Professor Ewaryst Rafajłowicz is affiliated with the Faculty of Information and Communication Technology at Wrocław University of Science and Technology, serving as Head of the Department of Control Systems and Mechatronics. His research spans control systems, pattern recognition, image processing, and spatio-temporal dynamics, with recent work focusing on iterative learning control, signal classification, and vibration analysis in industrial systems. Control systems System identification Pattern recognition Image processing Statistical process control Mechatronic control Recent publications highlight his expertise in functional data analysis, Bernstein polynomial applications, and shape-preserving descriptors for classification tasks. He has contributed to both theoretical advancements in control algorithms and practical implementations in industrial settings, particularly in vibration damping and mechatronic systems.
Yair Censor is a distinguished Professor in the Department of Mathematics at the University of Haifa. His work focuses on applied mathematics, particularly in optimization, projection methods, and computational algorithms with applications in medical physics and engineering. He has developed foundational techniques like the superiorization method, which balances feasibility-seeking and objective function reduction in iterative algorithms. Research Interests: Optimization theory, convex feasibility problems, superiorization, medical imaging algorithms, and parallel computing. Collaborations: Extensive work with Gabor T. Herman, Aviv Gibali, Reinhard Schulte, and others in computational methods for radiation therapy and tomography. His recent publications (2023–2025) emphasize feasibility-seeking algorithms, superiorization in floorplanning, and perturbation resilience. While no explicit awards or students are listed in the provided text, his contributions to iterative methods and medical physics remain influential.
Mahdi Boloursaz Mashhadi is a researcher at Imperial College London's Department of Electrical and Electronic Engineering, affiliated with the Information Processing and Communications Lab. He holds a Ph.D. in Electrical Engineering from Sharif University of Technology (2018), with prior research roles at the University of Central Florida and Queen's University. His expertise spans signal processing, wireless communications, machine learning applications in communication systems, and biomedical signal processing. Dr. Mashhadi's research focuses on massive MIMO channel state acquisition , deep learning-driven pilot design , and semantic communication frameworks . His recent work explores token-domain multiple access, generative AI integration in communication systems, and federated learning optimizations. He has contributed to foundational studies in sparse signal reconstruction (e.g., iterative adaptive thresholding methods) and wearable health monitoring via PPG signals. Key achievements : Best Paper Award at EWDTS 2012, multiple grants (IEEE, national/regional), and patents (e.g., US Patent 9729160). Current projects include semantic-aware power allocation in generative communications and latency optimizations in distributed deep learning frameworks. Labs/Teams : Member of the Intelligent Systems and Networks (ISN) group at Imperial, collaborating on AI-driven communication systems and edge computing solutions. His work bridges theoretical signal processing with practical implementations in 5G/6G networks, biomedical devices, and distributed machine learning ecosystems.
Dr. Richard Boardman is a Principal Enterprise Fellow at the University of Southampton, affiliated with the Engineering Materials and Surface Engineering Group. His research focuses on advanced imaging techniques, materials science, and geotechnical engineering, with applications in biomedical research, soil mechanics, and computational methods. He leads projects involving X-ray tomography, photon counting detectors, and GPU-accelerated algorithms. Boardman collaborates on interdisciplinary initiatives, such as the TIGRE toolbox and 3D X-ray histology facilities. His recent work includes optimizing X-ray imaging for soil analysis, developing directional dark-field imaging, and enhancing metrology through fast tomographic measurements. He has supervised PhD student Alexander Edward Leatherland and contributed to over a dozen peer-reviewed publications since 2020. Boardman’s expertise spans experimental and computational approaches to material characterization, with a strong emphasis on practical applications in engineering and healthcare.
İsa Yıldırım is an Associate Professor in the Department of Electronics and Communication Engineering at Istanbul Technical University (ITU) , Faculty of Electrical and Electronics Engineering. He earned his PhD from the University of Illinois at Chicago in 2009, following MSc and BSc degrees from ITU in 2004 and 2002, respectively. He has been a full-time academic at ITU since 2012, advancing from Assistant to Associate Professor in 2015. Education: PhD, University of Illinois at Chicago, 2009 MSc, Istanbul Technical University, 2004 BSc, Istanbul Technical University, 2002 His research centers on biomedical imaging , signal and image processing , and deep learning , with a focus on medical image reconstruction techniques. His work applies advanced computational methods to improve imaging in digital breast tomosynthesis and low-dose CT. He has led multiple research projects funded by TUBITAK and BAP, focusing on non-convex optimization, total variation regularization, and compressed sensing. His recent publications (2022–2024) demonstrate a strong trend in integrating deep learning with model-based reconstruction , particularly in self-supervised and unsupervised frameworks for low-dose CT. His work also extends into robotics , specifically air-ground robot localization, indicating interdisciplinary collaboration. Scientific Awards: PhD Scholarship, Presidency of the Board of Higher Education, 2004 He actively mentors graduate students, having supervised 29 theses. He serves as Principal Investigator on ongoing projects, including Patient-Adapted Digital Breast Tomosynthesis Design (TUBITAK, 2023–2026). His research combines theoretical innovation with clinical applicability, particularly in reducing radiation exposure while enhancing diagnostic image quality. Labs and Research Teams: While specific lab names are not mentioned, his projects suggest leadership in a research group focused on Medical Image Reconstruction and Signal Processing , likely involving graduate students and collaborators in biomedical engineering and computer science.
