جست‌وجوی استاد و پژوهشگر

Neil Reevesمشاهده پروفایل

استاد

• Digital Health Technologies
• Cybersecurity
• Diabetic Foot Ulcer Prevention
• +۳ مورد دیگر

Neil Reeves is a Professor of Secure Health Technologies at Lancaster University's Medical School. His work bridges digital health technologies and cybersecurity , focusing on diabetic foot ulcer prevention and gait improvement through smart medical devices. First proof-of-concept clinical trial on smart pressure-sensing insoles (Lancet Digital Health) Early clinical trial on vibrating insoles for balance improvement (Diabetes Care) EPSRC-funded Socksess project developing smart-sensing socks Co-developed FootSnap-AI app with Prof. Moi Hoon Yap Research spans AI-driven wearable technologies , medical device cybersecurity , and biomechanical interventions for chronic disease management. Recent publications focus on chronic wound segmentation , strain sensors , and plantar stress analysis . Scientific awards include the Oracle Innovator Accelerator Grant . He serves on international funding panels (EPSRC, NIH) and journal editorial boards (Gerontology, European Journal of Sport Science).

Gilad Lermanمشاهده پروفایل

استاد

• Machine Learning
• Data Analysis
• Computational Harmonic Analysis
• +۴ مورد دیگر

Gilad Lerman is a Professor at the School of Mathematics, University of Minnesota, and serves as Director of the Data Science Lab at both the Minnesota Center for Industrial Mathematics (MCIM) and Institute for Mathematics and its Applications (IMA). His office is located at 533 Vincent Hall, 206 Church Street SE, Minneapolis, MN 55455. Research Focus: Dr. Lerman specializes in computational harmonic analysis, high-dimensional data analysis, statistical learning, and machine learning. His work includes robust optimization techniques, non-convex modeling, and applications in computer vision and bioinformatics. Key methodologies involve geometric data analysis, variational methods, and spectral techniques for large-scale datasets. Publications: His recent works demonstrate consistent focus on robust machine learning, including subspace recovery, Riemannian data assimilation, graph neural networks, and applications in geophysics/computer vision. Theoretical rigor combines with practical implementations across domains. Student Advising & Grants: He has supervised 16+ PhD dissertations and 3 MSc theses, with graduates now in academia and industry (Google, NVIDIA, Mayo Clinic). Current research is supported by NSF and NGA funding. Laboratory Leadership: Directs the IMA Data Science Lab, focusing on industrial mathematics collaborations and developing algorithms for real-world data challenges.

• Machine Learning
• Quantum Machine Learning
• Neural Networks
• +۶ مورد دیگر

Sebastian Bugge Loeschcke is a PhD Fellow at the Machine Learning Section of the Department of Computer Science (DIKU), University of Copenhagen . His research spans theoretical and applied machine learning with focus on quantum machine learning, language modeling, and sustainability. Current affiliation: Machine Learning Section, DIKU Key research areas: Quantum-classical hybrid models, neural language processing, geospatial analysis Collaborative initiatives: SCIENCE AI Centre, TreeSense Centre Loeschcke's recent work includes Coarse-To-Fine Tensor Trains for compact representations and LoQT: Low-Rank Adapters for Quantized Pretraining , reflecting his focus on efficient neural architectures and quantum-inspired methods. His publications address cross-disciplinary challenges in climate modeling, healthcare, and quantum computing. Scientific contributions include: 2024: Tensor train compression methods for visual representations 2024: Low-rank adapter techniques for quantized models 2025: Quantum computing applications in molecular binding energy calculation 2025: Ethical frameworks for sustainable AI development 2025: Quantum dot array simulation tools (QDarts) Loeschcke contributes to interdisciplinary projects involving: TreeSense (remote sensing of global tree resources) Quantum computing optimization with Danish research consortia

• Machine Learning
• Medical Data Analysis
• Quantum Computing
• +۳ مورد دیگر

Simon Bartels is a researcher affiliated with the Department of Computer Science at the University of Copenhagen , contributing to the Machine Learning section. His work spans interdisciplinary applications of artificial intelligence, including quantum computing, healthcare diagnostics, and environmental modeling. Research activities at the department cover both theoretical and applied machine learning, with participation in the SCIENCE AI Centre . Key domains include medical data analysis , remote sensing , sustainability , and biological data modeling . Recent publications highlight contributions to quantum-inspired neural networks , geospatial biodiversity analysis , and energy-aware AI systems . Collaborations include rare disease research (e.g., MOSAIC framework ) and quantum computing optimizations.

