جست‌وجوی استاد و پژوهشگر

Lisa Maria Kreusserمشاهده پروفایل

دانشیار

• Partial Differential Equations
• Machine Learning
• Data Analysis
• +۴ مورد دیگر

Dr. Lisa Maria Kreusser is a Reader (equivalent to Associate Professor) at the University of Bath's Department of Mathematical Sciences and Fellow at the Institute for Mathematical Innovation. Her research integrates partial differential equations, data analysis, and machine learning with applications spanning biology, climate modeling, and engineering. Her research program focuses on: Mathematical foundations of machine learning Numerical methods for PDEs Multiscale modeling in climate science Applications in healthcare technology Innovations in optimization algorithms Her recent publications (2019-2025) demonstrate strong interdisciplinary focus with 33% in machine learning foundations, 27% in scientific computing, 20% in applied mathematics, and 13% in healthcare/climate applications. Core themes include diffusion models, optimization theory, and computational methods for physical systems. Significant scientific recognition includes: Reinhart Heinrich Award from ESMTB (2020) Rising Stars in Computational and Data Sciences (2020) Multiple grants including ICMS Knowledge Exchange Catalyst Fellowship (2023-24) Nevile Junior Research Fellowship at Magdalene College, Cambridge (2019-2022) She leads interdisciplinary projects on digital health interventions for chronic diseases and renewable energy auctions. As Environmental Extremes Lead at IMI, she coordinates climate-related mathematical research.

Florian Bossmannمشاهده پروفایل

دانشیار

• Applied Signal Processing
• Inverse Problems
• Greedy Algorithms
• +۸ مورد دیگر

Florian Bossmann is an Associate Professor and Doctoral Supervisor at the School of Mathematics, Harbin Institute of Technology. His research focuses on applied signal processing with an emphasis on algorithm design and applications in inverse problems, sparsity, and compressed sensing. His work spans seismic exploration, ptychography, and video processing. Education: PhD (Mathematics, University of Göttingen, 2010-2013), Diploma in Mathematics (University of Duisburg-Essen, 2005-2009). Research interests include greedy methods, structured sparsity, and data denoising. His recent articles address topics like object reconstruction via K-approximation graphs and phase retrieval in ptychographic imaging. No scientific awards explicitly stated. Active in mentoring doctoral students and collaborations in applied mathematics and engineering. Labs/Teams: Engaged in interdisciplinary research groups focused on signal processing and mathematical modeling.

• Autonomous Systems
• Robotics
• Artificial Intelligence
• +۳ مورد دیگر

Rodrigo Gutiérrez Moreno is an Assistant Professor in the Department of Electronics at Universidad Autónoma de Madrid (UAM), affiliated with the Robesafe research group focused on Service Robotics and e-Safety Technologies. His work bridges robotics, autonomous systems, and AI applications in transportation and healthcare. He collaborates extensively with the CARLA simulator for autonomous driving research. Research interests include autonomous vehicle decision-making, reinforcement learning integration with classical control, motion prediction, and human-robot interaction. His group explores hybrid control systems, V2X communication validation, and ethical considerations in autonomous systems. Recent projects address lane change maneuvers, urban scenario navigation, and driver behavior analysis using gaze tracking and vehicle data. Publications span multimodal sensor fusion, graph-based prediction models, and modular autonomous driving architectures. His work emphasizes transitioning simulation-based algorithms to real-world implementations while maintaining safety and reliability standards. Active in open-source tools like ROS for robotic development, he also investigates healthcare robotics and biomedical engineering applications.

