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Taiji SuzukiView profile

Associate Professor

• Machine Learning
• Statistical Learning Theory
• Deep Learning
• +6 more

Prof. Taiji Suzuki is an Associate Professor at the University of Tokyo in the Department of Mathematical Informatics . He also serves as Team Leader of the "Deep Learning Theory" team at AIP-RIKEN , Japan. With a PhD in Information Science and Technology from the University of Tokyo (2009), he has held academic positions at the University of Tokyo (2009-2013) and Tokyo Institute of Technology (2013-2017) before returning to the University of Tokyo in 2017. University of Tokyo (2004: BEng in Mathematical Engineering) University of Tokyo (2006: MSc in Information Science and Technology) University of Tokyo (2009: PhD in Information Science and Technology) His research interests span statistical learning theory , deep learning , kernel methods , sparse estimation , and stochastic optimization . He investigates how neural networks adapt to function smoothness, avoid the curse of dimensionality, and achieve global optimization through mean-field dynamics. His work bridges theoretical guarantees (minimax optimality, convergence analysis) with practical implementations (transformers, graph neural networks, diffusion models). The 15 most recent articles focus on transformers' representation power, graph neural networks' limitations, diffusion models' convergence, and optimization theories for deep learning. Key themes include information-theoretic bounds , feature learning dynamics , and mean-field analysis . He explores applications in AI for medicine, federated learning, and biomedical modeling. Awards & Recognition: Outstanding Paper Award, ICLR 2021 MEXT Young Scientists’ Prize Outstanding Achievement Award, Japan Statistical Society 2017 Outstanding Achievement Award, Japan Society for Industrial and Applied Mathematics 2016 Best Paper Award, IBISML 2012 Best Paper Candidate, ICDM 2019 He has served as Area Chair for NeurIPS, ICML, ICLR, AISTATS, and as Program Chair for ACML. His scientific contributions include convergence theories for stochastic gradient methods, minimax analysis of deep learning vs kernel methods, and novel frameworks for distributional optimization in diffusion models.

Douglas ArnoldView profile

Professor

• Neuroimaging
• Multiple Sclerosis
• Alzheimer's Disease
• +3 more

Douglas Arnold is a Distinguished James McGill Professor at McGill University, based at The Neuro (Montreal Neurological Institute-Hospital). He serves as Core Faculty for the Brain Imaging Centre (BIC) within the Neuroimaging and Neuroinformatics Research Group, specializing in advanced MRI applications for neurological disorders. His clinical role includes neurology practice with expertise in quantitative imaging biomarkers. Dr. Arnold's research centers on developing and applying neuroimaging techniques to track pathological evolution in multiple sclerosis and Alzheimer's disease. He investigates gray/white matter changes, lesion dynamics, and therapy effects using magnetization transfer imaging, spectroscopic methods, and voxel-based analysis. His work bridges technical innovation with clinical applications to quantify neurodegeneration and treatment efficacy. Analysis of his publications reveals consistent focus on multiple sclerosis pathophysiology, particularly demyelination/remyelination processes and cortical pathology. Key methodological contributions include advanced MRI quantification for brain atrophy, lesion evolution, and neuroprotection assessment. His collaborative work spans clinical trials (e.g., rituximab therapy) and technical innovations in imaging analysis. As an active member of The Neuro's Neuroimaging and Neuroinformatics Research Group, Dr. Arnold contributes to McGill University's neuroscience mission within the McGill University Health Centre. His work supports open science initiatives through data sharing and methodological transparency, advancing both research and clinical practice in neurodegenerative diseases.

