جست‌وجوی استاد و پژوهشگر

Alessio Maliziaمشاهده پروفایل

استاد

• Human-Centred Systems
• Ubiquitous Interactive Systems
• End-User Development
• +۳ مورد دیگر

Alessio Malizia is a Professor at the Faculty of Logistics, Molde University College (HiMolde), and concurrently holds a Professorship in User Experience Design at the University of Hertfordshire. His academic career spans roles at institutions like the University Carlos III of Madrid, Brunel University London, and Sapienza University of Rome. He is an ACM Distinguished Speaker and has research interests in Human-Centred Systems, Ubiquitous Interactive Systems, and hybrid physical-digital environments. His work integrates interdisciplinary collaboration and end-user development methodologies. Malizia’s research focuses on advancing technologies that blend digital and physical worlds, with contributions to wavelet transforms, signal processing, and image denoising. He has authored influential books and chapters, including works on time-frequency representations and sonar image processing. His industry experience includes roles at IBM and Silicon Graphics, alongside visiting research at Xerox PARC, where he explored neural networks and collaborative technologies. Key grants include projects with the European Space Agency (ESA) on sonar image fusion and ocean laboratory systems. His teaching spans Java programming, digital service design, and advanced signal processing techniques. Malizia has advised five doctoral students and currently mentors two, producing notable alumni like Prof. Corina Naforniță and Sorin Moga. His administrative roles include serving as Deputy Dean at Politehnica University Timișoara and Department Director at the University of Hertfordshire. Awards: ACM Distinguished Speaker Grants: ESA-funded projects on sonar imaging (2014–2016), IMFUSING (2014–2017) Labs/Teams: Active in interdisciplinary teams focusing on hybrid systems and end-user development

• Machine Learning
• Healthcare Analytics
• Agricultural Technology
• +۳ مورد دیگر

Dr. Yu Zhang is a Researcher at the School of Computer Science, University of Sheffield, affiliated with the Pervasive Computing research group. His work focuses on advanced machine learning techniques applied to healthcare and agricultural domains. He specializes in multi-task learning, spatio-temporal data analysis, and tensor methods. Key research areas include Alzheimer’s disease progression prediction using neuroimaging data and precision fertilization management through sustainable farming systems. His contributions bridge computational methods with real-world applications in healthcare analytics and environmental sustainability. Dr. Zhang’s research integrates temporal relation graphs, adaptive control systems, and interpretable AI frameworks to address complex challenges in disease modeling and agricultural optimization. He explores innovations like continuous incremental learning and randomized feature learning to enhance predictive accuracy and scalability. His work emphasizes practical applications, such as the ParallelFarm system for carbon-neutral farming and frameworks for medical decision-making based on MRI data analysis. His research trends show a strong focus on multi-task learning frameworks that adapt to temporal dynamics, with applications spanning healthcare biomarker identification, resource-efficient farming, and robust algorithm design. Collaborations likely involve interdisciplinary teams in computer science, neuroscience, and environmental science. Future directions may include expanding these methods to other healthcare domains and environmental sustainability initiatives. No scientific awards are explicitly mentioned in the provided text. Advising and grant details are not listed here, but his affiliations suggest involvement in collaborative research projects within the School of Computer Science. His work is rooted in the Pervasive Computing group, emphasizing embedded systems and AI-driven solutions for real-world problems.

• Algorithmic development
• Stochastic modelling
• Risk/reliability analysis
• +۹ مورد دیگر

Dr. Tanmoy Chatterjee is a Lecturer in Resilient Design at the University of Surrey's School of Mechanical Engineering Sciences, affiliated with the Surrey Institute for People-Centred AI and the Institute for Sustainability. Previously, he was a postdoctoral researcher at Swansea University (2018–2022), funded by the EPSRC 'DigiTwin' grant. His research focuses on algorithmic development, stochastic modeling, and machine learning for structural systems, with applications in nonlinear dynamics, metamaterials, and energy harvesting. Education: PhD in Civil Engineering (2014–2018), Indian Institute of Technology Roorkee Masters in Structural Engineering (2011–2013) Key Research Areas: Resilient design of structures under uncertainty Data-driven discovery of nonlinear dynamics Metamaterials for wave propagation control Machine learning for uncertainty quantification Publications: Recent work includes stochastic model updating, Bayesian inference for aeroelastic systems, and PINN-based reliability analysis. His 2025 paper on nonlinear structural joints introduced a novel framework combining experimental data with deep learning. Awards: Recipient of CSIR and Ministry of Education scholarships for his master's and PhD. Supervision: Co-supervised PhD students James Smith (University of Surrey) and Pushpa Pandey (Swansea University). Open to exceptional candidates for funded PhD projects in AI-driven engineering and resilient design. Labs/Teams: Active in Surrey's Centre for Engineering Materials and collaborations with industry partners like Autodesk and UK Atomic Energy Authority.