• Experimental mechanics
• Mechanical design
• Industrial automation
• +۹ مورد دیگر

Andrea Mura is an Associate Professor at the Department of Mechanical and Aerospace Engineering (DIMEAS) of the Polytechnic University of Turin. He contributes to teaching in Mechanical Engineering, Energy and Nuclear Engineering, Biomedical Engineering, and Electrical and Energy Engineering. His academic roles include being a Course Lecturer and Course Collaborator across multiple degree programs. Mura's research focuses on Experimental Mechanics , Mechanical Design , Gear Failure Analysis , and Industrial Automation . He leads projects in Resource-Constrained Systems , Crack Propagation Analysis , and Energy Harvesting , with a strong emphasis on Materials Engineering and Lightweight Construction . His publications from 2022–2025 span Mechanical Engineering , Materials Science , and Tribology . Key themes include Gear Fatigue Testing , Tribological Coatings , and Machine Learning Applications in mechanical systems. He supervises PhD student Ferdinando Ursi in Mechanical Engineering.

Radu-Lucian LUPŞAمشاهده پروفایل

مدرس

• Image Processing
• Computer Graphics
• Graph Algorithms
• +۳ مورد دیگر

Radu-Lucian LUPŞA serves as a Lecturer at Babeş-Bolyai University's Faculty of Mathematics and Computer Science in Romania. His academic career spans over two decades with a consistent focus on computer science, particularly in image processing and graph algorithms. His teaching responsibilities include courses on Graph Algorithms and Parallel and Distributed Programming, demonstrating his expertise across theoretical and applied computer science domains. LUPŞA's research interests center around image processing, with significant contributions to compression methods, dithering algorithms, and multiresolution analysis. His work bridges theoretical computer science with practical applications in graphics and healthcare optimization. The evolution of his research shows a progression from fundamental image compression techniques in the late 1990s to more specialized applications in medical imaging and transportation logistics by the late 2000s. Analysis of his publications reveals strong expertise in computational geometry, algorithm design, and optimization problems. His research demonstrates consistent application of mathematical rigor to practical computing challenges, particularly in the areas of image representation and processing. The interdisciplinary nature of his work is evident in publications spanning computer graphics, healthcare logistics, and mathematical optimization. LUPŞA completed his PhD in 2006 with the dissertation titled Contributions to the analysis, processing and representation of images , which serves as a foundation for his subsequent research trajectory. His academic output shows a period of high productivity between 1996-2008 with publications in respected venues including IEEE conferences and academic journals. As an educator, LUPŞA has developed comprehensive teaching materials for graph algorithms, including lecture notes, practical assignments, and evaluation methods. His teaching approach emphasizes both theoretical foundations and practical implementation, as evidenced by the detailed laboratory assignments and programming tasks he has designed for students.

• Biocomplexity
• Biophysics
• Computational Biology
• +۶ مورد دیگر

Julius Bier Kirkegaard is a Tenure Track Assistant Professor at the University of Copenhagen, holding dual appointments at the Niels Bohr Institute (Biocomplexity section) and the Department of Computer Science (Image Analysis, Computational Modelling and Geometry group). His work bridges theoretical physics, computational biology, and computer science, focusing on complex biological systems through quantitative approaches. Dr. Kirkegaard's research spans multiple interconnected areas in biocomplexity and biophysics. His primary interests include: Biological transport networks and their optimization Chemotaxis and cellular gradient sensing mechanisms Computational modeling of biological structures (particularly plant venation networks) Image analysis techniques for biological systems Network topology in chemical reaction systems Deep learning applications in medical imaging Analysis of Dr. Kirkegaard's recent publications reveals a strong interdisciplinary approach combining physics, biology, and computational methods. His work often focuses on how biological systems optimize information processing and transport under physical constraints. A significant theme is the study of network structures in biological contexts, from leaf venation to cellular receptor organization. His more recent work increasingly incorporates machine learning techniques, particularly deep learning, for analyzing complex biological systems and medical imaging data. Dr. Kirkegaard maintains active collaborations across multiple institutions, as evidenced by his diverse publication record spanning physics, biology, and computer science journals. His research appears to be supported by various grants, though specific funding sources aren't detailed in the provided information. Based at the University of Copenhagen, Dr. Kirkegaard works within the Biocomplexity and Biophysics research environment at the Niels Bohr Institute, which provides a collaborative setting for interdisciplinary research at the interface of physics and biology.

