar
    Year: 2022
    Citations: 6

📄 Conclusion

Dr. Gökalp Çınarer stands at the forefront of AI research, leveraging machine learning and image processing to solve complex problems in medicine, agriculture, and environmental science. His work continues to inspire advancements in interdisciplinary fields and pave the way for future innovations.

Dong Yang | Technologies | Best Researcher Award

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Prof. Dong Yang | Technologies | Best Researcher Award

Dalian Institute of Chemical Physics Chinese Academy of Sciences | China

Dong Yang is currently a professor at the Dalian Institute of Chemical Physics, Chinese Academy of Sciences. He also serves as the Deputy Director of the State Key Laboratory of Photoelectric Conversion and Utilization of Solar Energy. His research focuses on advancing solar energy technologies, particularly in perovskite photovoltaics and renewable energy applications.

Profile👤

Google Scholar

Strengths for the Awards✨

  • Extensive Research Experience – Professor Dong Yang has a well-rounded academic and research background, spanning China and the United States, with positions at Dalian Institute of Chemical Physics (CAS), Virginia Tech, and Pennsylvania State University.
  • Leadership & Innovation – As a Deputy Director of a State Key Laboratory, he plays a pivotal role in advancing solar energy research, which is highly impactful in the field of sustainable energy.
  • Cutting-Edge Research – His work in perovskite photovoltaics, flexible electronics, tandem solar cells, and renewable energy harvesters is at the forefront of energy research, addressing critical global challenges.
  • Industrial Applications – His research is not just theoretical but has strong potential for real-world applications, bridging the gap between academia and industry.
  • International Collaborations – Having worked in China and the United States, he brings a global perspective to research, fostering international partnerships.

🎓 Education

Dong Yang earned his Ph.D. in Physical Chemistry from the Dalian Institute of Chemical Physics in 2014. His academic journey has been instrumental in shaping his expertise in solar energy conversion and flexible electronics.

🏆 Experience

Following his Ph.D., Dr. Yang expanded his research through postdoctoral positions at Shaanxi Normal University in China and Virginia Tech in the United States. From 2018 to 2022, he served as an Assistant Research Professor at Pennsylvania State University, contributing significantly to solar cell innovation. Currently, he leads a research group specializing in next-generation photovoltaics and energy-harvesting technologies.

🔬 Research Interests On Technologies

Dr. Yang’s research interests include perovskite photovoltaics, flexible electronics, tandem solar cells, and renewable energy devices. His work aims to enhance the efficiency, stability, and industrial scalability of solar energy solutions.

🏅 Awards

Dr. Yang has received several prestigious awards for his contributions to solar energy research. His innovations in perovskite solar cells and flexible electronics have been recognized internationally.

📚 Publications

Dr. Yang has authored numerous high-impact publications in esteemed journals. Below are some of his notable works:

  • High efficiency planar-type perovskite solar cells with negligible hysteresis using EDTA-complexed SnO2

    • Authors: D Yang, R Yang, K Wang, C Wu, X Zhu, J Feng, X Ren, G Fang, S Priya, …
    • Year: 2018
    • Citations: 1358

  • Surface optimization to eliminate hysteresis for record efficiency planar perovskite solar cells

    • Authors: D Yang, X Zhou, R Yang, Z Yang, W Yu, X Wang, C Li, SF Liu, …
    • Year: 2016
    • Citations: 993

  • Stable high efficiency two-dimensional perovskite solar cells via cesium doping

    • Authors: X Zhang, X Ren, B Liu, R Munir, X Zhu, D Yang, J Li, Y Liu, DM Smilgies, …
    • Year: 2017
    • Citations: 668

  • High efficiency flexible perovskite solar cells using superior low temperature TiO2

    • Authors: D Yang, R Yang, J Zhang, Z Yang, SF Liu, C Li
    • Year: 2015
    • Citations: 579

  • Record efficiency stable flexible perovskite solar cell using effective additive assistant strategy

    • Authors: J Feng, X Zhu, Z Yang, X Zhang, J Niu, Z Wang, S Zuo, S Priya, S Liu, …
    • Year: 2018
    • Citations: 490

  • Hysteresis‐suppressed high‐efficiency flexible perovskite solar cells using solid‐state ionic‐liquids for effective electron transport

    • Authors: D Yang, R Yang, X Ren, X Zhu, Z Yang, C Li, S Liu
    • Year: 2016
    • Citations: 436

  • 20‐mm‐Large single‐crystalline formamidinium‐perovskite wafer for mass production of integrated photodetectors

    • Authors: Y Liu, J Sun, Z Yang, D Yang, X Ren, H Xu, Z Yang, S Liu
    • Year: 2016
    • Citations: 390

  • Recent advances in flexible perovskite solar cells: fabrication and applications

    • Authors: D Yang, R Yang, S Priya, S Liu
    • Year: 2019
    • Citations: 378

  • Thinness-and shape-controlled growth for ultrathin single-crystalline perovskite wafers for mass production of superior photoelectronic devices

    • Authors: Y Liu, Y Zhang, Z Yang, D Yang, X Ren, L Pang, SF Liu
    • Year: 2016
    • Citations: 373

  • Solution-Processed Nb:SnO2 Electron Transport Layer for Efficient Planar Perovskite Solar Cells

    • Authors: X Ren, D Yang, Z Yang, J Feng, X Zhu, J Niu, Y Liu, W Zhao, SF Liu
    • Year: 2017
    • Citations: 356

🔚 Conclusion

Dr. Dong Yang is a distinguished researcher in solar energy and flexible electronics. His pioneering work continues to drive advancements in photovoltaic technology, bridging the gap between scientific discovery and industrial application.