• Neurosciences
• Clinical Sciences
• Cognitive and Computational Psychology
• +5 more

Professor Torsten Baldeweg is a leading figure in Developmental Cognitive Neuroscience at the University College London (UCL) Developmental Neurosciences Department . With over a decade at UCL since 2006, his research bridges clinical neuroscience and neuroimaging to advance understanding of pediatric epilepsy and brain plasticity. Current role: Professor of Developmental Cognitive Neuroscience (2014–present) Prior role: Reader in Developmental Neurosciences (2006–2014)

• Magnetic Resonance Imaging
• Oncology
• Medical Imaging
• +4 more

Dr. Renske Gahrmann is a researcher at Erasmus MC in the Department of Radiology & Nuclear Medicine. Her work focuses on advancing magnetic resonance imaging (MRI) techniques and their applications in oncology and neuroradiology. Active in MRI optimization for radiation-free diagnostics Researches glioma growth modeling and head and neck cancer imaging Collaborates on HPV-related tumor differentiation and clinical decision-making tools Her recent publications highlight innovations in: Black Bone MRI for craniosynostosis evaluation Multi-task deep learning for glioma subtyping HPV status differentiation in oropharyngeal carcinomas Oligometastasis quantification in metastatic cancers Collaborative networks span international institutions, with a focus on integrating artificial intelligence with radiological practices . Key research outputs demonstrate translational impact across neuro-oncology and head and neck cancer domains.

• Neurology
• Neuro-Oncology
• Glioma Research
• +4 more

Maarten Wijnenga is a researcher at Erasmus Medical Center specializing in Neurology . His work focuses on neuro-oncology, particularly gliomas (astrocytoma, oligodendroglioma, ganglioglioma), and leverages advanced imaging techniques like MRI and diffusion tensor imaging to study tumor biology and surgical outcomes. Key Collaborations : Works with neurosurgeons, geneticists, and imaging specialists across institutions. Research Themes : Tumor growth modeling, epilepsy management in glioma patients, and AI-driven molecular subtyping. Methodologies : Multi-task deep learning, pre-operative imaging analysis, and clinical cohort studies. Impact : High citation rates in journals like Neuro-Oncology and Clinical Cancer Research . His recent publications highlight innovations in integrating molecular data with neuroimaging for personalized treatment planning and optimizing educational frameworks in neurology.

• Numerical Analysis
• Applied Mathematics
• Computational Mathematics
• +4 more

Angelamaria Cardone is an Associate Professor in the Department of Mathematics at the University of Salerno, Italy. Her office is located at Fisciano Campus, Building F2, First Floor, Room 022. She specializes in numerical analysis with a focus on fractional differential equations, Volterra integral equations, and computational mathematics. Her main research interests include: Numerical methods for fractional differential equations Collocation methods for integral equations Stability analysis of numerical methods Spectral methods for time-fractional problems Computational approaches to reaction-diffusion systems Dr. Cardone's research focuses on developing efficient numerical methods for solving differential and integral equations, particularly those involving fractional derivatives. Her work often combines finite difference schemes with spectral methods to achieve high accuracy and computational efficiency. She has made significant contributions to the understanding of conservation laws in time-fractional diffusion equations and has developed MATLAB implementations of numerical methods for fractional differential equations. Her scholarly work demonstrates a consistent focus on: Developing methods with improved stability properties Creating efficient algorithms that reduce computational cost Preserving important mathematical properties in discrete formulations Applying numerical methods to real-world problems in physics and engineering Dr. Cardone serves as a reviewer for multiple journals in her field and has collaborated with researchers internationally.

Csaba BalazsView profile

Professor

• Theoretical Particle Physics
• Cosmology
• Dark Matter
• +5 more

Professor Csaba Balazs at the School of Physics and Astronomy, Monash University, is a leading theoretical particle physicist investigating cosmic connections between elementary particles and the Universe's evolution. His work bridges CERN's Large Hadron Collider experiments with astrophysical observations to unravel dark matter, dark energy, and fundamental force unification. Research Pillars : Dark Matter & Energy, Supersymmetry, Gravitational Waves, Higgs Physics Key Collaborations : Cherenkov Telescope Array, GAMBIT Collaboration Scientific Achievements include developing frameworks for interpreting LHC data and advancing dark matter detection through multi-messenger astronomy. His 2019-2020 computational grants enabled large-scale simulations for particle-cosmology interplay. Publications span collider phenomenology, dark sector studies, and cosmological phase transitions. He serves as Scientific Coordinator for GAMBIT and organizes astroparticle workshops, while contributing to journals like Universe and Frontiers in Physics as editor/reviewer.

