جست‌وجوی استاد و پژوهشگر

Işın Ererمشاهده پروفایل

استاد

• Signal Processing
• Radar Systems
• Ground Penetrating Radar
• +۱۷ مورد دیگر

Işın Erer is a Professor at the Department of Electronics and Communication Engineering, Faculty of Electrical and Electronic Engineering, Istanbul Technical University (ITU). She is actively involved in research on radar signal processing, artificial intelligence, and image analysis, with a focus on ground-penetrating radar (GPR) and remote sensing applications. University: Istanbul Technical University School: Faculty of Electrical and Electronic Engineering Department: Department of Electronics and Communication Engineering Academic Rank: Professor Email: ierer@itu.edu.tr Her research interests span signal processing, radar systems, clutter removal, target detection, deep learning, vision transformers, U-Nets, and vital signs detection using stepped-frequency radar . She applies advanced machine learning techniques to enhance radar imaging and improve performance in challenging environments such as debris fields and outdoor conditions. The recent publication trends indicate a strong focus on integrating deep learning models (e.g., Vision Transformers, YOLOv5, U-Net) with radar signal processing for clutter removal, image restoration, and segmentation . Her work combines low-rank approximations, autoencoders, B-spline activation functions, and attention mechanisms to improve accuracy and robustness in GPR and remote sensing imagery. Applications include parcel boundary delineation, road segmentation, and life detection in search-and-rescue scenarios. She has received notable recognition for her academic mentorship: Best PhD Thesis Advisor in Telecommunications Engineering Program, 2018 She leads multiple active research projects funded by TÜBİTAK and ITU-BAP, focusing on real-time AI-based radar systems, through-wall vital sign detection, and clutter removal in GPR. She has supervised numerous graduate students, with 47 theses in progress or completed. Her research group works on both theoretical algorithm development and practical system implementation, bridging the gap between academia and real-world deployment. Current projects include developing integrated AI models for real-time GPR systems and ultra-wideband radar methods for behind-obstacle detection.

• Numerical Analysis
• High-Performance Computing
• Computational Mathematics
• +۴ مورد دیگر

Maciej Woźniak serves as a university professor at the Institute of Computer Science within the Faculty of Computer Science at AGH University of Science and Technology in Kraków, actively contributing to research and teaching staff while participating in the Computer Science Discipline Council and College of the Faculty. His office is located at D-17, ul. Kawiory 21, III, 4.58, with primary contact via macwozni@agh.edu.pl. His research spans computational mathematics and high-performance computing, specializing in parallel algorithms for numerical simulations including isogeometric analysis, finite element methods, and multi-frontal solvers. Key applications address environmental challenges like hail suppression and urban smog reduction, alongside biomedical modeling of tumor growth and airborne pathogen dispersion. Methodological innovations focus on GPU acceleration, shared-memory architectures, and efficient integration techniques for partial differential equations. Analysis of his 2019-2024 publications reveals consistent advancement in parallel computational frameworks for environmental and biomedical applications, demonstrating interdisciplinary impact through experimental validation of hail cannons, smog reduction technologies, and pathogen dispersion modeling during the COVID-19 pandemic. His work bridges theoretical algorithm development with real-world problem solving across physics, engineering, and life sciences. Scientific Awards: No specific awards were documented in the provided sources. Student advising, research grants, laboratory affiliations, and educational background details beyond his PhD, DSc, and Eng. titles remain unspecified in the available materials, though his extensive publication record suggests active mentorship and collaborative research leadership.

Knut Martin Mørkenمشاهده پروفایل

استاد

• Approximation Theory
• Numerical Analysis
• Spline Functions
• +۴ مورد دیگر

Knut Martin Mørken is a Professor and Vice Dean for Education at the Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Oslo. His academic career spans both research and leadership in computational mathematics and science education reform. Roles: Vice Dean of Education (since 2019), Leader of the CCSE (Centre for Computing in Science Education), Former leader of the InterAct educational development project (2012–2019) Teaching: Co-instructor of MAT-INF1100 and INF-MAT5340 courses Research Interests: Approximation theory with focus on spline functions, B-splines, wavelets, and geometric modeling Integration of computational methods into mathematics and science education, spanning the CSE (Computers in Science Education) project and its successor, the CCSE (now a national Centre of Excellence in Education) Scientific Contributions: His work includes algorithm development for function approximation, stability analysis of wavelets on nonuniform triangulations, and interdisciplinary studies on mathematics-computing integration in education. Awards & Recognition: CCSE awarded Centre of Excellence in Education (SFU) by DIKU Collaborations: Active partnerships across departments and institutions, including collaborations with Lyche, Reif, Lockwood, Caballero, and Melvær in research and educational initiatives.

