جست‌وجوی استاد و پژوهشگر

Kamal Premaratneمشاهده پروفایل

استاد

• Conspiracy Theory Research
• Social Media Analysis
• Machine Learning
• +۵ مورد دیگر

Kamal Premaratne serves as a Professor in the Department of Electrical & Computer Engineering at the College of Engineering, University of Miami. His scholarly work uniquely bridges technical expertise in machine learning and signal processing with social science research on conspiracy theories and political extremism. With numerous publications in both technical and social science journals, he demonstrates an exceptional interdisciplinary approach to understanding complex contemporary phenomena. Professor Premaratne's research spans two primary domains: advanced computational methods and social/political analysis. In computational methods, he focuses on quantum tensor networks for time series analysis, graph neural networks for gesture recognition, and uncertainty quantification in machine learning systems. His social science research examines conspiracy theories including the "White Replacement" theory, QAnon, and "white genocide" narratives, investigating their sociodemographic correlates and relationship to political extremism. His work often employs mixed-methods approaches combining qualitative analysis with quantitative content analysis and network analysis. Analysis of Premaratne's recent publications reveals a strong focus on understanding how conspiracy theories spread on social media platforms and their relationship to political behavior. His technical work shows innovative applications of quantum physics concepts to machine learning problems, creating more interpretable models while maintaining performance. This dual focus demonstrates how computational methods can be applied to social science questions and vice versa. Professor Premaratne actively collaborates with researchers across disciplines including psychology, political science, and communication studies. His research has been published in high-impact journals such as Scientific Reports, Journal of Politics, and Political Science Quarterly. Though specific grant information isn't detailed in the available materials, his extensive publication record suggests substantial research activity. His work has significant implications for understanding political polarization, misinformation spread, and developing more interpretable AI systems.

Jürgen Struckmeierمشاهده پروفایل

استاد مدعو

• Theoretical Physics
• General Relativity
• Gauge Theory
• +۶ مورد دیگر

Jürgen Struckmeier is an Adjunct Professor at the Department of Physics, Goethe University Frankfurt am Main, and a FIAS Fellow since 2016 leading the working group "Canonical Gravity Theory". His research focuses on deriving general relativity through canonical transformation formalism under dynamic space-time, grounded in classical field theory and the principle of action. Research Interests : Covariant canonical gauge theories of gravity Hamiltonian mechanics in extended phase space Dark energy and cosmic expansion Stochastic effects in particle beams Noether's theorem and Lie symmetries Geometrodynamics and torsion in spacetime Scientific Awards : 1995 Particle Accelerator Conference Award for work on emittance growth in intense beams Collaborators and Students : Vladimir Denk, Matthias Hanauske, Dirk Kehm, Johannes Kirsch, Julia Lienert, David Vasak, and PhD student Armin van de Venn.

• Homogenization and Multiscale problems
• Numerical analysis
• Inverse Problems
• +۲ مورد دیگر

Associate Professor Hoang Viet Ha is a faculty member at the School of Physical & Mathematical Sciences, Nanyang Technological University (NTU), Singapore. His research focuses on advanced mathematical methods for solving complex partial differential equations with applications in multiscale modeling and uncertainty quantification. His educational background includes: Undergraduate studies at the University of Wollongong, Australia (1993-1996) Ph.D. at the University of Cambridge, UK (1996-1999) Junior Research Fellow at Gonville and Caius College, Cambridge (1999-2004) Fellow at Emmanuel College, Cambridge (2004-2008) Professor Hoang's research spans Homogenization and Multiscale problems , Numerical analysis , Inverse Problems , Stochastic partial differential equations , and Free boundary value problems . His work develops computational frameworks for high-dimensional approximation, particularly through finite element methods and polynomial chaos expansions. This enables efficient solutions for stochastic PDEs in elasticity and fluid dynamics where traditional methods face the curse of dimensionality. Analysis of his 2009-2015 publications reveals a cohesive trajectory: evolving from foundational homogenization theory to cutting-edge stochastic multiscale algorithms. Key themes include sparse tensor discretizations for high-dimensional problems, Bayesian inverse frameworks for data assimilation, and rigorous analysis of convergence rates for uncertainty quantification methods. His collaborations with leading researchers (Schwab, Stuart, Xia) demonstrate strong interdisciplinary reach across applied mathematics and computational science. Information regarding scientific awards, student advising, research grants, and laboratory affiliations is not provided in the available text.