• Quantum Physics
• Quantum Computing
• Quantum Information
• +۳ مورد دیگر

Refik Mansuroglu is a researcher affiliated with the University of Vienna. His work spans quantum computing, quantum information, and computational physics, with a focus on quantum simulation and algorithm development. University: University of Vienna Research Areas: Quantum Physics, Quantum Computing, Quantum Information Research Interests: Refik Mansuroglu specializes in quantum algorithms, Hamiltonian simulation, and quantum state optimization. His research explores classical-quantum hybrid methods, noise reduction in quantum systems, and applications of variational techniques to translational invariant and periodically driven systems. Additionally, he has contributed to foundational studies in loop quantum gravity, particularly in spin matter fields and discrete spacetime models. Article Trends: His recent work emphasizes variational algorithms for quantum simulation, error mitigation in near-term quantum devices, and classical optimization strategies for quantum circuits. Earlier publications delve into quantum gravity and photonic simulations, reflecting a multidisciplinary approach across quantum mechanics and computational physics. Advising & Collaborations: No specific students or advising roles are mentioned in the provided data.

• Applied Mathematics
• Computational Physics
• Scientific Machine Learning
• +۱۰ مورد دیگر

Lukas Exl is a Senior Lecturer at the University of Vienna and a Research Director at the Wolfgang Pauli Institute (WPI), where he leads the Mathematical AI/ML Research Division. He holds a habilitation (venia docendi) in Computational Science from the University of Vienna, the first in this interdisciplinary field. Research Platform MMM Mathematics-Magnetism-Materials Wolfgang Pauli Institute (WPI), Vienna His research integrates Applied Mathematics, Computational Physics, and Scientific Machine Learning, focusing on numerical methods for PDE-based simulations, data-driven modeling, and reduced-order approaches. Key applications include computational micromagnetism for green energy materials and developing physics-informed neural networks (PINNs) with interpretable architectures. Recent publications emphasize machine learning techniques for magnetic material optimization, stray field computation, and trustworthy AI (TAI/XAI). He supervises students in Computational Science, Applied Mathematics, and Physics, with a focus on Extreme Learning Machines (ELMs), PINNs, and tensor decomposition methods. Data-driven Reduced Order Approaches for Micromagnetism (FWF Project, €484k, 2024-2028) Design of Nanocomposite Magnets by Machine Learning (FWF Project, €254k, 2022-2027) Reduced Order Approaches for Micromagnetics (FWF Project, €402k, 2018-2024) His team includes researchers like Dr. Sebastian Schaffer (PhD graduate), Kein Gjordeni, and Caroline Maitz. Collaborations span Danube University Krems and Technical University of Denmark's Energy Conversion and Storage department.

Simonetta Liutiمشاهده پروفایل

استاد پژوهشی

• Theoretical Nuclear Physics
• Particle Physics
• Quantum Chromodynamics
• +۵ مورد دیگر

Simonetta Liuti is a Research Professor in the Department of Physics at the University of Virginia. She holds a Ph.D. in Physics from La Sapienza University (1989) and a Laurea in Physics from Università degli Studi di Perugia (1984). Her work focuses on theoretical nuclear and particle physics, particularly the internal dynamics of protons governed by quantum chromodynamics (QCD). Education Ph.D., Physics, La Sapienza University (1989) Laurea in Physics, Università degli Studi di Perugia (1984) Research Interests Professor Liuti specializes in understanding the proton's spin, mass, and mechanical properties through QCD. Her research explores the role of quark and gluon orbital motion, utilizing 3D parton distributions (Wigner distributions) and the Electron Ion Collider (EIC) to create tomographic images linking quantum phenomena to macroscopic matter. Her work has implications for neutron star physics. Scientific Awards & Recognition APS Fellowship (2017) SESAPS Francis Slack Prize (2019) DOE Topical Collaboration Award (2017, with student Abha Rajan) Physical Review C Editorial Board member (2017-2020) Service & Leadership Liuti has held leadership roles in the American Physical Society, including serving as Vice Chair of SESAPS (2012-2016) and organizing workshops to promote inclusion in physics. She contributes to institutional committees at UVA, such as the Physics Community and Outreach Committee and Grievance Committee.

