جستجوی استاد راهنمای دانشگاهی

Charalampos Tsoumpasمشاهده پروفایل

استاد

• Quantitative Positron Emission Tomography (PET)
• Single Photon Emission Computed Tomography (SPECT)
• Medical Imaging
• +۴ مورد دیگر

Prof. Charalampos Tsoumpas is a Full Professor in Quantification in Molecular Diagnostics & Radionuclide Therapy at the University Medical Centre Groningen (UMCG), affiliated with the Faculty of Medical Sciences. His research focuses on advancing quantitative PET and SPECT imaging, including novel reconstruction algorithms and motion correction techniques. He holds academic roles including team leader in the Crystal Clear Collaboration (CERN), editorial board memberships in journals like European Journal of Nuclear Medicine , and leadership in initiatives such as the Collaborative Computational Project Synergistic Reconstruction in Biomedical Imaging (CCP SyneRBI) . Education includes a BSc in Physics (National University of Athens), MSc in Biomedical Engineering (National Technical University of Athens), and a PhD from Imperial College London. His career includes roles at King’s College London and the University of Leeds, where he led research projects funded by the Leverhulme Trust, EPSRC, and EU grants. Research Highlights: Pioneered methods for parametric image reconstruction, developed open-source STIR software, and contributed to PET-MRI integration. His work on motion correction and scatter correction has influenced commercial systems. Notable achievements include the Rotblat Medal (2017) for impactful research in medical physics. Leadership and Mentorship: Supervised 23 PhD students, fostering open science through collaborative coding and educational resources. Maintained industrial partnerships with companies like GE Healthcare and Bruker. Current projects include the SAFIR PET-MRI preclinical scanner and LAFOV PET/CT advancements. Labs/Teams: Leads the Quantification in Molecular Diagnostics lab at UMCG, collaborates on the SAFIR project (ETH Zurich), and contributes to CCP SyneRBI for synergistic reconstruction frameworks.

Mark Andrew Postمشاهده پروفایل

مدرس ارشد

• Robotics
• Autonomous Systems
• Space Robotics
• +۷ مورد دیگر

Dr. Mark A. Post is a Senior Lecturer in Robotics and Autonomous Systems at the University of York's School of Physics, Engineering and Technology. He has over 61 peer-reviewed publications and £1M+ in research funding as PI, including Horizon 2020 and Horizon Europe grants. His work spans space robotics , autonomous systems , and bio-inspired designs . B.A.Sc. Electrical Engineering (University of Toronto, 2004) M.Sc. Space Science (York University, 2008) Ph.D. Space Engineering (York University, 2014) Research focuses on autonomous planetary rovers , nanosatellites , and bio-inspired robots with robust probabilistic reasoning, modular sensor fusion, and adaptive control systems. Key projects include: STARFAB : Space warehouse ecosystem (Horizon Europe, 2024–2026) MOSAR : Modular satellite reconfiguration (Horizon 2020, €3.999M) RoboFish : Underwater inspection AUV (Supergen ORE Hub funding) Awarded three EU grants and multiple STFC/Innovate UK projects, he supervises PhD students in modular robotics , probabilistic programming , and autonomous systems .

Noirrit Kiran Chandraمشاهده پروفایل

استادیار

• Graphical models
• Clinical trial design
• Model-based clustering
• +۵ مورد دیگر

Noirrit Kiran Chandra is an Assistant Professor of Statistics at the Department of Mathematical Sciences within the School of Natural Sciences and Mathematics at The University of Texas at Dallas. His research focuses on developing novel statistical methods for high-dimensional data analysis, Bayesian computation, and applications in genomics and neuroscience. He holds a Ph.D. in Statistics from the Indian Statistical Institute, Kolkata (2019), and postdoctoral experience at Duke University (2019–2020) and The University of Texas at Austin (2020–2022). Education: Ph.D. in Statistics, Indian Statistical Institute, Kolkata (2019) M.Stat., Indian Statistical Institute, Kolkata (2013) B.Sc. in Statistics, Calcutta University (2011) Research Interests: Graphical models and covariance structure inference Clinical trial design and real-world data integration Bayesian asymptotics and scalable computation High-dimensional clustering and multiple hypothesis testing Applications in genomics, neuroscience, and public health Grants: Seed Program for Interdisciplinary Research (SPIRe) Grant ($60,000), UTD (2024) New Faculty Research Symposium (NFRS) Grant ($25,000), UTD (2023) Awards: ISBA Biostats and Pharma Junior Researcher Award (2023) Travel awards from ISBA (2022) and IBCASC (2018) Student Poster Award, International Indian Statistical Association (2017) His work includes developing open-source statistical tools such as the SUFA and CAPPMx packages for subspace factor analysis and scalable Bayesian computation. He collaborates with researchers in psychology and biomedical sciences to advance interdisciplinary research.