Stefan Van Aelstمشاهده پروفایل

استاد

• Robust statistics
• Analysis of complex data
• Functional data analysis
• +۵ مورد دیگر

Stefan Van Aelst is a Professor at the Department of Mathematics, KU Leuven, and heads the Statistics and Data Science unit. He is actively involved in research on robust statistical methods for complex and high-dimensional data. Affiliation: Faculty of Science, KU Leuven Research: Robust statistics, functional data, fuzzy data, and applications in chemometrics and bioinformatics Teaching: Advanced Statistical Methods, Robust Statistics, Statistical Data Analysis His recent publications focus on robust dispersion estimation, sparse logistic regression, functional data analysis, and multilinear PCA. Collaborations include researchers like Rousseeuw, Zamar, and Verdonck. He contributes to software development, including the MATLAB library LIBRA and R packages like robustbase and cellWise , and participates in organizing seminars and conferences.

Yasser Safaمشاهده پروفایل

پژوهشگر

• Computational Mechanics
• Multiphysics Modeling
• Solid Mechanics
• +۷ مورد دیگر

Dr. Yasser Safa is a Researcher at the Institute of Computational Physics (ICP) within the School of Engineering at Zurich University of Applied Sciences (ZHAW), leading the Multiphysics Modeling and Imaging research group. His work focuses on advanced computational techniques for solving complex engineering problems across energy systems, materials science, and mechanical design. His educational background includes a PhD from École polytechnique fédérale de Lausanne (EPFL) in 2005, where his thesis addressed numerical simulations of thermo-magneto-hydrodynamic phenomena in aluminum reduction cells. This foundation in coupled physics has driven his subsequent research in multiphysics modeling. Dr. Safa's research spans computational mechanics, solid mechanics (particularly nonlinear elasticity and buckling), corrosion engineering, additive manufacturing, metamaterials, and energy systems including fuel cells and wind power. His methodology emphasizes developing validated numerical models for real-world engineering challenges, often involving thin-film mechanics, material degradation, and structural integrity under extreme conditions. Recent work demonstrates increasing focus on renewable energy systems and advanced manufacturing processes. Analysis of his 15 most recent publications reveals strong thematic continuity in computational mechanics applied to energy and materials, with growing emphasis on wind energy systems and corrosion phenomena. His work consistently bridges theoretical mechanics with industrial applications, particularly in micro-fabrication and energy conversion technologies. He has secured and led multiple research projects including ongoing work on corrosion of multiphasic alloys and completed projects on airborne wind power systems, metamaterial wave guides, and additive manufacturing. These projects demonstrate his ability to translate computational expertise into practical engineering solutions across diverse domains. As part of ZHAW's ICP research group, Dr. Safa contributes to developing advanced computational tools for imaging and modeling physical systems, maintaining strong industry connections particularly in energy technology sectors. His current work continues to address critical challenges in material durability and energy system design through sophisticated numerical approaches.

