جست‌وجوی استاد و پژوهشگر

• Neuroimaging
• MRI Segmentation
• Brain Structure Analysis
• +۵ مورد دیگر

Dr. Pierre-Louis Bazin is a Researcher in the Department of Neurophysics at the Max Planck Institute for Human Cognitive and Brain Sciences. He holds a PhD from INRIA Rocquencourt (France) and conducted postdoctoral research at Brown University and Johns Hopkins University in the US. His work focuses on advanced neuroimaging techniques, including high-resolution MRI parcellation, quantitative MRI, and subcortical structure analysis. Key projects involve developing automated segmentation tools (e.g., Nighres), exploring cerebellar and subcortical anatomy, and investigating neurodegenerative markers using multi-modal approaches. Education 1995-1998: Electrical Engineering Diploma, SUPELEC (France) 1998-2001: PhD in Computer Vision & Graphics, INRIA Rocquencourt (France) 2001-2003: Postdoc in Computer Vision & Archaeology, Brown University (USA) 2003-2006: Postdoc in Neuroimaging, Johns Hopkins University (USA) Research Interests High-field MRI (7T+) applications in structural and functional brain mapping Development of neuroimaging software tools (e.g., Nighres) Subcortical and cerebellar morphometry in health and disease Quantitative MRI techniques for histology correlation Connectivity analysis in psychiatric disorders (e.g., schizophrenia) Awards Royal Netherlands Academy of Arts and Sciences (KNAW) membership (2020, 2024) Grants & Labs : Directs the Neurophysics lab at MPI-CBS. Active in large-scale projects like the Amsterdam Ultra-high field adult lifespan database (AHEAD). Collaborates on tools like MP2RAGEME for multi-parametric MRI. Labs/Teams : Leads neuroimaging method development and clinical translation efforts in Leipzig, Germany.

• Inverse Problems
• Image Processing
• Optimization
• +۵ مورد دیگر

Markus Grasmair is a Professor at the Department of Mathematical Sciences at the Norwegian University of Science and Technology (NTNU). His research focuses on inverse problems, mathematical methods in image processing, and optimization. He has contributed to nonsmooth variational methods and Lavrentiev regularization for ill-posed problems. Education: Habilitation in Mathematics (University of Vienna, 2011), PhD in Mathematics (University of Innsbruck, 2006), MSc in Mathematics (University of Innsbruck, 2003). Markus's research spans mathematical imaging, biomedical applications, and PDE-based modeling. He has developed regularization techniques for inverse problems, including Lavrentiev and L1 methods, and applied these to medical imaging and cervical cancer risk stratification. His work integrates sparse optimization, shape analysis, and data-driven approaches. Recent publications highlight interdisciplinary trends, combining inverse problems with medical imaging (X-ray calibration, cervical cancer prediction) and extending variational methods to PDEs, multiscale modeling, and graph-based systems. Key subfields include monotone operators, matrix factorization, and elastic shape analysis. He teaches courses in optimization and has held academic positions at the University of Vienna, Catholic University of Eichstätt-Ingolstadt, and University of Innsbruck. Contact: markus.grasmair@ntnu.no. Current Role: Professor (NTNU) Past Roles: Substitute Professor (Catholic University of Eichstätt-Ingolstadt, 2012-2013), Assistant Professor (University of Vienna, 2009-2012)

