جست‌وجوی استاد و پژوهشگر

Szymon Cyganمشاهده پروفایل

استادیار

• Strain Imaging Methods for Echocardiography
• Ultrasonic Imaging
• Biomedical Engineering
• +۵ مورد دیگر

Szymon Cygan, PhD, is an Assistant Professor and Deputy Dean for Students Affairs and Promotion at the Institute of Metrology and Biomedical Engineering , part of the Faculty of Mechatronics at Warsaw University of Technology. His work focuses on strain imaging methods for echocardiography, ultrasonic imaging, and biomedical engineering applications. Education: M.Sc. (2003) in Automatics and Robotics with Biocybernetics and Biomedical Engineering specialization; Ph.D. (2011) on displacements and strain estimation for ultrasonic strain imaging. Research: Specializes in speckle tracking echocardiography, left ventricular phantom modeling, finite element simulations, and validation of strain estimation algorithms using synthetic and physical models. Awards: No specific scientific awards explicitly mentioned. Projects: Involved in 2 projects, including development of low-cost gait analysis systems and synthetic cardiac phantom validation. Labs: Part of Krzysztof Kaluzynski's Lab, focusing on biomedical engineering and cardiac imaging.

Linus Wunderlichمشاهده پروفایل

مدرس

• Mathematical Finance
• Machine Learning
• Deep Neural Networks
• +۶ مورد دیگر

Dr. Linus Wunderlich is a Lecturer in Financial Mathematics at Queen Mary University of London (QMUL), affiliated with the School of Mathematical Sciences. He holds a PhD in Hybrid Finite Element Methods for Non-linear and Non-smooth Problems in Solid Mechanics from Technical University Munich. His research focuses on integrating machine learning with numerical techniques in mathematical finance, including solving high-dimensional PDEs via neural networks and accelerating risk computations through Chebyshev interpolation. He also explores the theoretical foundations of AI and its applications in finance, engineering, and education. Linus teaches courses such as Mathematical Tools for Asset Management and Masterclass in Business Analytics , emphasizing practical machine learning skills for business contexts. His work spans computational finance, numerical analysis, and interdisciplinary projects including Olympic medal prediction models and violin bridge modeling from CT scans. Research grants include a £10,800 project with Numerical Algorithms Group Ltd on efficient volatility interpolation (2018–2019). He collaborates with institutions like LSE, Birkbeck, and University College Dublin, presenting at conferences such as SIAM Financial Mathematics and the German Probability & Statistics Days. His activities include developing interactive art projects and teaching machine learning courses during the pandemic. No scientific awards are explicitly mentioned in the provided texts. His research interests and publications reflect a blend of mathematical rigor and applied innovation, bridging finance, engineering, and education.

Gerda Claeskensمشاهده پروفایل

استاد

• Model Selection
• Model Averaging
• Post-Selection Inference
• +۴ مورد دیگر

Gerda Claeskens is a full professor of Statistics at the Faculty of Economics and Business (FEB) at KU Leuven, Belgium. She holds positions in the Operations Research and Statistics Research Group (ORSTAT) and is affiliated with the Leuven Statistics Research Center. Her academic journey includes a Licentiate in Mathematics (Summa Cum Laude) from the University of Antwerp and a Ph.D. in Statistics from Limburgs Universitair Centrum (now Hasselt University). Research Interests: Her work focuses on model selection, post-selection inference, nonparametric methods, and high-dimensional statistics. She has contributed extensively to methodologies like focused information criteria, penalized splines, and quantile regression. Awards and Honors: Notable accolades include Fellow of the American Statistical Association (2019), Goodeve Medal (2017), and Medallion Lecturer (2016). She has also held editorial roles in top journals such as Biometrika and Journal of the American Statistical Association . Teaching and Mentoring: She has advised over 20 PhD students and mentored postdoctoral researchers. Her teaching spans advanced statistical methods, probability theory, and business statistics. She has delivered invited lectures globally, including at the European Meeting of Statisticians and the International Society for NonParametric Statistics. Key Contributions: Her book Model Selection and Model Averaging (2008) is a seminal work in statistical methodology. Her research addresses challenges in high-dimensional data, survival analysis, and model averaging, with applications in finance, insurance, and biostatistics.

