Dr. Zeynep Ilkilic Aytac | Artificial Intelligence | Best Researcher Award

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Dr. Zeynep Ilkilic Aytac | Artificial Intelligence | Best Researcher Award

Dr Lecturer, Ondokuzmayıs University, Turkey

Dr. Zeynep Ilkilic Aytac is a dynamic and innovative academician serving as a Lecturer at Ondokuz Mayıs University, Yeşilyurt Demir Çelik Vocational School, Department of Mechatronics 🏫. With over eight years of teaching experience, she has contributed significantly to interdisciplinary research that merges mechatronics, artificial intelligence 🤖, and sustainable technologies 🌱. Her strong academic foundation and passion for practical innovation enable her to mentor engineering students while advancing the frontiers of medical diagnostics and control systems. She is widely recognized for her work in MEMS gyroscope control, CNN-based cancer detection, and emission modeling using AI.

Publication Profile

🎓 Education Background

Dr. Aytac earned her BSc, MSc, and PhD degrees in Mechatronics Engineering from Fırat University, Turkey . Her academic journey showcases a strong foundation in mechanical-electrical integration, AI-driven design, and intelligent control systems. Her doctoral research focused on developing robust control strategies for MEMS gyroscopes, laying the groundwork for her multifaceted research career.

💼 Professional Experience

Currently a Lecturer at Ondokuz Mayıs University, Dr. Aytac brings over eight years of higher education teaching and project supervision experience. She has led various academic initiatives and research projects that combine engineering principles with AI and sustainability 🌐. Her interdisciplinary projects have strengthened both academic and industry collaborations, reflecting her commitment to applied research and impactful innovation.

🏅 Awards and Honors

Dr. Aytac has gained recognition for her research through publication in reputable international journals and conference proceedings 🏆. Although specific awards are not listed, her extensive interdisciplinary contributions and active role in innovation-driven education suggest an academic career marked by peer respect and institutional acknowledgment.

🔬 Research Focus

Her research interests lie in the robust control of MEMS gyroscopes, artificial intelligence in medical imaging 🧠, and emission prediction from internal combustion systems using neural networks. She has also focused on CNN-based thyroid cancer detection, leveraging hybrid metaheuristic optimization algorithms like COOT, GWO, PSO, and CMA-ES. Her contributions uniquely combine mechatronics, control theory, deep learning, and sustainability for real-world applications across engineering and healthcare.

🧩 Conclusion

Dr. Zeynep Ilkilic Aytac exemplifies the spirit of modern engineering innovation—bridging theoretical knowledge with hands-on impact. Her work continues to shape the convergence of control systems, AI, and biomedical diagnostics, enriching both academic fields and practical industries 🔧🧬. Through dedicated teaching, collaborative research, and a commitment to sustainable technology, she inspires the next generation of engineers and scientists.

📚 Top Publications 

AI-Based Emission Prediction Using Artificial Neural Networks Optimized by CMA-ES Algorithm.
Journal: Energy Reports, Year: 2022
Cited by: 24 articles

Robust Control of MEMS Gyroscopes Using Adaptive Sliding Mode Techniques.
Journal: Microsystem Technologies, Year: 2021
Cited by: 17 articles

Deep CNN Optimization for Thyroid Cancer Detection Using GWO and PSO.
Journal: Sensors, Year: 2023
Cited by: 12 articles

Hybrid AI Approaches in Digital Pathology: A CNN-Based Study.
Journal: IEEE Access, Year: 2022
Cited by: 9 articles

 Metaheuristic Optimization in CNNs for Histopathological Image Classification.
Journal: Expert Systems with Applications, Year: 2023
Cited by: 7 articles

Dr. Aiai Wang | Machine Learning | Best Researcher Award

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Dr. Aiai Wang | Machine Learning | Best Researcher Award

Doctoral student, University of Science and Technology Beijing, China

Ai-Ai Wang is a passionate and dedicated young researcher born in March 1998 in Langfang, Hebei Province, China. A proud member of the Communist Party of China (CPC), she is currently based at the University of Science and Technology Beijing (USTB), where she serves as the Secretary of the 16th Party Branch, 4 Zhaizhai. With a solid academic foundation in mining and civil engineering, Ai-Ai has excelled in both academic and research spheres, contributing significantly to digital and intelligent mining technologies. Her work emphasizes physical dynamics in tailings sand cementation and filling, showing strong potential for innovation in sustainable mining practices.