Laura Orellanaمشاهده پروفایل

استادیار

• Biophysics
• Computational Biology
• Structural Biology
• +۳ مورد دیگر

Laura Orellana is an Assistant Professor at the Karolinska Institute , Department of Oncology-Pathology. Her research bridges computational biophysics and structural biology to study protein conformational dynamics in cancer and mendelian diseases . Education : PhD in Physics (summa cum laude, University of Barcelona), MD in Medicine (University of Barcelona), MSc in Biophysics and Structure & Function of Proteins, BSc in Biochemistry Research Interests focus on protein dynamics , evolutionary mutational patterns , and conformational profiling of tumors. She develops multiscale simulation methods (normal modes, molecular dynamics, machine learning) to identify dynamically hot mutations and conformation-specific drug targets . Her 15 recent publications span topics from oncogenic EGFR mutations to ribosomal protein dysplasias , emphasizing computational approaches to decode protein structure-function relationships across cancer and genetic disorders . Scientific Awards : Extraordinary Prize for Academic Excellence in Biochemistry (2006) EMBO Biomolecular Simulation Bursary (2012) Biophysical Society Travel Award (2016) EMBRACE-Rigaku-EBI Bursary (2009) IUBMB-FEBS Young Scientist Program Fellow (2012) She leads the Protein Dynamics and Cancer Lab , advising PhD students like Knut-Rasmus Bergendahl and Gustav Lind. Grants include the Swedish Research Council Starting Grant (2022), Cancerfonden Junior Investigator Award (2021), and KI Tenure-track Faculty Position (2019).

Jonathan D Victorمشاهده پروفایل

استاد

• Computational Neuroscience
• Neurophysiology
• Sensory Physiology
• +۳ مورد دیگر

Jonathan D Victor is Professor of Computational Neuroscience in Computational Biomedicine at the Institute for Computational Biomedicine, Professor of Neuroscience at the Brain and Mind Research Institute, and Fred Plum Professor of Neurology within the Department of Neurology at Weill Cornell Medical College (Cornell University). He serves as an Attending Neurologist at NewYork-Presbyterian Hospital and directs the Division of Systems Neurology and Neuroscience. His educational background includes: B.A. from Harvard University (1973) Ph.D. in Neurophysiology from The Rockefeller University (1979) M.D. from Cornell University Medical College (1980) Victor's research integrates computational neuroscience with clinical neurology, focusing on neural coding in sensory systems (vision, taste, smell), disorders of consciousness, and eye movement dynamics. He employs information theory, statistical modeling, and electrophysiology to decode how the brain processes natural stimuli and recovers from injury, with emphasis on perceptual spaces and neural network signatures. Analysis of his 2023-2025 publications reveals three dominant trends: computational modeling of sensory-motor transformations in natural behaviors (e.g., Drosophila locomotion), geometric frameworks for perceptual spaces (visual texture, depth processing), and EEG-based biomarkers for traumatic brain injury recovery (alpha coherence, thalamic stimulation). No scientific awards were explicitly documented in the source material. He secures major NIH/Foundation grants as Principal Investigator or Co-Investigator, including National Eye Institute projects on fixational eye movements (2022-2026), James S. McDonnell Foundation's Covid-19 consciousness recovery consortium (2020-2025), and National Cancer Institute's NeuroNex olfactory behavior study (2020-2026). These fund translational work bridging neural coding theory with clinical applications. Victor leads the Division of Systems Neurology and Neuroscience, leveraging collaborations with Weill Cornell's Brain and Mind Research Institute and Institute for Computational Biomedicine. His external relationships include consulting for Elsevier and Springer Science and Business Media on scientific publishing and data analysis.

Hagen Radtkeمشاهده پروفایل

پژوهشگر

• Marine Biogeochemistry
• Coastal Ocean Dynamics
• Climate Change Impact on Baltic Sea
• +۴ مورد دیگر

Hagen Radtke is a Researcher at the Leibniz Institute for Baltic Sea Research (IOW) under the University of Rostock's Faculty of Mathematics and Natural Sciences . His work focuses on physical and biogeochemical modeling of the Baltic Sea , with emphasis on salinity dynamics , sediment-water interactions , and element cycling in marine ecosystems. Key Research Areas: Decadal variability in Baltic Sea salinity Mechanistic sediment process modeling Element tagging methods for nutrient pathways Numerical stability in ecosystem models Code generation tools for marine simulations Model validation through "Validator" software Publication Trends demonstrate expertise in Baltic Sea climate projections , microplastics transport , and coupled atmosphere-ocean modeling . His work intersects oceanography , biogeochemistry , and environmental informatics , with significant contributions to ERGOM SED (a benthic-pelagic coupled model) and BMIP (Baltic Model Intercomparison Project). Teaching includes "Fundamentals of Marine Biology - Oceanography" in the University of Rostock's Master's program, covering physical oceanography principles. He developed open-source model validation tools and pioneered mechanistic sediment modules for marine ecosystem projections.