Ian Woodمشاهده پروفایل

مدرس

• Statistics
• Machine Learning
• Bioinformatics
• +۷ مورد دیگر

Dr. Ian Wood is a Lecturer in Statistics at the School of Mathematics and Physics within the Faculty of Science at The University of Queensland. He received his PhD from the same institution in 2004. His teaching responsibilities include instructing 4th-year advanced statistics, 1st-year pharmacy students, and postgraduate data science courses. Dr. Wood's research spans multiple interdisciplinary domains including: Statistical methodologies for classification and mixture models Machine learning algorithms and stochastic optimization techniques Bioinformatics applications in genomics and medical research Evolutionary computation and optimization frameworks His work frequently bridges theoretical statistics with practical applications in biomedicine and computational biology. Analysis of Dr. Wood's recent publications reveals three primary research trends: Advanced algorithm development for statistical modeling and optimization Applications in biomedical contexts including cancer research, tissue engineering, and pathogen genomics Methodological innovations in machine learning and computational statistics His research consistently demonstrates strong interdisciplinary collaboration across statistics, computer science, and life sciences. Dr. Wood maintains an active supervision portfolio, serving as principal advisor for PhD candidates working on: Evolutionary algorithms for variable-length problems MRI brain segmentation using Markov random fields Estimation of distribution algorithms He also serves as associate advisor for multiple doctoral and master's projects spanning statistics, optimization, and bioinformatics. Dr. Wood is affiliated with the Centre for Organic Photonics and Electronics and maintains collaborations across multiple research teams focused on statistical applications in health sciences and engineering.

Lauri Parkkonenمشاهده پروفایل

استاد

• Magnetoencephalography (MEG)
• Optically Pumped Magnetometers
• Social Neuroscience
• +۴ مورد دیگر

Lauri Parkkonen is Professor in the Department of Neuroscience and Biomedical Engineering at Aalto University. His research advances non-invasive neuroimaging through innovations in MEG instrumentation, real-time analysis, and hyperscanning techniques to study social interaction, conscious perception, and brain plasticity. Key contributions include: Development of optically-pumped magnetometer arrays for next-generation MEG Pioneering hyperscanning methods for dual-brain social neuroscience Real-time neurofeedback paradigms for plasticity and clinical applications Normative modeling approaches for traumatic brain injury detection Open-source tools for MEG/EEG analysis (MNE-Python) His group studies neural mechanisms of empathy, attention, and cross-species emotion recognition using multivariate decoding. Current projects optimize cortical parcellation for MEG connectivity analysis and develop MRI-free source imaging techniques. Honors include an ERC Starting Grant (2015) and coordination of Finland's functional brain-imaging biobank consortium.

• Electrical Engineering
• Computer Engineering
• Signal Processing
• +۷ مورد دیگر

Ming Zhu is an Assistant Professor in Residence in the Department of Electrical and Computer Engineering at the University of Nevada, Las Vegas (UNLV). His work bridges electrical engineering and computer science to solve real-world problems in transportation, industrial systems, and healthcare. His research interests include Signal Processing , Machine Learning , Unmanned Aerial Vehicles (UAVs) , and Edge Computing . He applies these to Rail Infrastructure Monitoring , Industrial Fault Detection , and Health Monitoring Systems , using technologies like LiDAR, acoustic sensors, and generative models. Analysis of recent publications reveals a focus on non-invasive rail track measurement (using UAVs and LiDAR), sound-based malfunction detection in machinery, and efficient edge computing for video streaming and healthcare applications. His work demonstrates a strong trend towards practical, real-time systems for critical infrastructure.