Zhenyu Cuiمشاهده پروفایل

دانشیار

• Financial Engineering
• Insurance Analytics
• Operations Research
• +۵ مورد دیگر

Zhenyu Cui is an Associate Professor of Financial Engineering at the School of Business, Stevens Institute of Technology. He holds a PhD in Statistics from the University of Waterloo and a BS in Actuarial Science from the University of Hong Kong. His research focuses on financial engineering, insurance analytics, and operations research, with notable contributions to stochastic volatility models, Monte Carlo simulation, and financial systemic risk. He has published in top journals such as Mathematical Finance, SIAM Journal on Financial Mathematics, and European Journal of Operational Research. Education: PhD (2013, Statistics, University of Waterloo); MS (2010, Quantitative Finance, University of Waterloo); BS (2008, Actuarial Science, University of Hong Kong). Research Interests: Financial Systemic Risk, Monte Carlo Methods, Stochastic Volatility, Option Pricing, and Risk Management. His work bridges theoretical finance and practical applications, emphasizing robust pricing techniques and computational methods. Grants include leadership roles in NSF-funded projects on quantum algorithms for financial risk management and collaborations with institutions like Accenture and the Society of Actuaries. Awards include recognition for top-cited articles and editorial excellence.

Mats G Larsonمشاهده پروفایل

استاد

• Numerical Analysis
• Computational Mathematics
• Finite Element Methods
• +۳ مورد دیگر

Mats G Larson is a Professor at the Department of Mathematics and Mathematical Statistics at Umeå University. He holds the research qualification of Docent and specializes in computational mathematics, numerical analysis, and finite element methods. His work focuses on advancing numerical techniques for partial differential equations, including CutFEM, Isogeometric Analysis (IGA), and hybridized methods for complex geometries and multiphysics problems. Research interests include error estimation, stabilized finite element methods, computational mechanics, and applications in engineering and fluid-structure interaction. Larson leads projects such as the 2022–2025 'Multi-shell CutFEM for problems with mixed dimensions' and the 2017–2021 project on computational methods for elliptic problems. His publications appear in top journals like Computer Methods in Applied Mechanics and Engineering . Key contributions include developments in CutFEM for embedded surfaces, augmented Lagrangian methods for contact problems, and geometric modeling with CAD integration. His research bridges theoretical analysis and practical applications, addressing challenges in mesh generation, stability, and high-performance computing.

Ruth Milmanمشاهده پروفایل

دانشیار

• Systems Control Theory
• Model Predictive Control Systems
• Optimization
• +۳ مورد دیگر

Ruth Milman is an Associate Professor in the Department of Electrical, Computer and Software Engineering at the University of Ontario Institute of Technology. She holds a BASc (Honours Computer Engineering), MASc (Electrical Engineering), and PhD (Electrical Engineering) from the University of Toronto (1995, 1997, 2004 respectively). Her research focuses on advanced control systems, including Model Predictive Control (MPC), systems control theory, nonlinear control, constrained systems, and optimization. She has contributed to adaptive control of electromechanical systems and disturbance rejection in MPC frameworks. Her academic career includes teaching core engineering courses such as Discrete Math, Digital Systems, Probability and Random Signals, Linear Algebra, and Calculus. Her honors include multiple Ontario Graduate Scholarships (1998-2001) and the IEC Everitt Award (1994-1995) for excellence in communications systems. Dr. Milman's publications span over two decades, emphasizing algorithmic advancements in MPC and optimization. Recent work includes non-feasible active set methods for QP subproblems and transient response shaping in control systems. Technical reports highlight her contributions to pressure control and spline-based motor control strategies. Her expertise bridges theoretical control systems with practical engineering challenges, particularly in industrial and electromechanical applications. She maintains an active research agenda with a focus on real-time control systems and system robustness.