Marc HOFFMANNمشاهده پروفایل

استاد

• Statistics
• Nonparametric Statistics
• Stochastic Processes
• +۳ مورد دیگر

Marc HOFFMANN is a Professor at Université Paris Dauphine-PSL and holds the Fundamental Chair at the Institut Universitaire de France since 2024. His academic journey includes roles at institutions such as INRIA (2020-2022), École Polytechnique (2007-2015), and Université Gustave Eiffel (2003-2012). Research Focus: Statistics of random processes, nonparametric statistics, and applications in financial modeling and population biology. Key Contributions: Adaptive estimation, confidence bands, inverse problems, rough volatility modeling, and growth-fragmentation processes. Advising: Supervised 19 PhD students with topics spanning statistical inference, Hawkes processes, and stochastic volatility. Collaborations include CIFRE grants with EDF, SCOR, and Banque de France. Scientific Awards : Fundamental Chair at Institut Universitaire de France (2024).

• Statistical Bioinformatics
• Computational Biology
• Spatial Transcriptomics
• +۴ مورد دیگر

Dr. Shila Ghazanfar is an Australian Research Council DECRA Fellow at the University of Sydney, Faculty of Science. She is an expert in statistical and computational analysis of spatial transcriptomics and single-cell RNA-seq data, with a focus on developing bioinformatic approaches for integrating complex biological datasets across various omics modalities. Her educational background includes: Undergraduate studies in statistics and statistical bioinformatics at The University of Sydney PhD in statistics and statistical bioinformatics at The University of Sydney Royal Society Newton International Fellowship at The University of Cambridge under Dr. John Marioni in computational biology Dr. Ghazanfar's research interests center on developing statistical bioinformatic and biomedical data science approaches for meaningful integration of complex and high-dimensional biological datasets. Her multidisciplinary expertise spans statistics, statistical bioinformatics, and computational biology, enabling her to devise strategies to jointly model processes generating diverse data sources. Her work aligns with the University of Sydney Faculty of Science Research Strengths in Complex Systems, Precision and Digital Health, and Data and Decisions. Analysis of her recent publications (2021-2025) reveals strong focus areas including spatial transcriptomics infrastructure development, single-cell data integration methodologies, and applications in cancer research and cardiovascular biology. Key trends show progression from foundational method development to increasingly sophisticated multi-modal integration approaches, with significant emphasis on creating open-source computational tools for the research community. Her scientific recognition includes: Australian Research Council DECRA Fellowship Royal Society Newton International Fellowship Dr. Ghazanfar actively mentors research students, including Angel GUAN working on melanoma immunotherapy research. Her grant portfolio demonstrates substantial research support: 2023: Statistical Bioinformatics for Single Cell, Spatial and Multiomic Biotechnologies (Faculty of Science) 2022: Defining spatiotemporal mechanisms for peripheral nerve regeneration (Partnership Collaboration Award) 2022: Multiscale data integration for single cell spatial genomics (Chan Zuckerberg Initiative) Australian Research Council DECRA for statistical approaches in spatial genomics As a member of the Charles Perkins Centre, Dr. Ghazanfar participates in interdisciplinary research addressing complex health challenges through computational biology approaches, with collaborations spanning multiple domains from basic science to clinical applications.