• Applied Mathematics
• Continuum Mechanics
• Mathematical Biology
• +5 more

Alain Goriely is the Statutory Professor (Chair) of Mathematical Modelling at the University of Oxford's Mathematical Institute. He directs three major research centers: the Oxford Centre for Industrial Applied Mathematics (OCIAM), the Oxford Centre for Collaborative Applied Mathematics (OCCAM), and the International Brain and Mechanics Trauma Lab (IBMTL). He also serves as Director of External Relations and Public Engagement for the Mathematical Institute. His research integrates applied mathematics, mechanics, and biology, with core interests in: Dynamical systems and differential equations Continuum mechanics of biological growth and morphogenesis Mathematical neuroscience (brain mechanics, Alzheimer's modeling) Industrial applied mathematics and network science Analysis of his recent publications (2024-2025) reveals dominant themes in neuro-mechanics (brain dynamics, neurodegeneration, axon mechanics), biological morphogenesis (pattern formation, growth instabilities), and nonequilibrium physics (network thermodynamics, irreversibility). Methodologically, he employs multiscale modeling, inverse problems, and physics-informed machine learning. Honors include: Fellow of the Royal Society (FRS) Fellow of the Society for Industrial and Applied Mathematics (SIAM) Fellow of the Institute of Mathematics and its Applications (IMA) He leads interdisciplinary teams at IBMTL and OCCAM focusing on brain trauma mechanics and industrial collaborations. His authored books include The Mathematics and Mechanics of Biological Growth (Springer, 2018) and Integrability and Nonintegrability of Dynamical Systems (World Scientific, 2001).

Matt ParadiseView profile

Research Fellow

• Late-Life Depression
• Mild Cognitive Impairment
• Cerebrovascular Imaging
• +2 more

Matt Paradise is a Senior Research Fellow and Old Age Psychiatrist at the Centre for Healthy Brain Ageing (CHeBA) within the University of New South Wales (UNSW Sydney), where he bridges clinical practice and advanced neuroimaging research. His dual expertise in the UK and Australia includes a PhD in vascular cognitive impairment and neuroimaging under Professor Perminder Sachdev AM and a Masters of Psychiatric Research from University College London (UCL). His research focuses on late-life depression, mild cognitive impairment, cerebrovascular imaging, dementia risk-reduction strategies, and service development for dementia care. Matt pioneered a novel MRI-based cerebrovascular pathology index through a Dementia Centre for Research Collaboration grant (2016) and explored modifiable Alzheimer’s risk factors via the Josh Woolfson Memorial Scholarship (2018). Analysis of his 15 most recent publications reveals a central theme: quantifying cerebrovascular disease’s role in dementia using neuroimaging, genetic, and biomarker approaches. His work spans systematic reviews, MRI scale development, and multi-center clinical guidelines implementation, with an h-index of 13 (2022). Scientific Awards 2016 Dementia Centre for Research Collaboration grant for MRI cerebrovascular pathology index 2018 Inaugural Josh Woolfson Memorial Scholarship for Alzheimer’s risk factor research Dean’s Award for Outstanding PhD thesis Matt’s clinical roles include Senior Staff Specialist at Concord Hospital and Senior VMO in Coffs Harbour, where he advocates for older adults. His research aims to create standardized diagnostic tools for global dementia research and clinical practice, as exemplified by the 2025 national Memory & Cognition Clinic Guidelines implementation study in Alzheimer’s & Dementia journal.