PD Dr. Frank Hettlich is a Senior Lecturer at the Department of Mathematics , Karlsruhe Institute of Technology (KIT), Germany. He is affiliated with the Institute for Applied and Numerical Mathematics and leads research in inverse problems, numerical methods for partial differential equations, and wave scattering phenomena. Current Faculty of Mathematics member at KIT Active in teaching, including courses like Optimization Theory and Higher Mathematics for engineering disciplines His research focuses on: Theory and numerical approximation of acoustic, electromagnetic, and elastic wave scattering Domain derivatives for PDEs Integral equation methods Weak formulations for numerical analysis He has contributed to iterative regularization schemes for nonlinear ill-posed problems and obstacle reconstruction techniques in inverse problems. Scientific Awards : AM Teaching Prize 2020 (Mechanical Engineering)
Prof. Dr. Andreas Rieder is a Professor at the Institute for Applied and Numerical Mathematics of Karlsruher Institut für Technologie (KIT). He leads projects C1 and C2 within the CRC 1173 Wave Phenomena - Analysis and Numerics and serves on the advisory panel of the journal Inverse Problems . His office is located at Kollegiengebäude Mathematik (20.30) 3.040, Karlsruhe. Research Interests Inverse and Ill-posed Problems Numerical Analysis for Imaging Wavelet Methods in Signal Processing Partial Differential Equations His recent work focuses on: Full Waveform Inversion in visco-acoustic/viscoelastic regimes Generalized Radon Transforms for Seismic Imaging Microlocal Analysis of Migration Formulas Inexact Newton Regularization Techniques Tangential Cone Conditions for Wave Operators Key affiliations: Member of CRC 1173 Wave Phenomena Leader of Research Group 3: Scientific Computing Contributor to interdisciplinary projects with geophysics and medical imaging
Dr. Hamed Kalhori is a Lecturer in the School of Mechanical and Mechatronic Engineering at the University of Technology Sydney (UTS), Faculty of Engineering & Information Technology. As a Casual Academic, he contributes to teaching and research in mechanical engineering with a focus on dynamics, vibrations, and structural health monitoring. His work bridges theoretical and practical applications in civil infrastructure, aerospace systems, and composite materials. Dr. Kalhori earned his PhD in Mechanical Engineering from the University of Sydney between March 2013 and July 2017. Dr. Kalhori's research spans multiple domains within mechanical and structural engineering. His primary expertise lies in inverse dynamics problems , particularly impact force identification, where he develops advanced methodologies combining model-based approaches with machine learning techniques. He extensively investigates linear and nonlinear vibrations in various structures, from micro-plates to large-scale bridges. His work in structural health monitoring focuses on innovative approaches for bridge inspection, including drive-by assessment techniques and sensor network optimization. Additionally, he explores smart materials and structures , particularly carbon nanotube-reinforced composites and their mechanical behavior under dynamic loading conditions. Dr. Kalhori's publication record demonstrates a strong trajectory in mechanical engineering research with increasing focus on interdisciplinary approaches. His recent work shows growing integration of machine learning techniques with traditional mechanical engineering methods, particularly for solving inverse problems in structural dynamics. There's a clear progression from fundamental vibration analysis toward practical applications in infrastructure monitoring and composite material characterization. His research maintains strong connections between theoretical modeling and experimental validation across diverse structural systems. Dr. Kalhori has received notable recognition for his academic contributions: 2019 Dean's Award for Excellence in Teaching and Tutoring Multiple research grants in vibration-related fields Dr. Kalhori has secured research funding for projects related to vibration analysis and structural monitoring, including industry collaborations such as the Alstom DMU traincar crash test project (2025). He has led international research collaborations and conducted numerous workshops and short courses both locally and internationally. While the specific details of his advising relationships aren't provided in the available information, his extensive publication record and research leadership suggest active engagement with graduate students and early-career researchers. His research involves collaboration with the Centre for Autonomous Systems at UTS and Data61-CSIRO, where he previously worked as a research associate and research assistant respectively. These collaborations focus on structural health monitoring projects across New South Wales, leveraging advanced sensing technologies and computational methods for infrastructure assessment.