Steven P. Meyersمشاهده پروفایل

استاد

• Neuroradiology
• Musculoskeletal Imaging
• MRI
• +۵ مورد دیگر

Steven P. Meyers, M.D., Ph.D., serves as a Tenured Professor of Radiology, Neurosurgery, and Otolaryngology within the Department of Imaging Sciences at the University of Rochester School of Medicine and Dentistry. He is an active member of the University of Rochester Medical Faculty Group (URMFG) and Accountable Health Partners (AHP), contributing to clinical care and academic leadership in medical imaging. His educational foundation includes: Medical degree from the University of Vermont College of Medicine (1986) Ph.D. from the University of Wisconsin Radiology Residency at Mount Sinai Medical Center-Cleveland (1986-1990) MRI Fellowship at the University of Pittsburgh Medical Center (1990-1991) Neuroradiology Fellowship at the University of Rochester (1993-1994) Dr. Meyers' research spans advanced neuroimaging, musculoskeletal MRI, and differential diagnosis methodologies. His work focuses on traumatic brain injury biomechanics, neurodegenerative disorders, spinal pathologies, and tumor imaging. He has pioneered multiparametric MRI applications for post-concussive syndrome and prostate cancer recurrence, while his seminal textbooks on differential diagnosis in MRI and CT remain global standards translated into seven languages. Analysis of his recent publications reveals a strong emphasis on quantitative MRI biomarkers for neurological conditions, machine learning applications in concussion assessment, and pattern recognition in rare diseases like progressive multifocal leukoencephalopathy. His work consistently bridges technical imaging innovation with clinical diagnostic utility across neurology, oncology, and orthopedics. His accolades include: PROSE Award (2016) for Best Book in Clinical Medicine Certificate of Merit from the Radiological Society of North America (2016) American College of Radiology Fellow designation (2012) Six faculty teaching awards including the Arvin E. Robinson Award and multiple Shu-Ren Lin Faculty Teaching Awards As an educator, Dr. Meyers founded and directed the Advanced MRI Fellowship Program (training 55 fellows since 1999) and led the Radiology Residency Program (2010-2017). His mentorship extends through textbook authorship and visiting professorships at leading institutions worldwide. While specific grant details aren't highlighted in source materials, his extensive publication record and program leadership indicate sustained research funding. He maintains active collaboration through professional societies including the American Society of Neuroradiology and International Skeletal Society. His clinical and research activities are supported by the Department of Imaging Sciences infrastructure, with emphasis on cross-departmental collaboration between Neurosurgery, Otolaryngology, and Orthopedics for comprehensive imaging solutions.

David A. Shrierمشاهده پروفایل

استاد بالینی

• Medical Imaging
• Diagnostic Radiology
• Neuroradiology
• +۳ مورد دیگر

David A. Shrier is a Clinical Professor at the University of Rochester School of Medicine and Dentistry , affiliated with the Department of Imaging Sciences . With a career spanning decades, he has contributed extensively to Medical Imaging , particularly in Neuroradiology and Diagnostic Radiology through research, teaching, and clinical practice. MD from Yale University School of Medicine (1984) Residency in Diagnostic Radiology (University of Rochester, 1985-1989) Fellowship in Neuroradiology (Brigham & Women's Hospital, 1989-1990) His research focuses on Central Nervous System Imaging , where he developed innovative techniques in Diffusion-Weighted MRI , Magnetic Resonance Angiography , and CT Angiography . His work explores vasculopathies , neurodegenerative patterns , and inflammatory CNS disorders , with recent applications to pandemic response radiology . His 45+ publications demonstrate a long-standing commitment to imaging technology validation and clinical translation , including methodological advances in ground-glass nodule tracking , vascular occlusion assessment , and pediatric airway imaging . Current research is centered on contrast-enhanced MRA techniques for improved vascular diagnostics. 1998: American Medical Association's Physician's Recognition Award 1981: Short Term Research Training Award 1981: UCLA Medical Center Auxiliary Scholarship 1980: Magna Cum Laude 1980: Sigma Xi Scientific Research Society 1979: Tau Beta Pi National Engineering Honor Society Dr. Shrier's expertise spans both clinical imaging and academic research , with board certifications in Diagnostic Radiology and Neuroradiology . His professional affiliations include the American Society of Spine Radiology , American College of Radiology , and Radiological Society of North America , reflecting his multidisciplinary contributions to medical imaging.