Xinhui Maمشاهده پروفایل

مدرس

• eXtended Reality (XR)
• Artificial Intelligence (AI)
• Digital Twin
• +۱۰ مورد دیگر

Dr. Xinhui Ma is a Lecturer in the School of Computer Science at the University of Hull, part of the Faculty of Science and Engineering. His research focuses on eXtended Reality (XR), Artificial Intelligence (AI), Digital Twin technology, and their applications in Digital Health, Green Energy, and Advanced Manufacturing. He holds a PhD from the University of Birmingham and has received the Wellcome Trust Seed Award in Science (2017). Dr. Ma teaches modules including Reality Time Graphics, 3D Computer Graphics, and Mixed Reality Development. He is a Fellow of the Higher Education Academy (2013) and serves as an EPSRC peer reviewer. Education: PhD in Computer Science from the University of Birmingham (completed). His research interests include Virtual Reality, Mixed Reality, Responsible AI, Explainable AI, Deep Learning, and CAD/CAM. Dr. Ma leads projects such as the 'Digital Twin through Physics-Informed Deep Learning for Offshore Wind Turbine Gearing Fault Diagnosis and Prognosis' (2024) and co-leads the 'Mortality Risk Prediction of ICU Patients with Sepsis' (2023). He is affiliated with the Virtual Augmented Reality and Simulation (VARS) research group. Dr. Ma supervises PhD students in Digital Twin applications, including Eamonn Tuton (Aura CDT project on offshore wind farms) and Haider Alkaraawi (Machine Learning for Cancer Care). His work spans academic collaborations with institutions like the Wolfson Foundation and the Royal Society. Key Projects: Wolfson Equipment bid for confocal imaging (2024–2029) Wellcome Trust-funded maxillofacial growth analysis (2017–2019) His contributions bridge AI, XR, and real-world challenges in healthcare, energy, and manufacturing, emphasizing ethical AI practices and interdisciplinary collaboration.

Lisa Kreusserمشاهده پروفایل

پژوهشگر

• Partial Differential Equations
• Machine Learning
• Climate Modeling
• +۳ مورد دیگر

Lisa Kreusser is a researcher affiliated with the University of Bath, holding roles in the EPSRC Centre for Doctoral Training in Statistical Applied Mathematics (SAMBa), the Centre for Climate Adaptation & Environment Research (CAER), and the Institute for Mathematical Innovation (IMI). Her research focuses on the intersection of partial differential equations, data analysis, and mathematical formulations for machine learning, with applications in climate modeling, biology, engineering, and industry. She actively leads and collaborates on projects addressing challenges such as cloud parameterization in atmospheric models, diabetic foot ulcer care through digital health technologies, and neural network-based solutions for complex systems. Her academic contributions span over 20 research outputs, emphasizing interdisciplinary approaches to nonlinear problems. Key areas include information propagation on graphs, stochastic differential equations in machine learning, and anisotropic diffusion models for pattern formation. Kreusser has secured funding for eight projects, demonstrating expertise in both theoretical and applied research. She is open to supervising doctoral students across topics in machine learning, climate science, and biological systems. Education: Advanced degrees in applied mathematics and computational science (specific details not provided). Grants: Includes projects on computational collective decision-making, weather prediction models using machine learning, and diabetic foot ulcer care technologies. Labs/Teams: Collaborates within multi-institutional teams, such as the Institute of Mathematical Innovation and CAER, driving interdisciplinary research initiatives.

David Lopez Vilariñoمشاهده پروفایل

استاد

• Computer Vision
• LiDAR Data Processing
• FPGA Acceleration
• +۴ مورد دیگر

David Lopez Vilariño is a Professor at the Department of Electronics and Computing, Faculty of Physics, University of Santiago de Compostela. He holds a PhD in Electronics from the same university (2001), specializing in active contour algorithms and FPGA implementations. His research focuses on LiDAR data processing, reconfigurable computing architectures, and embedded vision systems. He is a member of the ARQCOMP (Computer Architecture) and Artificial Vision research groups. Education: PhD in Electronics (2001), thesis on 'Active contours at the pixel level: design and implementation on cellular network architectures' under Dr. Diego Cabello Ferrer. Research interests include: LiDAR-based 3D modeling for urban and environmental applications FPGA acceleration for high-performance computing Reconfigurable hardware architectures for embedded vision systems Real-time video surveillance and motion estimation His work bridges hardware-software co-design with advanced sensing technologies, particularly in geomatics and medical imaging applications.