Wesley Tanseyمشاهده پروفایل

استادیار

• Statistical Machine Learning
• Computational Oncology
• Bayesian Statistics
• +۳ مورد دیگر

Wesley Tansey serves as Assistant Professor in the Computational Oncology group within the Department of Epidemiology and Biostatistics at Memorial Sloan Kettering Cancer Center (MSKCC). His research bridges statistical machine learning with cancer biology, focusing on developing novel computational frameworks for oncology applications. Dr. Tansey's research program centers on Bayesian statistical methods for biological data analysis, with particular emphasis on spatial transcriptomics (evidenced by his BayesTME framework), drug response modeling , and multi-omics integration . His lab develops scalable algorithms for high-dimensional biological data, including UnitedMet for metabolite imputation and MultiTME for spatial profiling analysis. Current projects address combinatorial drug screening optimization, tumor microenvironment characterization, and predictive oncology platforms for rare cancers. His recent publications (2023-2025) demonstrate strong focus areas: Bayesian active learning for drug screening (6+ publications) Spatial biology methods (BayesTME, MultiTME) Metabolomics-transcriptomics integration (UnitedMet) Causal inference in biological systems Scientific recognition includes serving as Area Chair for AISTATS 2022 and frequent invited talks at major conferences including SIAM's Mathematics of Data Science meeting. Dr. Tansey actively mentors lab members including Sophie Jaro (Spotlight presenter at ICML Workshop), Haoran Zhang (contributed talk presenter), Christopher Tosh (Associate Research Scientist), and Jeff Quinn (Bioinformatics Software Engineer). His lab receives research funding supporting development of computational oncology platforms with clinical translation potential. The VIVO Lab maintains active GitHub repositories for core methodologies including BayesTME, reflecting strong software engineering practices in computational biology.

• Multiscale Materials Modelling
• Virtual Design
• Nanotechnology
• +۱ مورد دیگر

Celso Ricardo Caldeira Rego is a postdoctoral researcher at the Karlsruhe Institute of Technology (KIT), affiliated with the Institute of Nanotechnology. His research focuses on multiscale materials modeling and virtual design, contributing to advancements in nanomaterials engineering and computational methods for material innovation. His work intersects Materials Science , Computational Modeling , and Nanotechnology , emphasizing cross-disciplinary approaches for industrial and academic applications. The Institute of Nanotechnology provides state-of-the-art infrastructure for his collaborative projects.

Andrei Pisliakovمشاهده پروفایل

مدرس ارشد

• Theoretical Biophysics
• Computational Biophysics
• Biomolecular Simulations
• +۸ مورد دیگر

Dr. Andrei Pisliakov is a Senior Lecturer and Dundee Fellow at the University of Dundee, affiliated with the School of Science and Engineering. His research bridges theoretical physics, chemistry, and biology through computational biophysics. PhD: Theoretical Chemistry, Technical University of Munich MSc: Physics, Belarus State University His work focuses on developing and applying computational methods like atomistic molecular dynamics (MD), quantum mechanics/molecular mechanics (QM/MM), and hierarchical multiscale simulations to study membrane proteins, proton transfer in biological systems, and bioenergetics. Key applications include RNA processing enzymes, SARS-CoV-2 helicase, respiratory enzymes, and GPCR signaling. Recent research trends highlight interdisciplinary collaborations in structural biology and biochemistry, notably identifying novel interaction sites in mRNA capping enzymes. His 15 most recent publications emphasize computational approaches to RNA regulation, viral protein modeling, and proton dynamics in membrane systems. FPR Fellow, RIKEN Institute Dundee Fellow, University of Dundee Dr. Pisliakov's career spans postdoctoral work at UC Berkeley and the University of Southern California, followed by contract research at RIKEN Institute. He has contributed to understanding ultrafast energy transfer in photosynthetic complexes, enzyme catalysis, and proton transport mechanisms in bioenergetic systems.