• Neuroscience
• Neuroimaging
• Alzheimer's Disease
• +۷ مورد دیگر

Jace B. King is an Assistant Professor in the Department of Radiology & Imaging Sciences at the University of Utah School of Medicine . He holds multiple graduate degrees including a Ph.D. in Neuroscience , an M.B.A. from the David Eccles School of Business , and a B.S. in Psychology from the University of Utah. Ph.D. in Neuroscience - University of Utah School of Medicine M.B.A. - University of Utah David Eccles School of Business B.S. in Psychology - University of Utah Dr. King's research focuses on neuroimaging and brain connectivity across various conditions including Alzheimer's disease , autism spectrum disorder , and pediatric bipolar disorder . His work explores functional connectivity patterns , neuroanatomical differences , and biomarker correlations using advanced imaging techniques. Analysis of his publications reveals consistent work in Alzheimer's disease progression through motor task analysis and biomarker prediction (2021-2025), autism neuroconnectivity studies with longitudinal brain network assessments (2016-2024), and neurodevelopmental disorder research focusing on bipolar disorder and ADHD (2015-2023). His methodological approach combines fMRI , structural MRI , and electrophysiological analysis . Dr. King leads the Brain Network Lab , which conducts reproducibility studies for neuroimaging software and investigates functional connectivity patterns across multiple neurological conditions.

Whitney B. Popeمشاهده پروفایل

استاد

• Neuro-oncology
• Glioblastoma
• Magnetic Resonance Imaging (MRI)
• +۳ مورد دیگر

Whitney B. Pope, M.D., Ph.D., is a Professor in the Department of Radiological Sciences (Neuroradiology) at the David Geffen School of Medicine, University of California, Los Angeles. Her research focuses on neuro-oncology, glioblastoma, and advanced MRI techniques for tumor characterization and treatment response assessment. University: University of California, Los Angeles School: David Geffen School of Medicine Department: Radiological Sciences (Neuroradiology) Dr. Pope’s work explores the application of diffusion MRI, perfusion imaging, and molecular MRI techniques to predict outcomes in glioma patients. She has contributed extensively to understanding tumor heterogeneity, metabolic imaging, and the integration of AI in neuroimaging analysis. Recent publications highlight her research on synthetic perfusion MRI, T2-FLAIR mismatch quantification, and AI-driven tumor response assessment. Her studies often bridge imaging protocols with genetic and metabolic tumor profiling to optimize clinical decision-making.

• Machine Learning
• Medical Imaging
• Image Processing
• +۳ مورد دیگر

Jaejin Cho is an Assistant Professor in the Department of Artificial Intelligence and Robotics at Sejong University, where he conducts research at the intersection of machine learning and medical imaging. His work primarily focuses on advancing Magnetic Resonance Imaging (MRI) techniques through computational methods. His research interests span several key areas: Machine Learning : Network design, Domain transform, and Manifold learning. Image Processing : Numerical optimization, Annihilating filter, and Low-rank reconstruction. Medical Imaging : Fast MRI acquisition, Physics modeling, and MRI scanner operation. Dr. Cho's recent publications reveal a consistent focus on developing deep learning-based reconstruction algorithms for accelerated and distortion-free MRI. His work often integrates model-based approaches with deep learning, emphasizing zero-shot and self-supervised learning to avoid the need for large training datasets. Key application areas include quantitative MRI, diffusion imaging, and multi-contrast mapping, with a strong emphasis on clinical translation and cross-platform reproducibility. No scientific awards were mentioned in the available information. No information was available regarding advising activities, grants, or laboratory teams beyond his GitHub repositories which include tools for MRI reconstruction such as wave-modl and wave-qalas.

• Psychiatry
• Clinical Research
• Psychosis
• +۷ مورد دیگر

Simon Cervenka is a Professor of Psychiatry at Uppsala University and a Senior Physician at Uppsala Academic Hospital , specializing in Psychosis Research and Preventive Psychiatry . His career spans clinical practice, academic research, and teaching, with a focus on understanding early psychosis progression and developing preventive interventions. MD from Uppsala University (1999) PhD from Karolinska Institutet (2008) Specialist in Psychiatry (2009) Associate Professor at Karolinska Institutet (2016) Professor at Uppsala University (2021) His research investigates psychotic disorders through multidisciplinary approaches, including neuroinflammation , dopamine receptor mapping , and biomarker discovery in cerebrospinal fluid and blood. Key findings include complement system dysregulation and immune activation in early psychosis. Recent publications emphasize transdiagnostic risk prediction , longitudinal healthcare utilization patterns , and neurochemical heterogeneity in psychiatric conditions. He has contributed extensively to PET imaging methodology, particularly in dopamine and glial marker studies . His work bridges clinical psychiatry, neuroimaging, and immunopsychiatry, aiming to improve early detection , preventive strategies , and personalized treatment for psychosis and related disorders. He collaborates with institutions like Karolinska Institutet and participates in ENIGMA Consortium projects.