Clemens Verhooselمشاهده پروفایل

دانشیار

• Computational Mechanics
• Numerical Methods
• Solid Mechanics
• +۵ مورد دیگر

Clemens Verhoosel is an Associate Professor in Computational Methods for Model- and Data-Driven Engineering at Eindhoven University of Technology (TU/e), specializing in the Department of Mechanical Engineering. His research focuses on numerical methods for engineering applications, including isogeometric analysis, immersed methods for complex geometries, and uncertainty quantification. He holds a prestigious NWO Veni Grant (2011) and has contributed to open-source tools like the Nutils toolkit. Education: MSc (with honors) in Aerospace Engineering from TU Delft (2005) PhD in Computational Mechanics from TU Delft (2009, cum laude) Postdoctoral research at the University of Texas at Austin (2009-2010) Research Interests: Scan-based immersed isogeometric analysis Uncertainty quantification and Bayesian inference Fluid-structure interaction Multiphase flow and fracture mechanics Teaching: Courses include Advanced Discretization Techniques (Isogeometric Analysis) and Scientific Computing for Mechanical Engineering. Awards: NWO Veni Award (2011): "Efficient modelling of bone fractures" Labs/Teams: Leads the Group Verhoosel and contributes to EAISI Foundational and Energy Technology research groups.

Heiko Schüttمشاهده پروفایل

دانشیار

• Computational Cognitive Science
• Visual Perception
• Computational Neuroscience
• +۷ مورد دیگر

Heiko Schütt is Associate Professor for Computational Cognitive Science and Modeling at the Université du Luxembourg. His research focuses on developing mechanistic models of visual perception and cognition using deep neural networks, Bayesian inference, and efficient coding principles. He is also the developer of widely used toolboxes such as rsatoolbox for representational similarity analysis and Psignifit 4 for psychometric function fitting. His work lies at the intersection of cognitive science, computational neuroscience, and machine learning, with a strong emphasis on creating and evaluating models of human perception and decision-making. He investigates early visual processing, eye movement dynamics, and model evaluation methodologies, contributing both theoretical frameworks and practical tools to the scientific community. The recent publications reflect a strong trend toward developing rigorous statistical methods for comparing neural and cognitive models, particularly in the context of representational geometries and perceptual decision-making. His work increasingly bridges human cognition and artificial neural networks, exploring parallels in generalization and representation. Much of his research is image-computable and grounded in empirical psychophysics. He has previously held postdoctoral positions with Weiji Ma at New York University and Niko Kriegeskorte at the Zuckerman Institute, Columbia University. His PhD was jointly conducted with Felix Wichmann at Tübingen and Ralf Engbert at Potsdam, focusing on early visual processing and eye movements. No scientific awards are mentioned in the provided text. While no formal advisees are listed, his active research program and development of major scientific toolboxes suggest a role in mentoring students and collaborators. There is no mention of specific grants, but his work likely involves funded research given the scale and impact of his projects. He maintains active code repositories on GitHub for rsatoolbox, Psignifit 4, early vision models, and eye movement modeling, indicating leadership in open science and computational tool development. These resources support a broad community in cognitive and computational neuroscience.

Ross T. Whitakerمشاهده پروفایل

استاد

• Image Processing
• Scientific Computing
• Statistical Shape Analysis
• +۷ مورد دیگر