Publication Profile

Scopus

🎓Education Background:

Ai-Ai Wang completed her Bachelor of Science in Mining Engineering from North China University of Science and Technology in 2021. She further pursued her Master’s degree in Civil Engineering at the University of Science and Technology Beijing (2021.09–2024.06), affiliated with the School of Civil and Resource Engineering.

🛠️Professional Experience:

Alongside her academic journey, Ai-Ai has undertaken significant responsibilities, currently serving as Secretary of the Party Branch at USTB. Her leadership extends beyond administration into collaborative research projects, software development, and patent contributions under renowned mentors such as Prof. Cao Shuai. She has played vital roles in developing intelligent systems for mining operations, reinforcing her multidisciplinary strengths.

🏅Awards and Honors:

Ai-Ai Wang has been recognized extensively for her academic and research excellence. Notable accolades include the “Top Ten Academic Stars” at USTB (2023), a National Scholarship for Master’s Degree Students (2022), the prestigious Taishan Iron and Steel Scholarship (2023), and multiple First-Class Academic Scholarships from USTB. She was twice named an Outstanding Three-Good Graduate Student and honored by her school as an outstanding individual. Moreover, she has received scientific awards such as the First Prize from the China Gold Association and the Second Prize from the China Nonferrous Metals Industry for her impactful contributions to green and safe mining.

🔬Research Focus:

Ai-Ai Wang’s research is rooted in advanced techniques of tailings sand cementation, intelligent filling systems, and digital mining. She explores the structural stability of backfills, application of nanomaterials, and CT-based 3D modeling of internal structures. Her work blends civil engineering, environmental safety, and digital innovation, aiming to enhance sustainability and efficiency in modern mining. She also contributes to cutting-edge software systems and patented technologies for mining design and operation support.

📝Conclusion:

Ai-Ai Wang stands out as a promising engineer and researcher whose academic achievements, professional dedication, and innovative research in intelligent mining set a high standard for future civil and mining engineers. Her trajectory reflects not just technical mastery but a deep commitment to sustainable and smart engineering solutions in the mining industry.

📚Top Publications with Details

Effect of height to diameter ratio on dynamic characteristics of cemented tailings backfills with fiber reinforcement through impact loading – Construction and Building Materials, 2022
Cited by: 26 articles
Influence of types and contents of nano cellulose materials as reinforcement on stability performance of cementitious tailings backfill – Construction and Building Materials, 2022
Cited by: 20 articles
Quantitative analysis of pore characteristics of nanocellulose reinforced cementitious tailings fills using 3D reconstruction of CT images – Journal of Materials Research and Technology, 2023
Cited by: 12 articles

 

Ulas Bagci | Artificial Intelligence | Outstanding Scientist Award

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Assoc. Prof. Dr. Ulas Bagci | Artificial Intelligence | Outstanding Scientist Award

Assoc. Prof., Northwestern University, United States

Dr. Ulas Bagci is a distinguished researcher and tenured Associate Professor at Northwestern University, specializing in Radiology, Electrical and Computer Engineering, and Biomedical Engineering. He is also a courtesy professor at the University of Central Florida’s Center for Research in Computer Vision. As the Director of the Machine and Hybrid Intelligence Lab, Dr. Bagci focuses on the integration of artificial intelligence, deep learning, and medical imaging. His extensive research contributions include over 330 peer-reviewed articles in these domains. Previously, he was a staff scientist and lab co-manager at the National Institutes of Health (NIH), where he played a pivotal role in advancing AI-driven medical imaging applications. Dr. Bagci actively contributes to leading scientific journals, serving as an associate editor for IEEE Transactions on Medical Imaging, Medical Physics, and Medical Image Analysis.

Publication Profile

🎓 Education

Dr. Ulas Bagci holds a Ph.D. in Computer Science from the University of Nottingham (2010), where he conducted pioneering research in medical imaging. He was a Visiting Research Fellow in Radiology at the University of Pennsylvania (2008-2009), further refining his expertise in AI applications for biomedical sciences. He earned his M.Sc. in Electrical and Computer Engineering from Koç University (2005) and his B.Sc. in Electrical and Computer Engineering from Bilkent University (2003).

💼 Experience

Dr. Bagci has built an impressive academic and research career across top institutions. Since 2021, he has been an Associate Professor at Northwestern University, where he leads research in AI-driven medical imaging. Before that, he served as an Assistant Professor in Computer Science at the University of Central Florida (2014-2020), fostering innovation in deep learning for radiology. From 2010 to 2014, he was a Staff Scientist and Lab Manager at the National Institutes of Health (NIH), playing a key role in infectious disease imaging and AI applications in radiology.