Lennart Binkowskiمشاهده پروفایل

پژوهشگر

• Quantum Computing
• Quantum Algorithms
• Theoretical Physics
• +۴ مورد دیگر

Lennart Binkowski is a doctoral candidate and scientific staff member at the Institute of Theoretical Physics , part of the Faculty of Mathematics and Physics at Leibniz University Hannover. His research focuses on quantum computing, particularly quantum algorithms and combinatorial optimization. University: Leibniz University Hannover School: Faculty of Mathematics and Physics Department: Institute of Theoretical Physics Email: lennart.binkowski@itp.uni-hannover.de Lennart's research interests span quantum algorithms, quantum walks, and optimization frameworks. His work explores quantum programming languages, Pauli transfer matrices, and hybrid quantum-classical systems. Recent publications highlight advancements in QAOA, quantum permutation generation, and tensor network applications. Lennart's 15 most recent articles focus on quantum computing trends, including algorithm design, constraint handling, and tensor structures. No scientific awards are mentioned in the provided data. Contact details: Schneiderberg 32, 30167 Hanover, Germany (Building 3702, Room 013).

Mark M. Churchlandمشاهده پروفایل

استاد

• Neuroscience
• Motor Control
• Neural Prosthetics
• +۳ مورد دیگر

Mark M. Churchland is a Professor of Neuroscience at Columbia University's Vagelos College of Physicians and Surgeons. He serves as Codirector of the Grossman Center for the Statistics of Mind and Principal Investigator at the Zuckerman Institute, working at the intersection of neuroscience and computational modeling. Research Focus : Churchland investigates how the brain plans, triggers, and executes voluntary movements. His work combines primate neurophysiology with dynamical systems analysis to uncover unified neural mechanisms across different movement types, with applications for Parkinson's disease and neural prosthetics. Scientific Contributions : 2017 BRAIN Initiative Team-Research Circuit Program (U19) for Motor Control Pioneering tensor analysis methods for neural population structure Discovering brain activity compression scales in movement preparation Developing brain-machine interface algorithms from cortical dynamics Recent Publications (2024-2025) reveal his lab's focus on: Neural null-space dynamics in movement Interdisciplinary neurotechnology (DREDge motion correction) Preparation-execution timing mechanisms Motor unit flexibility and variability Cortical prosthetic control improvements

Alperen Ergürمشاهده پروفایل

استادیار

• Real algebraic geometry
• Convex geometry
• Randomized numerical algorithms
• +۵ مورد دیگر

Alperen Ergür is an Assistant Professor at the University of Texas at San Antonio , affiliated with the College of Sciences and Department of Mathematics. His teaching integrates modern mathematical and computational skills, including courses on probability theory, optimization, algorithmic foundations of data science, reinforcement learning, and computational abstract algebra. Ph.D. in Mathematics from Texas A&M University M.S. in Mathematics from TOBB University of Science and Technology B.S. in Mathematics from Bilkent University Research spans real algebraic geometry , convex geometry , randomized numerical algorithms , and reinforcement learning . Key trends in his publications include: Polynomial system analysis Tropical geometry applications Tensor rank approximation Algorithm complexity in computational geometry Probabilistic methods in optimization Homotopy algorithms for real zeros He organizes the UTSA Algorithms Seminar and co-founded a student-friendly research seminar on Geometry, Probability, and Computing with Grigoris Paouris and Petros Valettas. With Suleyman Tek and Rodrigo Velez, he established a creative/competitive math circle for middle schoolers.