Ross T. Whitaker is a Professor and Director of the School of Computing at the University of Utah, where he is also a faculty member of the Scientific Computing and Imaging (SCI) Institute. He has been a key figure in advancing computational methods in medical imaging, geometry processing, and scientific visualization. University: University of Utah School: School of Computing Institute: Scientific Computing and Imaging (SCI) Institute Education: B.S. in Electrical Engineering and Computer Science, Princeton University (Summa Cum Laude, 1986) Ph.D. in Computer Science, University of North Carolina at Chapel Hill (1994) Research Interests: Ross Whitaker's research spans image analysis, statistical shape modeling, GPU-based PDEs, level-set methods, and uncertainty visualization. His work emphasizes adaptive, data-driven models for image denoising, segmentation, and reconstruction, often using nonparametric statistics and information-theoretic principles. He has pioneered methods in tetrahedral meshing, particle systems for surface sampling, and manifold learning. Recent Publication Trends: His recent publications (2020–2025) highlight a strong focus on statistical shape analysis in medical applications, particularly in metopic craniosynostosis , using tools like DeepSSM and CranioRate . There is a growing emphasis on deep learning, explainable AI, and multimodal sensing (e.g., audio-visual detection of idling vehicles). His interdisciplinary work bridges computer science, biomedical engineering, and environmental health. Scientific Awards: IEEE Fellow NSF Career Award Alumni Scholarship Award (UNC Chapel Hill) Advising and Grants: He leads a graduate research group in image analysis and scientific computing, mentoring students like Miriah Meyer (Ph.D. 2010). His projects are supported by federal agencies and industrial contracts, focusing on shape modeling, medical imaging, and environmental monitoring. He has developed influential software such as ShapeWorks, Seg3D, Cleaver, and contributed to ITK. Labs and Teams: He is a core member of the SCI Institute, a leading interdisciplinary research center in scientific computing and visualization. His team develops computational tools for biomedical image analysis and uncertainty quantification, with applications in surgery, diagnostics, and public health.

Daniele Marinazzoمشاهده پروفایل

استاد

• Computational Neuroscience
• Complex Systems
• Neurophysics
• +۴ مورد دیگر

Daniele Marinazzo is a Full Professor in the Department of Data Analysis at Ghent University (Belgium), with a background in statistical physics and a focus on computational neuroscience and complex systems. He serves as Deputy Editor at PLOS Computational Biology , Co-Editor-in-Chief at Neurons, Behavior, Data Analysis, and Theory , and academic editor at Network Neuroscience and Imaging Neuroscience. His work spans theoretical physics, neuroscience methodology, and experimental neurophysiology. Academic Affiliation: Ghent University (Belgium) Editorial Roles: 3+ journals Research Initiatives: EBRAINS representative Research Interests combine computational neuroscience, complexity science, and network analysis. He investigates higher-order interactions in brain networks, synergistic information processing, and methodological advancements in neuroimaging. Recent work includes MEG/EEG analysis, gene expression modeling, and developing open-source tools like SPM-Python. Scientific Contributions include 15+ recent publications on topics ranging from individual brain fingerprinting to multisensory integration models. His work appears in venues like Nature Communications , Physics of Life Reviews , and Entropy , with methodological innovations in hidden factor localization and network synchronization analysis. Current projects include PhD/postdoc recruitment for fast-sampled fMRI analysis and organizing the Erice workshop Higher-Order Interactions: Mechanisms, Behaviors, and Networks (August 2025).

Daniel J. Packمشاهده پروفایل

استاد

• Unmanned Aerial Vehicles
• Intelligent Control
• Automatic Target Recognition
• +۷ مورد دیگر

Daniel J. Pack is the Dean of the School of Engineering and Computer Science at Baylor University, a position he assumed on June 1, 2023. Previously, he served as Dean at the University of Tennessee at Chattanooga (UTC) from 2015, leading departments in civil, chemical, computer, electrical, and mechanical engineering, as well as engineering management and technology. His educational background includes: Bachelor of Science in Electrical Engineering – Arizona State University Master of Science in Engineering Sciences – Harvard University Ph.D. in Electrical Engineering – Purdue University Visiting Scholar – MIT-Lincoln Laboratory Dr. Pack's research focuses on unmanned aerial vehicles, intelligent control, robotics, automatic target recognition, and engineering education . His work emphasizes autonomous systems, cooperative control, and embedded systems design, contributing to both defense and educational applications. He has co-authored seven textbooks and published over 130 technical papers. The most recent articles reflect a strong trend in autonomous systems, multi-vehicle coordination, intelligent control, and educational innovation . Topics span from deep learning in UAV imagery to experiential learning frameworks, indicating a dual focus on cutting-edge robotics and transformative engineering pedagogy. His scientific awards include: Carnegie U.S. Professor of the Year (2005) Frank J. Seiler Research Excellence Award Tau Beta Pi Outstanding Professor Award Academy Educator Award Magoon Award Dr. Pack has advised numerous students and led major research initiatives, particularly in unmanned systems. He directed the Academy Center for Unmanned Aircraft Systems Research at the USAFA and has secured significant research grants. His editorial roles include Associate Editor for IEEE Systems Journal and Editor-at-Large for Journal of Intelligent & Robotic Systems. He is actively involved in professional societies such as IEEE, ASEE, Eta Kappa Nu, and Tau Beta Pi, and is a registered Professional Engineer in Colorado. His leadership fosters experiential learning environments across bachelor’s, master’s, and doctoral engineering programs.