Thomas J.R. Hughesمشاهده پروفایل

استاد

• Isogeometric Analysis
• Computational Mechanics
• Finite Element Analysis
• +۷ مورد دیگر

Thomas J.R. Hughes is the Peter O’Donnell Jr. Chair in Computational and Applied Mathematics and a Professor of Aerospace Engineering and Engineering Mechanics at The University of Texas at Austin. He is affiliated with the Oden Institute for Computational Engineering and Sciences (Oden Institute), where he leads the Computational Mechanics Group. His research focuses on computational mechanics, isogeometric analysis, and biomedical modeling. Education: B.E. and M.E. in Mechanical Engineering from Pratt Institute; M.S. in Mathematics and Ph.D. in Engineering Science from the University of California, Berkeley. Research Interests include Isogeometric Analysis (integrating CAD and FEA), stabilized numerical methods for fluid flows, patient-specific biomedical simulations, and phase-field modeling. His work spans applications in cardiovascular systems, tumor growth, and geophysical flows. Hughes has authored over 150 journal articles, with recent contributions on isogeometric analysis, fluid-structure interaction, and computational geosciences. Notable awards include the von Neumann Medal (USACM), Gauss-Newton Medal (IACM), and Worcester Reed Warner Medal (ASME). He is a member of the National Academy of Engineering. His group advises students and postdocs in computational methods and collaborates with centers like the Oden Institute’s Computational Visualization Center. Current projects include isogeometric analysis, nanoparticle drug delivery, and ice sheet modeling.

Vladimir Kaishevمشاهده پروفایل

استاد

• Actuarial Science
• Risk Theory
• Reinsurance Modeling
• +۷ مورد دیگر

Prof. Vladimir Kaishev is a Professor of Actuarial Science at the Faculty of Actuarial Science and Insurance (FASI), Bayes Business School, City, University of London. He holds a PhD in Statistics and Information Theory from Moscow Technical University and has held visiting positions at Kyoto University, the University of Melbourne, and Heriot-Watt University. His research focuses on actuarial mathematics, risk theory, reinsurance modeling, and the application of spline functions in finance and insurance. He has supervised numerous PhD students and contributed to over 30 journal articles, conference papers, and software packages such as StMoMo and KSgeneral . His work includes advancements in ruin probability modeling, copula functions, and operational risk assessment, alongside consultancy roles for institutions like Swiss Reinsurance and the Bulgarian Actuarial Society. He is an Associate Editor of Economic Quality Control and a member of professional societies including the American Mathematical Society. Education: MSc and PhD in Statistics from Moscow Technical University, followed by postdoctoral research at the University of Wisconsin-Madison and UCLA. Research Interests: Multivariate Lévy processes, ruin theory, optimal reinsurance, spline functions, and stochastic modeling. His work bridges actuarial science with financial mathematics and statistical methods. Publications: Over 40 peer-reviewed articles and book chapters, with recent focus on generalized linear models, survival analysis, and actuarial software development. His 2023 paper in Applied Mathematics and Computation introduced novel variable knot spline methods. Awards: Received the Cass Business School Certificate for Excellence in Research in 2009 and 2013. Consultancy & Grants: Projects include stochastic mortality modeling for the UK Office for National Statistics and economic scenario generator development for Aon-Benfield. His research has been supported by grants from the Leverhulme Trust and the Swiss Re. Labs/Teams: Leads the Actuarial Research Center and collaborates internationally on projects such as the StMoMo R package for stochastic mortality modeling.

Hervé Cardotمشاهده پروفایل

استاد

• Functional Data Analysis
• Stochastic Algorithms
• Survey Sampling
• +۱۰ مورد دیگر

Hervé Cardot is a Professor of Statistics and team leader of the Statistics, Probability, Optimization and Control (SPOC) group at the Institut de Mathématiques de Bourgogne (IMB) , University of Burgundy. His research spans functional data analysis, stochastic algorithms for robust estimation, survey sampling methodology, and applied statistics across diverse fields. Research Interests : Functional data analysis, online principal component analysis, zero-inflated regression, survey sampling, nonparametric estimation Key Applications : Agriculture, climatology, energy consumption, food science, remote sensing, medical imaging Recent Work includes: Developing statistical frameworks for analyzing categorical trajectories using multivariate functional PCA (2025) Advancing bias-robust estimation for functional data in survey sampling (2020) Creating online stochastic algorithms for high-dimensional robust estimation (2017) Applying semi-Markov models to sensory analysis (2018) Teaching at the University of Burgundy includes courses on statistical modeling, big data analysis, and applied statistics for economics and psychology. He supervises PhD students in statistics and co-edits Statistics and Probability Letters .