• Calculus of Variations
• Multiscale Methods
• Atomistic Models
• +۴ مورد دیگر

Dr. Leonard Kreutz is a Researcher at the Department of Mathematics , Technical University of Munich (TUM), leading an Emmy Noether Junior Research Group since 2023. He has held postdoctoral positions at Carnegie Mellon University, WWU Münster, and Universität Wien, and was an acting professor at TUM in 2019. Education: PhD (2018) from Gran Sasso Science Institute, Honors Master (2014) and Bachelor (2013) in Mathematics at TUM. Research Interests: His work focuses on the Calculus of Variations , Multiscale Methods , and Discrete-to-Continuum Limits , particularly in modeling crystallization, elastic materials with voids, and topological singularities in spin systems. He explores free-discontinuity problems and geometric rigidity in variable domains. Publications span 2023–2017, emphasizing atomistic models , nonlinear elasticity , and homogenization across mathematics, materials science, and physics. Collaborative projects with leading experts like M. Cicalese and A. Braides dominate his output. Scientific Award: Emmy Noether Junior Research Group Grant (2023–present). Collaborations include institutions such as Carnegie Mellon University, WWU Münster, and the University of Vienna. His research intersects with groups focused on Mathematical Finance , Numerical Analysis , and Quantum Information Theory at TUM.

Derek Groenمشاهده پروفایل

استاد

• Multiscale Modelling
• High-Performance Computing
• Agent-Based Simulation
• +۴ مورد دیگر

Derek Groen serves as Reader in Computer Science at Brunel University within the College of Engineering, Design and Physical Sciences. He holds additional appointments as an Emeritus Fellow of the EPSRC-funded 2020 Science Network, Fellow of the Software Sustainability Institute, and Visiting Lecturer at University College London's Centre for Computational Science. His academic background includes an MSc in Grid Computing from the University of Amsterdam (2006) and a PhD in Computational Astrophysics from the University of Amsterdam and Leiden University (2010). Prior to joining Brunel in September 2015 as a Lecturer, he held postdoctoral positions on EU projects MAPPER and CRESTA focused on distributed multiscale computing and exascale high-performance computing. Groen's research centers on multiscale modeling and high-performance computing with applications spanning forced migration simulation, cerebral bloodflow modeling, and materials science. His work addresses critical challenges in performance optimization, distributed computing frameworks, and verification/validation methodologies for complex computational systems. Recent projects demonstrate strong interdisciplinary connections between computational science, social dynamics, and public health crises. Analysis of his 15 most recent publications reveals a clear trajectory toward increasingly sophisticated migration modeling tools (Flee 3, P-Flee), enhanced verification frameworks (VECMAtk, FabSim3), and pandemic response applications. His work consistently bridges computational rigor with real-world humanitarian challenges, particularly in forced displacement scenarios and public health emergencies. EPSRC eCSE funding for domain decomposition research Fellow of Software Sustainability Institute Co-author of first feature article in Advanced Materials (2014) with BBC/Daily Telegraph coverage Emeritus Fellow of 2020 Science Network Groen has secured significant research funding from EPSRC, European Commission, and United Nations for projects including SEAVEA (2021-2024), ITFLOWS (2020-2023), and STAMINA (2020-2022). His supervision history includes summer projects resulting in peer-reviewed publications, with current PhD opportunities focused on real-time environmental monitoring, migration modeling, and urban planning simulations. He actively leads Science Hackathons to foster interdisciplinary collaboration and maintains strong ties with EU research consortia through projects like ComPat and HIDALGO. His laboratory work centers on the FabSim automation toolkit and VECMAtk verification framework, supporting a research team that develops scalable solutions for global challenges. Current efforts focus on integrating machine learning with agent-based simulations to improve migration forecasting and pandemic response capabilities.