Roberta La PianaView profile

Assistant Professor

• White Matter Disorders
• Neurogenetics
• Neuroradiology
• +5 more

Roberta La Piana, MD, PhD is an Assistant Professor in the Department of Neurology and Neurosurgery at the Montreal Neurological Institute (The Neuro), McGill University, with an associate appointment in the Department of Radiology. Her interdisciplinary work bridges clinical neurology, neuroradiology, and genetics. Her primary research focuses on: Rare genetic white matter disorders and leukodystrophies Discrimination between genetic and acquired myelin disorders (e.g., multiple sclerosis) Imaging biomarkers in hereditary spastic paraplegia and cerebellar ataxias Genetic mechanisms of adult-onset neurological disorders Analysis of her 15 most recent publications reveals consistent emphasis on neurogenetic disorders with strong neuroradiological correlation. Key trends include development of MRI scoring systems for leukodystrophies, exploration of genotype-phenotype correlations in POLR3-related disorders, and investigation of atypical presentations in conditions like Krabbe disease and Vanishing White Matter disease. Her work frequently employs advanced neuroimaging techniques to identify novel diagnostic pointers. She founded the international White Matter Rounds initiative in 2013, connecting researchers and clinicians from over 15 global centers to discuss complex white matter disease cases. Her laboratory maintains strong collaborative ties with the Care4Rare Canada Consortium and international research networks.

• Functional Magnetic Resonance Imaging
• Major Depressive Disorder
• Neuroscience
• +3 more

Rolf M.J.N. Lamerichs is a University Researcher in the Department of Signal Processing Systems at Eindhoven University of Technology , affiliated with the Eindhoven MedTech Innovation Center . His work focuses on applying advanced signal processing and machine learning techniques to functional MRI (fMRI) data for improving clinical outcomes in depression. Research Interests : Functional MRI, Brain Network Analysis, Major Depressive Disorder, Neurodynamics, Clinical Decision Making, Neurofeedback Collaborations : Extensive collaborations with psychiatrists, clinicians, and computer scientists across Europe His recent work explores multi-echo fMRI , real-time neuroimaging , and multi-modal MRI for objective diagnosis and prognosis of depression. He has contributed to reproducibility frameworks in neuroimaging and developed web-based applications for brain network analysis. Scientific Impact : 273 citations in publications, with recent high-impact contributions in Frontiers in Psychiatry and NeuroImage: Clinical . His research has received media attention from 6 news outlets and academic coverage in 3 datasets.

H.J.C.E. (Rick) SengersView profile

Researcher

• Mathematics
• Computer Science
• Medical Imaging
• +4 more

H.J.C.E. (Rick) Sengers is a Postdoctoral Researcher at the Applied Differential Geometry department of the Mathematics and Computer Science school at Eindhoven University of Technology . His work bridges mathematical theory with applications in medical imaging and computer vision. Education: MSc in Mathematics (TU/e, 2018), BSc in Mathematics (TU/e, 2016), PhD in Mathematics (TU/e, 2024) Research focuses on Riemannian geometry , optimal transport , and diffusion tensor imaging , with applications in high-angular MRI and geodesic tractography . Key themes include data-driven metrics, fiber bundle coherence, and geometric modeling for medical imaging. Recent publications highlight advancements in entropic optimal transport on Lie groups, Riemannian frameworks for diffusion MRI, and harmonic-based tensor approximations . His methodology integrates differential geometry with statistical physics and computational algorithms. Collaborations : L. Florack, R. Duits, A. Fuster Active in computational diffusion MRI and variational methods, his work appears in conference proceedings (MICCAI, SSVM) and journals like Frontiers in Physics . No awards or supervisory roles are disclosed in available data.

