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 🏥

 

Robin Augustine | Artificial Intelligence | Excellence in Research

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Assoc. Prof. Dr. Robin Augustine | Artificial Intelligence | Excellence in Research

Associate Professor, Uppsala University, Sweden

🎓 Associate Professor Robin Augustine is a renowned expert in Medical Engineering and Microwave Technology, leading research at Uppsala University in Sweden. He heads the Microwaves in Medical Engineering Group at the Angstrom Laboratory, Department of Electrical Engineering, and serves as an Associate Editor for IET journals. His interdisciplinary work spans medical sensor development, bioelectromagnetic interactions, and innovative in-body communication technologies. Robin has collaborated globally as a visiting professor and researcher, focusing on advancements in medical engineering through impactful research projects.

Publication Profile

Scopus

Education

📚 Dr. Robin Augustine earned his Ph.D. in Electronics and Optronics Systems from Université de Paris Est Marne La Vallée, specializing in human tissue electromagnetic modeling and its implications for medical sensor design. He holds an MSc in Electronics Science with a focus on Robotics from Cochin University of Science and Technology, and a BSc in Electronics Science from Mahatma Gandhi University. His expertise is further strengthened by advanced training in Diagnostic and Therapeutic Applications of Electromagnetics from Politecnico di Torino, Italy.

Experience

💼 Robin’s career includes extensive experience as a senior lecturer and associate professor at Uppsala University, where he has been leading research in microwave applications for medical technology since 2011. He has held visiting professorships and research roles at institutions such as the Beijing Institute of Nanoenergy and Nanosystems and University Medical Center Maastricht, contributing to medical sensor innovation and orthopedic measurement systems. Robin has also worked internationally, including postdoctoral research in France, with expertise in antenna design, bioelectromagnetics, and microwave characterization.

Research Focus

🔬 Robin’s research focuses on medical engineering, bioelectromagnetics, and intra-body communication, including developing microwave-based sensors for diagnosing conditions like osteoporosis, skin cancer, and muscular atrophy. As a leader in the B-CRATOS and COMFORT projects, he explores body-centric technologies and in-body wireless communication to enhance medical diagnostics. His pioneering work addresses the integration of electromagnetic technology with healthcare, making strides in non-invasive monitoring systems.

Awards and Honours

🏆 Dr. Augustine’s impactful research has attracted numerous grants and awards, including significant EU funding for projects like PERSIMMON and DIAMPS. He has secured research funding from bodies such as the Swedish Research Council, Vinnova, and the Foundation for Strategic Research, supporting his innovative work on body communication systems and medical diagnostics. His research has earned recognition through the Swedish Excellence Grant for Young Researchers and multiple grants for advancing medical engineering solutions.

Publication Top Notes

Biphasic lithium iron oxide nanocomposites for enhancement in electromagnetic interference shielding properties

Rotation insensitive implantable wireless power transfer system for medical devices using metamaterial-polarization converter

Improving burn diagnosis in medical image retrieval from grafting burn samples using B-coefficients and the CLAHE algorithm

 

slimane arbaoui | Artificial Intelligence | Young Scientist Award

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Mr. slimane arbaoui | Artificial intellegence | Young Scientist Award

Cube-SDC team, INSA Strasbourg, University of Strasbourg , 24 Bd de la Victoire, Strasbourg, 67000, France, insa strasbourg, France

Slimane Arbaoui is a dedicated final-year Computer Science student at École Supérieure en Informatique (ESI) in Sidi Bel Abbess, Algeria, specializing in Android application development and machine learning. 🎓 His skills span Java-based Android development, data integration, and advanced problem-solving in software, alongside a versatile understanding of multiple programming languages, including Python and Kotlin. Slimane has applied his AI knowledge to impactful projects, even authoring a research paper. 📚 Known for his innovation and strong analytical skills, Slimane is passionate about tackling real-world challenges with technology.

Publication Profile

Scopus

Education

Slimane completed his State Engineering and Master’s degrees in Computer Science at ESI SBA in 2023. 🎓 His academic journey has strengthened his technical expertise and provided a foundation in both theoretical and applied computing, with a focus on machine learning, mobile app development, and web technologies.

Experience

During his internship at INSA-Strasbourg, France 🇫🇷, Slimane applied machine learning to improve battery health prediction, developing models that track and identify factors contributing to battery degradation. At CNAS in Algeria, he gained practical insights into network database applications and web app development. 💻 As a freelancer on Upwork, Slimane developed Android applications and managed web back-end services, demonstrating his versatility in real-world projects.

Research Focus

Slimane’s research interests center on artificial intelligence and machine learning, with a special focus on NLP applications, sentiment analysis, and health data prediction. 🧠 His projects include sentiment analysis and fake news detection in Arabic language datasets, alongside health management applications that leverage data-driven insights to enhance service quality. His work in battery health prediction highlights his proficiency in machine learning model development and evaluation.

Awards and Honours

Slimane holds several certifications, including Microsoft Certified: Azure Fundamentals and the Android Basics Nanodegree. 🏅 His achievements in AI include completing courses on deep learning and machine learning through Kaggle and Coursera, which demonstrate his commitment to continuous learning and professional development.

Publication Top Notes

Dual-model approach for one-shot lithium-ion battery state of health sequence prediction

SOCXAI: Leveraging CNN and SHAP Analysis for Battery SOC Estimation and Anomaly Detection

Data-driven strategy for state of health prediction and anomaly detection in lithium-ion batteries