• Blockchain Applications
• Process Compliance
• Distributed Systems
• +۲ مورد دیگر

Walid Fdhila is a Researcher in the Security and Privacy group within the Faculty of Computer Science, focusing on blockchain applications and process management systems. His technical expertise spans distributed systems, privacy-enhancing technologies, and compliance verification. Research domains include: Blockchain-based audit systems for healthcare Privacy-preserving federated learning Cryptocurrency transaction anonymity Process compliance in decentralized environments Verification of distributed business processes He leads the 'Decentralized Trust in Digital Societies' project and develops applications of distributed ledger technology. Recent work creates frameworks for privacy-aware process execution across organizational boundaries.

Chenlei Lengمشاهده پروفایل

استاد

• Statistical Methodology
• High-Dimensional Statistics
• Network Analysis
• +۳ مورد دیگر

Professor Chenlei Leng is a faculty member in the Department of Statistics at the University of Warwick. Previously, he held positions at Peking University, the University of Munich, and the National University of Singapore. He earned a bachelor's degree in mathematics from the University of Science and Technology of China and a PhD in statistics from the University of Wisconsin-Madison. His research focuses on developing statistical methodologies for analyzing complex, high-dimensional data, including network analysis, longitudinal data modeling, and machine learning applications. He has organized workshops on statistical network analysis and co-directed the Oxford-Warwick Statistics Centre for Doctoral Training (CDT). Elected Member of the International Statistical Institute Fellow of the Institute of Mathematical Statistics His current research group at Warwick explores network structures and high-dimensional statistical challenges. Advising includes PhD candidates like Yuanhe Zhang and Xinyuan Fan, alongside visiting students from institutions such as Tsinghua University. He actively participates in academic leadership roles, including chairing the Research Section of the Royal Statistical Society. Publications span topics like network models, covariance estimation, and sparse regression, with a focus on methodological advancements in statistics and machine learning.

Pengtao Sunمشاهده پروفایل

استاد

• Computational Mathematics
• Numerical Solutions of Partial Differential Equations
• Finite Element Method
• +۵ مورد دیگر

Pengtao Sun is a Professor in the Department of Mathematical Sciences at the University of Nevada, Las Vegas (UNLV). His research focuses on computational mathematics, numerical solutions of partial differential equations, and scientific computing, with applications in fluid-structure interaction, fuel cell dynamics, and clean energy technologies. Supported by NSF since 2009, his work emphasizes advanced numerical methods like the finite element method, finite volume method, and domain decomposition techniques. He has contributed to modeling fluid-structure interactions using ALE and fictitious domain methods, as well as studies on lithium batteries and PEM fuel cells. His research also explores energy-preserving algorithms and machine learning approaches for complex systems. Dr. Sun’s expertise includes anisotropic/isotropic adaptive finite element methods, phase field methods, and the development of efficient solvers for multiphysics problems. His recent work addresses challenges in deterministic lateral displacement problems and thermal management of energy systems. He collaborates with the Center for Applied Math & Statistics (CAMS) at UNLV and has published extensively on topics like blood pressure prediction via fluid-structure interaction modeling and mesh-free neural network methods. His research portfolio reflects a blend of theoretical advancements and practical applications, bridging computational mathematics with engineering and environmental science. While no awards are explicitly listed, his sustained NSF funding underscores the significance of his work in advancing numerical methodologies for complex physical systems.

• Data Science & Business Analytics
• FinTech
• Artificial Intelligence
• +۴ مورد دیگر

ZHAO Rui is a Part-Time Lecturer at the School of Computing, National University of Singapore, where he teaches courses in business analytics and applied machine learning. Concurrently, he serves as the Head of Data & Quant at Pluang. He holds a Ph.D. from Nanyang Technological University and a B.Eng. from Southeast University, establishing a foundation in both theoretical and applied technical domains. His research spans interdisciplinary domains including: Core computational fields : Data Science, Machine Learning, and Artificial Intelligence with applications in Text Mining and Smart Manufacturing Industry-specific applications : Quantitative Finance and FinTech solutions Environmental informatics : Remote sensing and modeling of soil properties, carbon cycles, and land-use changes Publication analysis reveals three primary research arcs: 1) Foundational machine learning methods for document classification and industrial monitoring; 2) Geospatial modeling of soil properties using remote sensing across China; and 3) Ecosystem-scale studies of carbon dynamics and climate impacts. Recent work shows increasing focus on environmental applications while maintaining methodological rigor in AI. He leads courses including BT4012 Fraud Analytics and BT5153 Applied Machine Learning for Business Analytics. While the text mentions no current students, advised projects, or research laboratories, his industry role at Pluang suggests active engagement with fintech applications.