Olga Mula is a researcher at Eindhoven University of Technology (TU Eindhoven) in the Netherlands specializing in optimal transport theory, Wasserstein spaces, and model reduction techniques for partial differential equations. Her work bridges theoretical mathematics with practical applications in state estimation and inverse problems. Her research interests focus on Optimal Transport , Wasserstein Spaces , Model Reduction , and Numerical Analysis of PDEs . She develops algorithms for state estimation in metric spaces, particularly focusing on the Wasserstein space of probability measures. Her work includes developing reduced models using barycentric approximation, analyzing convergence properties, and addressing challenges in sensor placement for optimal data acquisition. Her recent publications demonstrate significant contributions to understanding how to build efficient reduced models in Wasserstein spaces for both forward and inverse problems. She has developed theoretical frameworks for state estimation algorithms, analyzed their performance in terms of Kolmogorov widths, and created practical implementations using sparse Wasserstein barycenters. Her work spans pure mathematical theory to applications in image processing and PDE-constrained optimization. Her scientific contributions include: Development of piecewise-affine algorithms for state estimation Nonlinear model reduction on metric spaces for conservative PDEs Sparse approximation using Wasserstein barycenters Applications to shape reconstruction and line completion Theoretical analysis of approximation rates in Wasserstein spaces She collaborates extensively with leading researchers in the field including Cohen, Dahmen, Feydy, and Rai. Her work demonstrates both theoretical depth and practical relevance, connecting abstract mathematical concepts to real-world problems in data assimilation and inverse modeling.
Müjdat Çetin is a Professor of Electrical and Computer Engineering and serves as the Robin and Tim Wentworth Director of the Goergen Institute for Data Science and Director of the New York State Center of Excellence in Data Science at the University of Rochester. He previously held faculty positions at Sabancı University and was a Research Scientist at MIT, with visiting roles at Boston University, Northeastern University, and MIT. Education: PhD in Electrical Engineering, Boston University, 2001 MS in Electrical Engineering, University of Salford, 1995 BS in Electrical Engineering, Boğaziçi University, 1993 His research lies at the intersection of signal processing, machine learning, and data science, with applications in biomedical imaging, radar, and brain-computer interfaces. He develops probabilistic and deep learning models for robust information extraction from noisy and complex data. His work emphasizes computational imaging, sparse representations, and multimodal data fusion. The recent publications reflect a strong trend toward integrating Bayesian methods and deep learning in imaging sciences, particularly in medical image reconstruction, neuroimaging analysis, and radar systems. His group actively explores transformer architectures, federated learning, and model-based deep learning for solving inverse problems in imaging. Scientific Awards and Honors: IEEE Fellow IEEE Signal Processing Society Best Paper Award IET Radar, Sonar and Navigation Premium Award Elsevier Signal Processing Best Paper Award Turkish Academy of Sciences Distinguished Young Scientist Award (GEBİP) ODTÜ Mustafa Parlar Foundation Research Incentive Award TÜBİTAK Career Award Boston University Best Engineering Research Award Professor Cetin has advised numerous PhD and Master’s students and led significant research grants in data science and imaging. He has served as a Senior Area Editor for IEEE Transactions on Image Processing and IEEE Transactions on Computational Imaging, and held editorial roles in several top journals. He has chaired major conferences including ICASSP, ICIP, and IVMSP workshops. He leads a multidisciplinary research group focused on data science and imaging, collaborating with neuroscientists and medical researchers. The team develops novel algorithms for brain-computer interfaces, medical image analysis, and remote sensing systems, often integrating machine learning with physical models of data acquisition.
Dr.-Ing. Norbert Hosters is a Research Associate and Chief Engineer at the Chair for Computational Analysis of Technical Systems (CATS), Faculty of Mechanical Engineering, RWTH Aachen University. He has been active since 2020 and serves as General Secretary of the German Association for Computational Mechanics (GACM). His work bridges advanced computational methods with engineering applications. His research focuses on numerical methods for fluid-structure interaction , computational fluid and structural dynamics , isogeometric analysis , and aerothermoelasticity . He also explores applied quantum methods and physics-informed neural networks for solving complex PDEs and optimizing industrial processes. His interdisciplinary work spans mechanical, biomedical, and computational engineering. The recent publications (2023–2025) demonstrate a strong trend toward integrating machine learning with traditional simulation techniques, particularly in partitioned FSI , multiphase flow , shape optimization , and biomedical simulations such as LVAD modeling. His work appears in high-impact journals like Scientific Reports , Computers & Fluids , and International Journal for Numerical Methods in Engineering , as well as major conferences including GACM and CMBE. He is actively involved in teaching courses on Numerical Methods for Fluid-Structure Interaction , Isogeometric Analysis , and Simulation Methods in Mechanical Engineering . He offers student projects and supervises research, though no named advisees are listed. He has no listed scientific awards or fellowships. His research is conducted within the CATS chair, a leading group in computational mechanics, contributing to both fundamental methods and industrial applications. He plays a key role in academic service through GACM leadership.