Luc M.J. FlorackView profile

Professor

• Medical Image Analysis
• Differential Geometry
• Multiscale Representations
• +5 more

Luc M.J. Florack is a Full Professor in the Applied Differential Geometry (ADG) group of the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e). He also serves as Full Professor at EAISI Health and within the Center for Analysis, Scientific Computing and Applications. His academic credentials include an MSc in Theoretical Physics and a PhD in Medical Image Analysis, both earned at Utrecht University with honors (top 2%). Professor Florack's research centers on multiscale and differential geometric representations of complex imaging data , with primary applications in magnetic resonance imaging (MRI) techniques, particularly diffusion and tagging MRI. His work bridges theoretical mathematics with clinical applications through geometrical reformulation of image analysis challenges, including geodesic tractography for brain connectivity mapping (connectomics) and deformation/strain analysis for myocardial function quantification. His research aims to integrate these advanced mathematical techniques into clinical workflows. His scientific contributions have been recognized with several prestigious awards: “Een gepersonaliseerd zorgpad voor iedere hersentumorpatiënt” (NWO Scientific Project, 2020) PhD obtained with judicium cum laude (1993) “The Problem of Scale in Biomedical Image Analysis” (NWO Vici Scientific award, 2005) With approximately 200 peer-reviewed publications, Professor Florack has significantly advanced medical image analysis. His publication trends show consistent output with recent work focusing on diffusion tensor imaging, white matter structure analysis, and cardiac MRI applications. He has served on the editorial board of the Journal of Mathematical Imaging and Vision and reviewed for over 30 academic venues. In 1997, he co-founded the International Scale Space Conferences series (SSVM). He actively teaches courses in Differential Geometry and Tensor Calculus. Professor Florack serves as a Member of the Management Team 3TU.AMI representing TU/e and on the Advisory Board for Mathematics (‘Wiskunde Tafel’) of the Dutch Science Foundation NWO. His research contributes to UN Sustainable Development Goals, particularly in health-related areas through advanced medical imaging technologies.

• Database Systems
• Data Mining
• Machine Learning
• +7 more

K. Selçuk Candan is a Professor at Arizona State University, Tempe, Arizona, with an extensive research career spanning over two decades in database systems, data mining, and machine learning. His work demonstrates deep expertise in tensor decomposition, causal inference, spatio-temporal analysis, and time series modeling. Candan has maintained a prolific publication record with significant contributions to premier venues including SIGMOD, ICDE, VLDB, and CIKM. His research interests focus on the intersection of data management and machine learning, particularly in developing novel methods for tensor decomposition, causal discovery, and efficient data representation. Candan's work addresses critical challenges in handling high-dimensional data, building robust predictive models, and developing systems for complex decision-making scenarios. Recent research directions include spatio-causal modeling for environmental systems, causal benchmarks, and learned data mapping techniques that bridge traditional database systems with modern machine learning approaches. Analysis of Candan's recent publications reveals a strong trend toward causal inference applications across multiple domains including hydrology, environmental science, and healthcare. His work increasingly integrates deep learning techniques with traditional data management principles, as evidenced by research on diffusion models for time series imputation and learned data mapping for compression. The research demonstrates practical applications in wetland conservation, streamflow forecasting, building fault detection, and medical diagnostics, showing a commitment to solving real-world problems through advanced data science techniques. Candan has established significant collaborative relationships, particularly with Maria Luisa Sapino, Huan Liu, and several other researchers across multiple institutions. His work often appears in top-tier conferences and journals, reflecting the high impact and quality of his research contributions. While specific grant information isn't detailed in the available text, the scope and depth of his research suggest substantial research funding supporting his work in data science and causal inference.

Patrick FriedrichView profile

Researcher

• Neuroscience
• Neuroimaging
• Brain Connectivity
• +3 more

Dr. Patrick Friedrich is a researcher at the Institute of Neuroscience and Medicine (INM) , specifically within the Brain and Behavior (INM-7) department at Research Center Jülich GmbH. His work focuses on functional brain connectivity, hemispheric asymmetries, and neuroimaging methodologies.