• Biomechanics
• Mechanical Engineering
• Musculoskeletal System
• +۷ مورد دیگر

Denis Rancourt, a Professor at Université de Sherbrooke, specializes in biomechanics, mechanical engineering, and adaptive sports equipment design. His research spans musculoskeletal modeling, EMG-torque relationships, and innovative solutions for cancer therapy, vehicle safety, and Paralympic sports. Education: PhD (MIT, 1995), MSc (École Polytechnique de Montréal, 1989), BSc (Université Laval, 1986) Rancourt's work focuses on dynamic modeling of biological systems, vibration analysis, and biomedical device development. Recent projects include EMG-driven joint impedance estimation, tumor self-heating technologies, and wheelchair propulsion optimization. His publications highlight nonlinear tissue mechanics, Gaussian beam alignment algorithms, and ergonomic design innovations. Key grants include ATLAS structural lightweighting initiatives (2017-2021), cancer eradication research (2018-2020), and customized mattress technology (2016-2019), funded by NSERC and FRQNT. Collaborations span institutions like École Polytechnique de Montréal and industry partners including Bombardier Recreational Products. Scientific Awards Research Scholarship Award (1988) Dr. John Chapman Award General Motors Scholarship Award I.W. Smith Award for creative engineering Krashinsky Award Rancourt has mentored over 20 graduate students, including Stephane Martel and Francois Martel, across biomechanics, robotics, and materials science projects. His patents include EMG-based tumor heating devices and wheel chock systems.

• Virtual flow metering
• Surrogate modeling
• Mathematical programming
• +۴ مورد دیگر

Bjarne André Grimstad is an Associate Professor at the Norwegian University of Science and Technology (NTNU). His work focuses on virtual flow metering, surrogate modeling, and optimization of industrial systems. He has extensively explored hybrid modeling approaches combining data-driven and mechanistic methods, particularly through B-spline constraints and neural networks. His research spans mathematical programming, multi-task learning, and adaptive observer designs for gas-lift wells. His publications highlight advancements in virtual flow metering, including Bayesian neural networks, gray-box modeling, and nonstationarity handling. Key collaborations include Lars Struen Imsland, Odd Kolbjørnsen, and Bjarne Anton Foss. While no specific educational details are provided, his 2015 PhD thesis at NTNU established the foundation for his work in surrogate modeling and production optimization. Grimstad’s work trends involve integrating machine learning with industrial control systems, emphasizing practical applications in petroleum production. He has contributed to methods using B-splines for global optimization and divide-and-conquer strategies in production systems. Despite numerous publications, no scientific awards or student advising details are mentioned in the available text.

Nathaniel E Helwigمشاهده پروفایل

دانشیار

• Statistics
• Machine Learning
• Neuroscience
• +۳ مورد دیگر

Nathaniel E Helwig is an Associate Professor of Psychology and Statistics at the University of Minnesota, Twin Cities, affiliated with the Driven to Discover Research Facility (D2D). His research bridges statistical methodology and psychological inquiry, focusing on advanced analytical techniques. Research Interests Development of statistical learning frameworks Neuroscience applications through brain network analysis Computational modeling for behavioral and biological data Data analysis techniques for food choice and satisfaction Nonparametric regression and spline-based methods Dynamic modeling of physiological systems Grants & Collaborations NIH funding for visual field remapping in Central Scotomas (2020-2026) Alcohol use moderation studies in early adulthood (2019-2022) Intersegmental dynamic mechanisms of neck pain research (2019-2022) Neural disconnection and visual perception studies (2016-2022) Lab & Technical Contributions Developed Driven to Discover Research Facility (D2D) tools for data-intensive research Created open-access statistical software packages for tensor smoothing and permutation tests Applied cross-validated simultaneous component analysis for brain network modeling