• Numerical Mathematics
• Data Assimilation
• Hamiltonian Dynamics
• +۵ مورد دیگر

Sebastian Reich is a Professor of Numerical Analysis at the University of Potsdam and holds an honorary Visiting Professorship at Imperial College London . He leads the Chair of Numerical Mathematics and serves as Editor-in-Chief of the SIAM/ASA Journal on Uncertainty Quantification since 2021. Research Interests Numerical methods for Hamiltonian systems Data assimilation in geoscience Stochastic particle filters Bayesian inference algorithms Molecular dynamics simulation Multi-scale modeling Collaborative Projects : Principal Investigator and former Speaker (2017-2024) of SFB 1294 Data Assimilation , a DFG-funded Collaborative Research Center Active participant in SFB 1114 Scaling Cascades in Complex Systems at Freie Universität Berlin Books Authored : Probabilistic Forecasting and Bayesian Data Assimilation (Cambridge UP, 2015) Simulating Hamiltonian Mechanics (Cambridge UP, 2005) Technical Contributions : Development of symplectic integration methods Innovations in ensemble Kalman filtering Regularization approaches for geophysical models Stochastic algorithms for molecular simulations

Eleni Myrto Asimakopoulouمشاهده پروفایل

پژوهشگر

• X-ray Physics
• Synchrotron Radiation
• Crystal Optics
• +۶ مورد دیگر

Eleni Myrto Asimakopoulou is a Researcher and Postdoctoral fellow specializing in synchrotron radiation research at MAX IV Laboratory, which is closely affiliated with Lund University in Sweden. She holds multiple affiliations within Lund University's research ecosystem, including membership in NanoLund: Centre for Nanoscience, LTH Profile Area: Nanoscience and Semiconductor Technology, LTH Profile Area: Photon Science and Technology, and LU Profile Area: Light and Materials. Her research focuses on advanced X-ray imaging techniques, with expertise spanning X-ray physics, crystal optics, phase contrast imaging, and high-resolution imaging systems. Dr. Asimakopoulou's work primarily addresses challenges in synchrotron radiation applications, with particular emphasis on developing novel imaging methodologies for scientific investigation. Analysis of her recent publications reveals a strong focus on cutting-edge imaging technologies, particularly in the development of MHz X-ray phase contrast imaging, crystal optics for multi-projection imaging, time-resolved imaging techniques using diffraction-limited storage rings, and high-resolution volumetric imaging systems. Her research demonstrates a consistent trajectory toward improving imaging speed, resolution, and capabilities for materials science and structural analysis applications. Dr. Asimakopoulou has received significant attention for her work, with multiple publications garnering readership on academic platforms like Mendeley and social media mentions on platforms like X (formerly Twitter). Her collaborative approach is evident through her extensive co-authorship networks across international research institutions. As a researcher at MAX IV's Science division, she contributes to advancing the capabilities of one of Europe's premier synchrotron radiation facilities, with particular focus on developing next-generation imaging techniques that push the boundaries of what's possible in materials characterization and structural analysis.

Jun Huangمشاهده پروفایل

استادیار

• Electrochemistry
• Electrocatalysis
• Computational Materials Science
• +۲ مورد دیگر

Dr. Jun Huang is an Assistant Professor at Forschungszentrum Jülich, leading the Helmholtz Young Investigator Group focused on the 'Theory of Electrocatalytic Interfaces.' He is affiliated with the Institute of Energy Technologies (IET), specifically in the department of Theory and Computer-Based Modelling of Materials in Energy Technology. His research centers on theoretical electrocatalysis and electrochemical interfaces, with expertise in: Electrical double layer phenomena and capacitance behavior Density-potential functional theory for metal-solution interfaces Multiscale modeling of electrochemical reaction environments Electrocatalyst design through computational methods Ion transport and interfacial structuring in energy systems His recent publications demonstrate a strong focus on developing fundamental theoretical frameworks for understanding electrocatalytic interfaces, with recurring themes in double-layer effects, reaction kinetics, and computational method development. The work bridges theoretical electrochemistry with applications in energy conversion and storage. Major recognitions include: Helmholtz Young Investigator Group Grant European Research Council Starting Grant Dr. Huang leads a computational research group developing advanced theoretical models to decipher electrocatalytic processes. His team focuses on creating predictive frameworks for interfacial reactions relevant to energy technologies.