Tatjana Pavlenkoمشاهده پروفایل

استاد

• high-dimensional statistical inference
• statistical machine learning
• sparse signal detection
• +۴ مورد دیگر

Tatjana Pavlenko is a Professor in Statistics at Uppsala University, affiliated with the Department of Statistics. Her research bridges mathematical statistics, probability theory, and computational methods, focusing on high-dimensional data analysis in biomedical and machine learning contexts. Key research areas include: High-dimensional statistical inference and Bayesian graph structure learning Sparse signal detection and adaptive thresholding methods Statistical machine learning with applications to biomedical datasets Her recent publications demonstrate expertise in: Bayesian model averaging and junction tree sampling Asymptotic theory for high-dimensional classifiers Testing independence and covariance structures L2-type statistics and empirical process theory She supervises PhD students working on: High-dimensional causal inference in media Bayesian graphical models Sparse classification algorithms Currently active in Uppsala University's AI4Research initiative, Pavlenko develops adaptive data-driven procedures for statistical learning problems with complex sparsity patterns.

Vijay Narayananمشاهده پروفایل

استاد

• Power Aware Computing
• Computer Architecture
• Embedded Systems
• +۴ مورد دیگر

Vijay Narayanan is the Robert Noll Chair Professor in Computer Science & Engineering and Electrical Engineering at Pennsylvania State University. He co-directs the Microsystems Design Lab and leads research in embedded visual analytics, self-powered processors, and system design using emerging devices. Education: B.E in Computer Science and Engineering (1993) from University of Madras, India Ph.D. in Computer Science and Engineering (1998) from University of South Florida, USA His research spans Power Aware Computing , Computer Architecture , and Embedded Systems , with emphasis on Visual Cortex on Silicon and Self-Powered Processors . Current work includes Non-Volatile Processors and Design Automation under unreliable power conditions via NSF ERC ASSIST. Recent publications focus on GPU architecture (Tensor Cores, ACE), Memory Consistency Verification (QED), and Neural Radiance Fields (Disorf, Distwar) for robotics and rendering. Key collaborations include Tsinghua University and DARPA/SRC LEAST Center . Scientific Awards: IEEE Fellow ACM Fellow He leads the Architecture, Benchmarking and Circuits Thrust in the DARPA/SRC LEAST Center and contributes to NSF ERC ASSIST for self-powered systems. Grants and projects emphasize cross-layer optimizations and hardware-software co-design.

• Matrix and Tensor Factorizations
• Data Fusion
• Machine Learning
• +۳ مورد دیگر