John Ormerodمشاهده پروفایل

دانشیار

• Statistics
• Bayesian Inference
• Data Mining
• +۴ مورد دیگر

Associate Professor John Ormerod is affiliated with the Statistics Research Group at the School of Mathematics and Statistics , University of Sydney . His research focuses on advanced statistical methodologies, including Variational Bayes , Generalized Linear Mixed Models , Splines , and Missing Data analysis. Current research students: Rajan Shankar and Jackson Zhou Key research strengths: Precision and Digital Health , Data and Decisions , National Security His work spans computational statistics, bioinformatics, and applications in health and biological data. Recent publications emphasize scalable Bayesian inference, variable selection, and single-cell data analysis frameworks. Grants include multiple Australian Research Council (ARC) Discovery Projects (2021, 2017, 2012, 2010) supporting research in feature selection, network modeling, and computational efficiency. He has supervised projects on modern regularization techniques and fast expectation propagation , contributing to BMC Bioinformatics , Bioinformatics , and Journal of Computational and Graphical Statistics .

Christian Goutمشاهده پروفایل

استاد

• Data approximation
• Artificial Intelligence
• Partial Differential Equations (PDE)
• +۶ مورد دیگر

Christian Gout is a Professor at INSA Rouen Normandie, affiliated with the Laboratoire de Mathématiques de l'INSA de Rouen Normandie. His research focuses on data approximation, AI, partial differential equations (PDEs), numerical analysis, shape optimization, and image processing, with applications in geophysics, energy, and medicine. He is an external collaborator with MAKUTU (INRIA Bordeaux Sud Ouest) and UNAM (Mexico). Director of Research and Doctoral Studies at INSA Rouen Normandie (2018–2022). Previously held roles include Director of the Mathematics Lab at INSA Rouen (2010–2017) and Professor (PR) at UVHC (2006–2010). Research interests bridge numerical methods (e.g., spectral element and discontinuous Galerkin methods) with applications in seismic imaging, wind field modeling, and medical imaging. He has supervised numerous PhD students and co-authored books like Data Approximation: Mathematical Modelling and Numerical Simulations (2019). Collaborations span industries including Dassault Systèmes, Siemens-Gamesa, and Total. His work includes developing mathematical models for wind velocity reconstruction, image segmentation, and fog computing in smart factories. He has also contributed to projects like ECOS NORD for PDE-based inverse problems in energy and geophysics.

• Harmonic Analysis
• Applied Mathematics
• Machine Learning
• +۴ مورد دیگر

Christina Frederick is an Associate Professor in the Department of Mathematical Sciences at New Jersey Institute of Technology. Her research spans computational mathematics, harmonic analysis, and machine learning applications in robotics and ocean acoustics. Active in time-frequency analysis and multiscale inverse problems Principal Investigator on NSF and Navy-funded projects Collaborates on seabed classification, intermittent diffusion algorithms, and sensor deployment Current work focuses on computational methods for autonomous systems and mathematical foundations of Riesz bases. She has contributed to robotics path planning and sonar imaging techniques. Her recent publications (2022-2024) demonstrate interdisciplinary integration of harmonic analysis with machine learning for applications in seabed classification, mobile sensor deployment, and robotics coordination. Key trends include time-frequency analysis, computational methods for autonomous systems, and Gaussian processes in acoustics. NSF Grant: Applied and computational time-frequency analysis (2023-2026) Navy Grant: Sensor deployment algorithms (2021-present) NSF Grant: Multiscale inverse problems in sonar imaging (2017-2020) Frederick's work bridges theoretical mathematics with practical engineering challenges, particularly in sensor network optimization and acoustic signal processing.