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 🏥

 

Ching-Lung Fan | Deep Learning | Best Researcher Award

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Assoc. Prof. Dr. Ching-Lung Fan | Deep Learning | Best Researcher Award

Associate Professor, ROC Military Academy, Taiwan

Ching-Lung Fan is an associate professor in Civil Engineering at the Republic of China Military Academy. He completed his Ph.D. in 2019 from the National Kaohsiung University of Science and Technology. His professional journey reflects a strong dedication to advancing technology in the construction and civil engineering sectors, particularly through the application of machine learning and deep learning methods. 🏫

Publication Profile

Education

Dr. Fan holds a Master of Science (M.S.) from National Taiwan University (2006) and a Ph.D. from National Kaohsiung University of Science and Technology (2019). His academic background underscores his commitment to both theoretical and practical contributions to the field. 🎓

Experience

Dr. Fan started his academic career as an assistant professor at the Republic of China Military Academy in January 2019 and was promoted to associate professor in June 2022. His teaching and research experience has significantly impacted the study of civil engineering, especially through the integration of machine learning and data mining. 🏢

Awards and Honors

Ching-Lung Fan has received several prestigious awards, including the Phi Tau Phi Scholastic Honor (2019), Outstanding Paper Award (2021), Excellent Paper Award (2022), and Best Researcher Award (2024). In 2023, he was honored with membership in Sigma Xi, an esteemed scientific organization. 🏅

Research Focus

Dr. Fan’s research interests are primarily centered around machine learning, deep learning, data mining, construction performance evaluation, and risk management. His work integrates cutting-edge computational methods with civil engineering applications to enhance the quality and efficiency of construction projects. 🤖📊

Conclusion

Dr. Fan’s innovative contributions to civil engineering, particularly in the realm of AI-driven solutions, continue to shape the future of construction and infrastructure development. His ongoing research and recognition in the academic community highlight his expertise and impact in the field. 🌟

Publications

 Integrating image processing technology and deep learning to identify crops in UAV orthoimages. CMC-Computers, Materials & Continua. (Accepted).

Predicting the construction quality of projects by using hybrid soft computing techniques. CMES-Computer Modeling in Engineering & Sciences. (Accepted).

 Evaluation model for crack detection with deep learning—Improved confusion matrix based on linear features. Journal of Construction Engineering and Management (ASCE), 151(3): 04024210. (SCI).

 Evaluating the performance of Taiwan airport renovation projects: An application of multiple attributes intelligent decision analysis. Buildings, 14(10): 3314. (SCI).

Deep neural networks for automated damage classification in image-based visual data of reinforced concrete structures. Heliyon, 10(19): e38104. (SCI).

Multiscale feature extraction by using convolutional neural network: Extraction of objects from multiresolution images of urban areas. ISPRS International Journal of GeoInformation, 13(1): 5. (SCI).

Ground surface structure classification using UAV remote sensing images and machine learning algorithms. Applied Geomatics, 15: 919-931. (ESCI).

 Using convolutional neural networks to identify illegal roofs from unmanned aerial vehicle images. Architectural Engineering and Design Management, 20(2): 390-410. (SCI).

Evaluation of machine learning in recognizing images of reinforced concrete damage. Multimedia Tools and Applications, 82: 30221-30246. (SCI).

 Supervised machine learning–Based detection of concrete efflorescence. Symmetry, 14(11): 284. (SCI).