🏅 Awards and Honors

Dr. Bagci has received numerous recognitions for his outstanding contributions to artificial intelligence and medical imaging. He has secured multiple NIH grants (R01, U01, R15, R21, R03) as a Principal Investigator and is a steering committee member for the NIH Artificial Intelligence Resource (AIR). Additionally, he has been honored with best paper and reviewer awards in top-tier AI and medical imaging conferences such as MICCAI and IEEE Medical Imaging.

🔬 Research Focus

Dr. Bagci’s research revolves around artificial intelligence, deep learning, radiology, and computer vision. His work has significantly impacted medical imaging applications, including MRI, CT scans, nuclear medicine imaging, and disease diagnosis. He has contributed extensively to federated learning, probabilistic modeling, and AI-powered decision-making in healthcare. His recent studies include advancements in brain tumor segmentation, bias field correction in MRI, and AI-driven road network prediction.

🔚 Conclusion

Dr. Ulas Bagci is a leading expert in AI-powered medical imaging, consistently pushing the boundaries of deep learning, radiology, and computer vision. His impactful contributions in academia and research have earned him global recognition. With a strong presence in prestigious institutions, his pioneering work continues to shape the future of AI in healthcare. 🚀

📚 Publications

Evidential Federated Learning for Skin Lesion Image Classification (2025) – Published in a book chapter DOI: 10.1007/978-3-031-78110-0_23 📖

Paradoxical Response to Neoadjuvant Therapy in Undifferentiated Pleomorphic Sarcoma (2025) – Published in Cancers DOI: 10.3390/cancers17050830 🏥

Foundational Artificial Intelligence Models and Modern Medical Practice (2025) – Published in BJR | Artificial Intelligence DOI: 10.1093/bjrai/ubae018 🧠

A Probabilistic Hadamard U-Net for MRI Bias Field Correction (2024) – Published in arXiv arXiv:2403.05024 🖥️

AI-Powered Road Network Prediction with Fused Low-Resolution Satellite Imagery and GPS Trajectory (2024) – Published in Earth Science Informatics 🌍

Beyond Self-Attention: Deformable Large Kernel Attention for Medical Image Segmentation (2024) – Presented at the IEEE/CVF Winter Conference on Applications of Computer Vision 🤖

Brain Tumor Segmentation (BraTS) Challenge 2024: Meningioma Radiotherapy Planning Automated Segmentation  (2024) – Published in arXiv arXiv:2405.18383 🏥

 

Constantina Kopitsa | Computer Science | Best Researcher Award

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Ms. Constantina Kopitsa | Computer Science | Best Researcher Award

PhD Student, University of Ioannina, Greece

📜 Kopitsa Konstantina Panagiota is a dedicated Municipal Police Specialist Pre-Investigative Officer in Marathon, Greece. With extensive experience in public administration and security, she has served in various roles across municipal police, prisons, and administrative offices. Passionate about leveraging technology for societal betterment, she is currently pursuing research in artificial intelligence and its role in disaster management. 🚓💻🌍

Publication Profile

ORCID

Education

🎓 Konstantina’s academic journey is rich and diverse. She is a Ph.D. candidate in IT and Telecommunications at the University of Ioannina, exploring artificial intelligence in natural disaster management. 🧠🌪️ She holds an M.Sc. in Analysis and Management of Man-Made and Natural Disasters from Democritus University of Thrace, with a thesis on AI’s role in disaster management. She has further enriched her learning with certifications from prestigious institutions, including Harvard EDX, UN CC: Learn, IBM, and the Hellenic National Center for Public Administration. 🌟

Experience

💼 Konstantina has an impressive career spanning over two decades. Currently serving in the Municipal Police of Marathon, she specializes in pre-investigative procedures. She has previously worked at Korydallos Prison as a Prison Officer and held administrative and security roles at various organizations, including the Independent Personal Data Protection Authority and Brink’s Hermes Aviation Security. Her diverse roles reflect her adaptability and commitment to public service. 👮‍♀️📊

Research Interests

🔍 Konstantina is passionate about the intersection of technology and disaster resilience. Her research interests include the application of artificial intelligence in natural disaster management, climate change adaptation, and nature-based solutions for disaster risk reduction. 🌱🤖

Awards

🏆 While no specific awards were listed, Konstantina’s continuous pursuit of professional development and her significant contributions to public administration and disaster management showcase her commitment to excellence. 🌟

Publications

Predicting the Duration of Forest Fires Using Machine Learning MethodsFuture Internet

2024-10-28 | journal-article

DOI: 10.3390/fi16110396