Vicente Fco Candela Pomares is an Associate Professor in the Department of Mathematics at the Faculty of Mathematics, University of Valencia, Spain. His academic career has been centered on numerical analysis and computational mathematics, with a focus on iterative methods for nonlinear equations and multiresolution techniques. His research interests lie primarily in Numerical Analysis , especially iterative root-finding methods such as Halley, Chebyshev, and Steffensen-type algorithms. He has contributed significantly to the convergence analysis of these methods, particularly in Banach spaces and for ill-conditioned problems. His work extends to multiresolution analysis , wavelets , and image restoration , where he applies fractional regularization and nonlinear approximation frameworks. The trends in his recent publications show a sustained focus on derivative-free iterative methods , convergence theory , and applications in image processing . His work often bridges theoretical numerical analysis with practical computational challenges. He earned his PhD from the University of Valencia in 1988 under the supervision of Dr. Antonio Marquina Vila, with a thesis on a priori error estimators for iterative methods. He has collaborated extensively with researchers including Sergio Amat, Sonia Busquier, and Rosa Peris. Notable co-authors include Pantaleón D. Romero and Francesc Aràndiga. His publications appear in high-quality journals such as Journal of Computational and Applied Mathematics , Applied Mathematics and Computation , and SIAM journals. He is actively affiliated with the University of Valencia, as evidenced by his institutional email and ongoing publications. There is no indication of part-time status, retirement, or awards in the available data.
Flavio SARTORETTO is an Associate Professor in Scientific Computing at Ca' Foscari University of Venice. He holds a Mathematics degree from the University of Padua and has held academic positions at University of Padua (1982-1992) and Sapienza University of Rome (1992-1993) before joining Ca' Foscari in 1993. His research focuses on numerical analysis, computational methods, and interdisciplinary applications including environmental modeling, cognitive processes, and assistive technologies. Key research areas include numerical solutions of PDEs, meshless methods, EEG signal analysis, and e-learning tools for impaired individuals. He has participated in major research projects such as EC Network (1992-1995), PRIN initiatives (1997-2010), and contributed to software development for geomechanical models and air quality systems. His work spans computational fluid dynamics, robotics applications, and cognitive studies. Recent publications highlight advancements in mesh refinement strategies, robotic assistive devices, and spatial cognition research. He has reviewed for prestigious journals and served in academic committees for state examinations and international conferences. He maintains active involvement in academic service, including roles in evaluation committees and contributions to professional societies like SIAM and CICAP.
Bijoy Kumar Kundu is an Associate Professor in the Department of Radiology and Medical Imaging and the Department of Biomedical Engineering at the University of Virginia. He holds a PhD in Physics from the Bhabha Atomic Research Center, India, and completed postdoctoral work at the Indian Institute of Technology and UVA. His primary affiliation is within the School of Medicine, specifically in the Cardiovascular Research Center. Dr. Kundu's research focuses on developing novel PET imaging techniques to study metabolic and cardiovascular processes. Key areas include quantifying myocardial glucose and fatty acid metabolism in rodent models of heart disease, optimizing PET imaging for human brain and breast lesions, and applying machine learning for image analysis. His lab is funded by NIH grants, collaborating with institutions like the University of Texas and Spain’s Oncovision Inc. His work addresses metabolic remodeling in hypertension-induced left ventricular hypertrophy, brain abnormalities via FDG PET, and breast lesion segmentation using neural networks. Notable projects include noninvasive detection of early metabolic changes in cardiac diseases and advancements in PET quantification methods to correct partial volume and spill-over effects.
Frank Lagor is an Associate Professor in the Department of Mechanical and Aerospace Engineering at the University of Virginia. He joined UVA in 2023 after serving as faculty at the State University of New York at Buffalo. His research focuses on estimation and control for autonomous systems interacting with fluid environments, including gust mitigation in aerospace systems and bio-inspired flow sensing for robotics. Dr. Lagor holds a Ph.D. in Aerospace Engineering from the University of Maryland (2017), with prior degrees from the University of Pennsylvania (M.S., 2009) and Villanova University (B.S., 2006). Before academia, he worked at Lockheed Martin as a Certified Principal Engineer for satellite solar array systems. His research interests emphasize unsteady flow estimation, optimal maneuver design in gust encounters, and reduced-order modeling techniques. Key contributions include sensor placement strategies for data-driven flow estimation and closed-loop control methodologies for autonomous underwater vehicles. He has received prestigious awards including the AFOSR Young Investigator Award (2021) and UB SEAS Early Career Teacher of the Year (2020). Courses taught include advanced control systems theory, dynamics, and stochastic estimation methods.
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