• Machine Learning
• Computer Vision
• Signal Processing
• +۳ مورد دیگر

Chenguang Liu is a researcher at Institut Polytechnique de Paris , France, with a focus on computational methods, machine learning, and control systems. His work bridges theoretical and applied research across multiple domains including computer vision, signal processing, and operations research. Key research areas: Machine Learning, Computer Vision, Computational Physics, Control Theory Notable publication trends: Develops novel algorithms for parallel computing, peridynamic modeling, and real-time systems Recent contributions include Bayesian neural networks for gas-bearing prediction, multi-agent reinforcement learning for UAV swarms, and domain adaptation techniques in object detection. He collaborates extensively with researchers in electrical engineering and applied mathematics disciplines.

• Neuroscience
• Cognitive Science
• Computational Neuroscience
• +۳ مورد دیگر

Yoon Bai is a Researcher at the McGovern Institute for Brain Research at the Massachusetts Institute of Technology. Their research focuses on understanding how the brain processes natural visual and sensory information, with a particular emphasis on perceptual decision-making, neural coding in visual cortices, and the application of computational models to neurophysiological data. Key research areas include analyzing neural responses to natural stimuli, comparing human and macaque detection performances, and developing methods like neural straightening to decode high-dimensional neural activity. Their work bridges computational neuroscience and experimental neurophysiology, often using calcium imaging and population coding techniques. Recent studies investigate how environmental dynamics influence perceptual biases and how unsupervised learning algorithms align with human learning processes. No scientific awards are explicitly mentioned in the provided texts. Research contributions span foundational studies in visual perception, comparative neurophysiology, and the development of advanced imaging methodologies for primate studies.

• Machine Learning
• Artificial Intelligence
• Cyber-Physical Systems
• +۳ مورد دیگر

Ning Xiong is a Professor at Mälardalen University, affiliated with the School of Innovation, Design and Engineering and the Division of Intelligent Future Technologies. His research focuses on advanced artificial intelligence, machine learning, optimization algorithms, and cyber-physical systems. He explores applications ranging from digital twin frameworks in distributed systems to predictive maintenance using explainable AI and anomaly detection in timeseries data. His work integrates techniques like federated learning, Bayesian classifiers, and bio-inspired computing (e.g., membrane clustering) to address challenges in smart systems and data science. Key research areas include: Machine Learning & Deep Learning Cyber-Physical Systems Optimization Algorithms Smart Systems & IoT Data Science & Big Data Recent publications highlight advancements in digital twin frameworks for resilient distributed systems, ensemble learning for imbalanced data, and lightweight object detection methods for UAV imagery. His contributions emphasize practical applications in energy grids, predictive maintenance, and industrial automation while addressing theoretical challenges in model explainability and scalability. His research is characterized by interdisciplinary collaboration, combining software engineering, systems architecture, and domain-specific expertise to develop innovative solutions for dynamic environments.

Hongpeng Zhouمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Deep Learning
• Interpretable Learning
• +۳ مورد دیگر

Hongpeng Zhou is a Dame Kathleen Ollerenshaw Fellow at the Department of Computer Science, University of Manchester. His research focuses on interpretable machine learning for cancer research, Bayesian deep learning, and bioinformatics. Previously, he was a postdoctoral researcher at the same university, developing ML algorithms for single-cell transcriptomics data. Education: PhD in Bayesian deep learning, Delft University of Technology (2018–2022) MEng in Control Science, Harbin Institute of Technology (2015–2017) BEng in Control Science, Harbin Institute of Technology (2011–2015) Research Interests: Dr. Zhou specializes in creating interpretable ML models for biological and engineering systems. His work bridges deep learning, Bayesian methods, and domain-specific applications like materials science, geotechnical engineering, and cancer genomics. Core themes include neural network compression, physics-informed learning, and unsupervised exploration of complex datasets. Publication Trends: Recent articles demonstrate interdisciplinary collaboration across computer science, bioinformatics, and mechanical engineering. Dominant themes include interpretable ML architectures, physics-informed neural networks, and applications in biomedicine/materials science, with emphasis on uncertainty quantification and real-world system optimization. Awards: Dame Kathleen Ollerenshaw Fellowship Projects & Advising: Leads the TICM R&D project on efficient 3D Gaussian splatting and co-investigates breast cancer prevention initiatives. Accepts PhD students and collaborates with groups like Prof. Magnus Rattray's bioinformatics team.