KÜBRA UYARمشاهده پروفایل

مدرس

• Artificial Intelligence
• Computer Vision
• Medical Image Analysis
• +۲ مورد دیگر

KÜBRA UYAR is a Lecturer at Alanya Alaaddin Keykubat University's Rafet Kayış Faculty of Engineering, Department of Computer Engineering. She previously worked as a Research Assistant at Selçuk University's Faculty of Technology (2022-2023) and continues her academic contributions in image processing and artificial intelligence. PhD in Computer Engineering (2022), Selçuk University MSc in Computer Engineering (2017), Selçuk University BSc in Computer Engineering (2014), Melikşah University Double Major in Mathematics (2014), Melikşah University Her research focuses on Artificial Intelligence , Computer Vision , and Medical Image Analysis , with a strong emphasis on Machine Learning and Image Processing . Current work includes developing explainable AI models for retinopathy diagnosis and optimizing CNN architectures for biomedical applications. Recent publications highlight trends in Deep Learning for agriculture (chestnut classification), Medical Imaging (retinopathy, leukocyte detection), and Optimization (CNN hyperparameters, B-spline algorithms). Her work bridges Computer Science with Healthcare and Industrial Applications . Key projects include the Data-Intensive and Computer Vision Research Laboratory Infrastructure Project (2020-2021) and the Selçuk University Weather Monitoring System (2018-2020), both funded by higher education institutions.

Griffith Boyceمشاهده پروفایل

استاد

• Computational mathematics
• Applied mathematics
• Cardiovascular physiology
• +۴ مورد دیگر

Griffith Boyce is a Professor of Mathematics and Biomedical Engineering at the University of North Carolina at Chapel Hill (UNC), with an adjunct appointment in Applied Physical Sciences. He serves as Associate Chair for Research in the Department of Mathematics and leads the Cardiovascular Modeling and Simulation Laboratory. His affiliations include the Carolina Center for Interdisciplinary Applied Mathematics, Computational Medicine Program, and McAllister Heart Institute at UNC School of Medicine. Dr. Griffith earned his BA and BS in Mathematics from Rice University (2000), followed by a PhD in Mathematics from New York University (2005). He completed postdoctoral training at NYU (2005-2008) before joining NYU as faculty (2008-2014), then UNC in 2014. His research focuses on developing computational tools to predict cardiovascular physiology and pathology through fluid-structure interaction (FSI) modeling. Key applications include cardiac hemodynamics, bioprosthetic heart valves, ventricular assist devices, and thrombosis in atrial fibrillation. His group advances the immersed boundary (IB) method and maintains the IBAMR software infrastructure for adaptive mesh refinement in FSI problems. Recent publications highlight innovations in divergence-free numerical methods, viscoelastic flow modeling, and patient-specific cardiovascular simulations. His team's work spans from fundamental FSI algorithms to clinical translation in medical device design. Dr. Griffith collaborates with clinicians, engineers, and computational scientists across academia, industry, and regulatory agencies. His lab includes PhD alumni like Marshall Davey, Charles Puelz, Aaron Barrett, and Cole Gruninger, alongside current researchers. The Cardiovascular Modeling and Simulation Laboratory develops open-source software and interdisciplinary frameworks for predictive biomedical simulations. Their research integrates mathematical modeling, high-performance computing, and experimental validation to address cardiovascular disease mechanisms and therapy optimization.

Floris Ernstمشاهده پروفایل

استاد

• Signal processing and analysis for biomedical applications
• Sensors for robotics
• Motion tracking and recognition in medical technology
• +۴ مورد دیگر

Prof. Floris Ernst serves as Professor of Medical Robotics at the Institute for Robotics and Cognitive Systems, University of Lübeck, where he has been faculty since 2017 after joining as a research associate in 2013. He holds significant leadership roles including membership on the Steering Committee of the Graduate School 'Computing in Medicine and Life Sciences' and editorial positions with IEEE Robotics and Automation Letters and Frontiers in Robotics and AI. His research spans medical robotics , signal processing for biomedical applications , sensors for robotics , and augmented reality in surgery . Key projects include SonoBox (robotic ultrasound for pediatric fracture diagnosis), TWIN-WIN (digital supertwin technology), and robotics applications in rescue medicine. His work consistently bridges theoretical algorithm development with clinical implementation, focusing on real-world medical challenges. Prof. Ernst's recent publications (2023-2025) demonstrate strong activity across medical imaging, rescue robotics, and navigation systems. Trends show increasing focus on deep learning applications in medical imaging, real-time motion tracking for radiosurgery, and autonomous systems for emergency response. His work frequently appears in top robotics and medical imaging venues including IEEE conferences and journals. IEEE Senior Member Associate Editor, IEEE Robotics and Automation Letters (Medical Robotics) Associate Editor, Frontiers in Robotics and AI (Biomedical Robotics) As an active supervisor, Prof. Ernst guides students through courses like Medical Robotics (CS4270) and Bachelor/Master projects, with numerous publications co-authored with students. His lab maintains strong industry and clinical collaborations, particularly in medical device development and clinical robotics applications. The Robotics Laboratory (RobLab) serves as the primary research environment for his team's work on medical and rescue robotics systems.