Carla Schenker is a Postdoctoral Fellow in the Department of Data Science and Knowledge Discovery at Simula Metropolitan, specializing in advanced tensor decomposition methods for multi-modal data analysis. Her research bridges machine learning, optimization, and neuroimaging applications, with a focus on interpretable pattern discovery from complex datasets. Her educational background includes: PhD from Oslo Metropolitan University, Norway (Thesis: A Flexible Framework for Data Fusion Based on Coupled Matrix and Tensor Factorizations for Interpretable Pattern Discovery) Dr. Schenker's research centers on Matrix and Tensor Factorizations , where she develops constrained optimization frameworks for PARAFAC2 and coupled decompositions. Her work enables Data Fusion across dynamic and static sources, with critical applications in neuroimaging biomarker discovery and temporal pattern tracking . She pioneers methods for handling incomplete temporal data while maintaining model interpretability, advancing both theoretical foundations and real-world implementations in multi-way data analysis. Analysis of her 11 publications (2019-2025) reveals a clear evolution: early work established optimization frameworks for regularized tensor factorizations (2019-2021), while recent breakthroughs (2023-2025) focus on temporal dynamics, interpretable evolving patterns, and constrained PARAFAC2 variants. Her research consistently bridges Machine Learning theory with applications in neuroscience and signal processing, demonstrating increasing sophistication in handling heterogeneous, multi-modal datasets. No scientific awards are documented in available sources. Public records indicate no formal student advising or grant leadership, though her collaborative publications involve significant interdisciplinary partnerships with institutions like Oslo Metropolitan University and international research teams. As a core member of Simula Metropolitan's Data Science and Knowledge Discovery department, she contributes to Norway's national research infrastructure for digital engineering, working within teams focused on algorithmic innovation for complex data challenges in healthcare and industrial applications.

Tatyana Polenovaمشاهده پروفایل

استاد

• Solid-state NMR spectroscopy
• Structural biology
• Biophysics
• +۵ مورد دیگر

Tatyana Polenova is Professor of Chemistry at the University of Delaware, holding the prestigious C. Eugene Bennett Chair of Chemistry. She is a member of the Department of Chemistry and Biochemistry within the College of Arts & Sciences, where she directs the NIH-COBRE "Molecular Design of Advanced Biomaterials" program since 2014. Her academic journey began with a B.S. from Lomonosov Moscow State University in 1992, followed by a Ph.D. from Columbia University in 1997, and a postdoctoral position at Columbia University (1997-1999). She served as Assistant Professor at City University of New York-Hunter College (1999-2003) before joining the University of Delaware as Assistant Professor (2003-2006), then Associate Professor (2006-2011), and ultimately Professor (2011-present). Dr. Polenova's research focuses on understanding the structure, dynamics, and function of complex macromolecular assemblies using solid-state NMR spectroscopy combined with computational quantum-mechanical and biophysical methods. Her laboratory has made significant contributions to the field of biomolecular solid-state NMR, particularly in studying HIV-1 capsid protein assemblies, microtubule/cargo protein assemblies, vanadium haloperoxidases, intervertebral disc tissues, and oxoanionic solids. Her work bridges fundamental physical chemistry with applied biomedical research, seeking to develop novel magnetic-resonance-based diagnostic methods. She has pioneered the development of new solid-state NMR methodologies that provide unique insights into systems where other structural biology techniques fall short. Her extensive publication record, spanning from 2003 to 2025, demonstrates consistent leadership in the field of solid-state NMR applied to biological systems. Her most recent work shows a strong emphasis on pushing the technical boundaries of NMR through high-frequency magic-angle spinning, dynamic nuclear polarization, and fluorine-based methodologies to achieve unprecedented resolution and sensitivity for studying complex biomolecular assemblies, particularly viral structures and protein dynamics. C. Eugene Bennett Chair of Chemistry Dr. Polenova's research program involves extensive collaborations with computational scientists, virologists, and structural biologists. She directs the NIH-COBRE "Molecular Design of Advanced Biomaterials" program, which supports interdisciplinary research in structural biology and materials science. Her laboratory combines experimental NMR spectroscopy with molecular dynamics simulations and quantum chemical calculations to gain comprehensive insights into the structure-function relationships of complex biological systems. Her work has significant implications for developing novel therapeutic strategies and diagnostic methods for viral infections and other diseases.