• Materials Modelling
• Micro-mechanics
• Voronoi Tessellation
• +۱ مورد دیگر

Dr. Pan Zhang is a Researcher at the Department of Mechanical Engineering, Imperial College London, specializing in materials modeling and micro-mechanics. His work focuses on crystal plasticity modeling, particularly the generation of microstructures for polycrystalline materials to support finite element analysis. He holds a BSc and MSc in Control Engineering and a PhD in Mechanical Engineering. Research interests include Voronoi tessellation applications, optimization using evolutionary algorithms, and material behavior under extreme conditions. His contributions span computational geometry, composite materials, and supply chain environmental impact analysis. Publications highlight advancements in grain structure simulation, cryptographic integration in embedded systems, and cloud-based privacy-preserving techniques. Collaborations include projects with Professors Liliang Wang and Daniel Balint, though he is not directly listed as a supervisor for the mentioned PhD studentships. No scientific awards are explicitly stated, but his work demonstrates expertise in interdisciplinary engineering and computational methods.

Pak-Wing Fokمشاهده پروفایل

استاد

• Applied Mathematics
• Mathematical Biology
• Cardiovascular Modeling
• +۱۲ مورد دیگر

Pak-Wing Fok is a Professor in the Department of Mathematical Sciences at the University of Delaware, within the College of Arts & Sciences. M.Sci. - Imperial College Ph.D. - Massachusetts Institute of Technology Postdoctoral - California Institute of Technology Postdoctoral - University of California, Los Angeles As an applied mathematician, Dr. Fok uses mathematical models to gain insight into complex problems from biology and the physical sciences, with a particular focus on cardiovascular diseases. His research spans multiple areas: Mathematical Modeling of Cardiovascular Systems : He investigates atherosclerosis and diabetes-related vascular complications, focusing on how mechanical properties and geometry of arteries change in these diseases. His work helps understand how plaque formation and rupture can lead to myocardial infarction or stroke. Mechanics of Growth : This research involves modeling biological systems where growth is a dominant feature, such as diseased blood vessels and bacterial chains. He studies how tissues undergo volumetric growth or resorption under mechanical deformation. MRI Parameter Estimation : He develops algorithms to improve parameter estimation in magnetic resonance imaging, addressing the challenge of inferring parameters from noisy signals in multi-component exponential functions. Dr. Fok's recent publications reflect his interdisciplinary approach, combining mathematics, biology, and biomedical engineering. His work includes computational modeling of vulnerable plaques, stochastic processes in biophysics, and mathematical approaches to understanding vascular disease progression. Dr. Fok has supervised numerous graduate students whose theses have focused on topics including: Simulation of Intimal Thickening Stochastic Modeling of Toxin Effects Finite Element Simulation of Atherosclerotic Plaque Estimation and Inference in Imaging and Biophysics Portfolio Theory Applications Analysis of Exit Time Problems His methodology combines partial differential equations, asymptotic methods, stochastic analysis, and simulations to tackle complex problems in both biological systems and operations research.

Orhan Deniz GENÇAĞAمشاهده پروفایل

استادیار

• Signal and Image Processing
• Machine Learning
• Knowledge Discovery and Data Mining
• +۹ مورد دیگر

Orhan Deniz GENÇAĞA is an Assistant Professor in the Department of Electrical and Electronic Engineering at Antalya Bilim University since 2017. Prior to this, he held postdoctoral and research associate roles at SUNY Albany (2007), National Oceanic and Atmospheric Administration (NOAA) Center of CUNY (2009–2011), University of Texas Department of Space Sciences, and Carnegie Mellon University. His academic career spans interdisciplinary projects in signal processing, statistics, and systems modeling. Education: Ph.D. in Electrical and Electronic Engineering from Boğaziçi University M.S. in Electrical and Electronic Engineering from Boğaziçi University B.S. in Electrical and Electronic Engineering from Yıldız Teknik University Research Interests: GENÇAĞA’s work focuses on statistical signal processing, Bayesian inference, uncertainty modeling, causality, chaos theory, and remote sensing. His expertise extends to heavy-tailed processes, acoustic echo cancellation, source separation, and system identification, with applications in machine learning, information theory, and speech processing. Professional Roles and Affiliations: He chaired the IEEE Pittsburgh Signal Processing and Control Systems Societies, served as general chair for the First ECEA, and organized MaxEnt 2007. Additionally, he is a member of the editorial board of Entropy and an inventor in two US patents. Scientific Awards: NATO Research Fellowship