Chris Keylockمشاهده پروفایل

استاد

• Fluid Mechanics
• Turbulence
• Nonlinear Dynamics
• +۶ مورد دیگر

Professor Chris Keylock is a Professor of Fluid Mechanics at the University of Leicester. His research focuses on non-linear and complex phenomena, particularly in non-equilibrium turbulence dynamics. Key interests include the role of non-local effects in turbulent flows, synthetic data for null hypothesis testing, and multifractal methods. He has contributed to intermittency science, wavelet techniques, and closure models for computational fluid dynamics. Notable work includes studies on canopy turbulence (with Duke, MIT, and Western Australia) and boundary-layer modulation using Hölder exponents (with Southampton and Melbourne). Educations: MA, MSc, PhD Research emphasizes turbulence structure analysis, velocity gradient tensor dynamics, and applications in fluid mechanics, geomorphology, and hydrology. His work bridges statistical and physics-based perspectives, with interdisciplinary applications in environmental and engineering systems. Scientific Awards: 10th Fluid Dynamics Research Prize (2016) Advising and grants: No explicit mentions of advising students or grants in the text. His research groups and collaborations are active in fluid mechanics and turbulence, with contributions to international conferences and publications. Labs/Teams: Engaged with research groups focused on non-equilibrium turbulence, synthetic data generation, and fluid-structure interactions.

• Machine Learning
• Deep Learning
• Biomedical Signal Processing
• +۷ مورد دیگر

Yue Zhang is a Professor at Westlake University's School of Engineering with an exceptionally active research program spanning multiple disciplines. With dozens of publications in high-impact venues during 2025-2026 alone, Dr. Zhang demonstrates leadership in interdisciplinary research connecting computer science with practical applications in healthcare, environmental science, and education. Affiliation: Westlake University, School of Engineering Research Areas: Machine Learning, Biomedical Engineering, Computer Vision, Environmental Monitoring Publication Output: 90+ papers indexed in dblp (1996-2026), with remarkable productivity in recent years Dr. Zhang's research interests center on applying advanced machine learning techniques to solve real-world problems across multiple domains. The work demonstrates particular strength in biomedical signal processing (EEG, ECG, HRV analysis), environmental monitoring (mangrove forests, crop yield estimation, nitrogen content), and educational technology (AI integration in classrooms). The research approach typically combines deep learning architectures with domain-specific knowledge to create interpretable and effective solutions. The publication record reveals a strong trend toward multimodal and interdisciplinary approaches, with increasing focus on practical applications of AI in healthcare diagnostics, environmental conservation, and educational transformation. Recent work shows particular innovation in attention mechanisms, tensor-based optimization, and knowledge distillation techniques adapted for specialized domains. Dr. Zhang's collaborative network spans multiple institutions and disciplines, with frequent co-authorship patterns suggesting established research groups in both computer science and application domains. The work appears well-funded given the range of sophisticated applications and high publication output across top venues.

• Mathematical Optimization
• Discrete Mathematics
• Mathematics of Data Science
• +۳ مورد دیگر

Alberto Del Pia is a Professor in the Department of Industrial & Systems Engineering at the University of Wisconsin–Madison, where he has been a faculty member since 2014 and received tenure in 2020. His research focuses on mathematical optimization, discrete mathematics, and the mathematics of data science. He holds a Ph.D. in Mathematics from the University of Padova (2009) and has held postdoctoral positions at Otto von Guericke University, ETH Zürich, and IBM Research as a Goldstine Fellow. Educational Background: PhD in Mathematics (University of Padova, 2009); MS and BS in Mathematics (University of Padova, 2005 and 2004). Research Interests: Del Pia's work emphasizes theoretical and computational aspects of optimization, including convex quadratic programming, mixed-integer programming, and polynomial optimization. His contributions span the design of algorithms and polyhedral analysis, with applications in data science and operations research. Notable Awards: He has received the Egon Balas Prize (INFORMS Optimization Society, 2023), the INFORMS Computing Society Prize (2023), and the Young Researchers Prize (INFORMS Optimization Society, 2017). His postdoctoral work was supported by the IBM Herman Goldstine Fellowship (2013) and the INdAM Scholarship (2000). Teaching and Service: Del Pia teaches courses in integer optimization and linear optimization across multiple departments (Industrial & Systems Engineering, Mathematics, Computer Science). He has also organized conferences, such as the 2023 IPCO conference in Madison.