• Signal Processing
• Machine Learning
• Optimization
• +۷ مورد دیگر

Hiroyuki Kasai is a Full Professor at the School of Fundamental Science and Engineering, Waseda University, where he leads research in signal processing, machine learning, and optimization. He holds a B.Eng. (1996), M.Eng. (1998), and Dr.Eng. (2000) in Electronics, Information, and Communication Engineering from Waseda University. His career includes positions as Associate Professor and Professor at the University of Electro-Communications (2007-2019), Senior Policy Researcher at Japan's Cabinet Office (2011-2013), and visiting roles at Technical University of Munich and British Telecom. His research spans: Fundamental methodologies : Riemannian optimization, stochastic gradient algorithms, tensor decomposition Applied domains : Network analysis, multimedia systems, environmental sound processing, video coding Emerging areas : Low-rank modeling, manifold learning, and large-scale anomaly detection His publications focus on efficient algorithms for high-dimensional data, with recent work emphasizing Riemannian manifold optimization and real-time tensor analysis. This includes development of open-source tools like SGDLibrary (MATLAB) and McTorch (PyTorch) for optimization tasks. Awards include: IEEE ICCE Best Paper Award (2011) Yamashita Memorial Award (2003) Ericsson Young Scientist Award (2001) 電気通信普及財団賞 (2015) IEICE Service Recognition Award (2010) He maintains memberships in IEEE, IEICE, IPSJ, and JSIAM, and has contributed to over 100 peer-reviewed publications with significant citation impact (h-index 27 via Google Scholar).

• Biomechanics
• Sports Science
• Exercise Physiology
• +۷ مورد دیگر

Hiroto Shiotani is a non-tenure-track Assistant Professor at Waseda University’s Faculty of Sport Sciences and an Adjunct Researcher at Aichi Medical University. Trained entirely at Waseda (BSc 2016 – PhD 2021), he applies advanced imaging and motion-capture techniques to unravel in-vivo mechanics of muscle-tendon units, the foot arch and the Achilles–plantar-fascia linkage. Education: PhD Sport Sciences, Waseda University, 2021 MSc Sport Sciences, Waseda University, 2018 BSc Sport Sciences, Waseda University, 2016 Research interests: His core interests span biomechanics, biodynamics, exercise physiology, sports science, ultrasonography, MRI, motion analysis, foot-and-ankle mechanics, skeletal muscle-tendon units and fascia. He combines diffusion-tensor MRI, shear-wave elastography and 3-D motion analysis to quantify how fascicles rotate and gear during passive lengthening, how Achilles tendon and plantar fascia mechanically interact, and how running induces site-specific adaptation or injury. Publication trends: Across 45 peer-reviewed works (h-index 7, 119 Scopus citations) his papers consistently integrate medical imaging with mechanical testing to clarify how human lower-limb tissues adapt to growth, sex, fatigue, footwear and repetitive loading. Recent articles highlight (i) 3-D fascicle gearing in gastrocnemius, (ii) bilateral asymmetry of plantar fascia in runners, (iii) acute mechanical decline of plantar fascia after long-distance running, and (iv) age-dependent neuromuscular strategies in children’s gait. Scientific awards: ISEK 2024 Family Grant Top Cited & Top Downloaded Article 2024 – Journal of Anatomy JSB Best Podium Presentation 2023 MSSE Featured Article 2023 Japanese Society of Biomechanics New Investigator Award 2022 (individual) and 2020 (shared) ISBS New Investigator Poster Award 3rd Place 2022 Hamano Yoshio Commemorative Award 2020 – Waseda University Several early-career awards from JSBiomechanics and JSTSS (2017-2019) Grants & advising: He currently leads a JSPS Early-Career grant (2023-26) on Achilles–plantar-fascia imbalance and running injuries, a Waseda special-project grant (2024-25) for cohort methodology development, and has completed five other university/JSPS grants since 2019. While specific student names are not listed, he supervises graduate seminars and master’s research at Waseda and has taught exercise physiology at Shibaura Institute of Technology. Professional service: Dr. Shiotani serves as proceedings reviewer for the International Society of Biomechanics in Sports (2023-25) and abstract reviewer for ISB congresses (2021-25). He is a member of seven international societies including ISB, ISBS, ISEK and ECSS.