• Civil Engineering
• Computational Fluid Dynamics
• Porous Media Flow
• +۳ مورد دیگر

Dr. Luka Malenica is a dedicated researcher at ETH Zurich's Professorship for Durability of Engineering Materials, focusing on computational modeling of material degradation processes in civil infrastructure. His work bridges theoretical numerical methods with practical engineering applications through direct involvement in the department's research operations. His research program centers on multiphase flow in porous media , corrosion mechanisms in reinforced concrete , and advanced numerical techniques . He employs pore-scale direct numerical simulations to investigate steel-concrete interface phenomena, developing predictive models for infrastructure durability. His methodological innovations include control volume isogeometric analysis, adaptive multiresolution modeling, and deep learning applications for adaptive meshing—addressing challenges in heterogeneous media flow and transport phenomena. Analysis of his 15 most recent publications reveals a consistent research trajectory since 2015, with accelerating output in corrosion science (5 papers in 2024-2025) and computational methods (7 papers 2019-2022). Key interdisciplinary connections span civil engineering (corrosion in reinforced concrete), chemical engineering (bubble column reactors), and hydrology (karst aquifer modeling), unified by his expertise in numerical simulation of transport phenomena. Recent work demonstrates increasing sophistication in modeling macrovoid formation at steel-concrete interfaces and capillary-driven multiphase systems.

Armin Iskeمشاهده پروفایل

استاد

• Numerical Approximation
• Applied Mathematics
• Kernel Methods
• +۴ مورد دیگر

Prof. Dr. Armin Iske is a Full Professor of Numerical Approximation at the University of Hamburg's Department of Mathematics, within the Faculty of Mathematics, Computer Science and Natural Sciences. He holds a PhD from the University of Göttingen (1994) and habilitation from TU Munich (2002). His research focuses on numerical approximation, kernel-based methods, computational fluid dynamics, and medical imaging. He has held academic positions globally, including visiting roles at ANU (Australia) and the University of Leicester (UK). Research interests include scattered data approximation, adaptive particle methods for flow simulation, and high-dimensional data analysis. He has authored 118+ publications, including works on kernel interpolation, medical imaging reconstruction, and machine learning applications. He serves on editorial boards for journals like Advances in Computational Mathematics and Sampling Theory . His contributions span interdisciplinary projects, such as SFB/TRR 181 on energy transfer in atmosphere and ocean, and collaborations in nanotechnology for brain interfaces. His work bridges theoretical mathematics with practical applications in engineering and biosciences.

• Machine Learning
• Medical Data Analysis
• Time-Series Data Analysis
• +۳ مورد دیگر

Dr. Weitong Chen is a Senior Lecturer at the School of Computer and Mathematical Sciences, University of Adelaide, and an ARC EC Industry Fellow at the Australian Institute for Machine Learning (AIML). He holds a PhD from the University of Queensland (2020), with prior roles as a Post-Doc Research Fellow and Associate Lecturer there. His research focuses on machine learning applications in medical data, particularly time-series analysis, semi-supervised learning, and IoT. He collaborates widely across academia, industry, and government, supported by multiple grants. His work emphasizes healthcare applications, adversarial robustness, federated learning, and unlearning mechanisms. Education: PhD in Machine Learning, University of Queensland (2020) Master's Degree, University of Queensland Bachelor's Degree, Griffith University Research Interests: Medical Data Analysis (e.g., EHRs, radiology) Time-Series Modeling (healthcare IoT, irregular data) Adversarial Machine Learning (backdoor attacks, robustness) Federated Learning (modality incompleteness, clustered frameworks) Data Privacy (unlearning, compliance) Grants & Collaborations: ARC EC Industry Fellowship Industry partnerships in healthcare and IoT Labs/Teams: Australian Institute for Machine Learning (AIML) Cross-disciplinary health tech collaborations