Alexander Bauerمشاهده پروفایل

پژوهشگر

• Deep Learning
• Computer Vision
• Natural Language Processing
• +۲ مورد دیگر

Alexander Bauer is a Postdoctoral Researcher in the Machine Learning Group at Technical University of Berlin, and a member of BASLEARN—a joint lab between BASF and TU Berlin focusing on machine learning applications. His academic background includes a PhD in Computer Science (2017) and a Diploma degree in Computer Science (2012), both from TU Berlin, alongside a B.Sc. in Mathematics (2011) from the same institution. His research interests span Deep Learning , Computer Vision , and Natural Language Processing . In Computer Vision, he specializes in visual anomaly detection , image classification , and semantic segmentation , with industry applications in automotive quality assurance. In NLP, he focuses on transformer architectures and foundation models for tasks like question answering and textual entailment. His earlier work centered on structured prediction , including optimization techniques for MAP inference in graphical models. His publications emphasize algorithmic efficiency and practical applications, with recent work exploring self-supervised learning and model interpretability. Bauer has contributed to both theoretical advances (e.g., exact inference algorithms) and applied solutions (e.g., anomaly detection in manufacturing). He collaborates with industry partners like BASF through BASLEARN, bridging academic research and industrial challenges. His technical expertise spans neural network design, optimization frameworks, and interdisciplinary problem-solving in vision and language domains.

Rakhim Aitbayevمشاهده پروفایل

دانشیار

• Numerical Methods for Partial Differential Equations (PDEs)
• Interface Problems
• Orthogonal Spline Collocation Method
• +۵ مورد دیگر

Rakhim Aitbayev is an Associate Professor of Mathematics at New Mexico Institute of Mining and Technology. His research focuses on numerical methods for solving partial differential equations (PDEs), particularly in interface problems, orthogonal spline collocation, iterative methods, and domain decomposition techniques. Research Interests: Numerical methods for interface boundary value problems Orthogonal spline collocation and multigrid methods Iterative solvers for large linear/nonlinear systems Applications to nonlinear elliptic and non-self-adjoint PDEs Finite element and Galerkin schemes Teaching: He has taught courses ranging from Calculus and Linear Algebra to advanced numerical methods, including Computational Fluid Dynamics, PDEs, and COMSOL modeling.

Wojciech L. Golikمشاهده پروفایل

استاد

• Numerical Analysis
• Computational Mathematics
• Computational Electromagnetics
• +۳ مورد دیگر

Dr. Wojciech L. Golik serves as Professor of Mathematics and Assistant Dean for Mathematical and Natural Sciences at Lindenwood University's College of Science, Technology, and Health. With a distinguished academic career spanning over three decades, he holds leadership responsibilities while maintaining active research and teaching commitments. His educational background includes a Master of Science in Mechanical Engineering from Poznan Technological University (1982), followed by an M.S. (1985) and Ph.D. (1988) in Mathematics from New Mexico State University. His doctoral dissertation focused on Convergence of the Boundary Integral Methods in Numerical Solutions of Fourier Problems . Dr. Golik's research centers on advanced computational methodologies, with particular expertise in numerical analysis, computational electromagnetics, and data science applications. His work demonstrates sophisticated integration of wavelet theory with electromagnetic modeling, developing innovative algorithms for solving complex boundary value problems and integral equations. The publication timeline reveals sustained contributions to computational mathematics, with recent expansion into data science and interdisciplinary applications. His 2013 Polish-language publication Siedemdziesiąt Trzy indicates broader intellectual interests beyond pure mathematics. Teaching responsibilities encompass foundational and advanced mathematics courses including Calculus sequences, Differential Equations, Linear Algebra, Numerical Analysis, and specialized topics like Computational Electromagnetics and History of Mathematics. His extensive course portfolio reflects deep expertise across both theoretical and applied mathematics domains. Professional activities include significant administrative leadership as Assistant Dean, though specific grant funding details aren't documented in available materials. His departmental involvement supports multiple undergraduate and graduate programs including Mathematics (BA/BS), Data Science, and Actuarial Science tracks within the College of Science, Technology, and Health.

Elmar Mertensمشاهده پروفایل

استاد پژوهشی

• Time series macro
• Bayesian econometrics
• Informational frictions
• +۱ مورد دیگر

Elmar Mertens is a Research Professor at the Research Centre of the Deutsche Bundesbank. His research focuses on macroeconomics, Bayesian econometrics, and monetary policy design. He holds an academic position within the Research and Research Data division, contributing to advancements in time series analysis and informational frictions. His work bridges theoretical econometrics with practical policy applications, particularly in addressing challenges like the impact of the effective lower bound on interest rates and modeling stochastic volatility in macroeconomic systems. His research interests include developing methodologies to handle outliers in Bayesian Vector Autoregressions (BVARs), a critical area intensified by the economic disruptions of the COVID-19 pandemic. He has also explored inflation dynamics, forecast uncertainty, and the role of imperfect information in macroeconomic models. His contributions span peer-reviewed journals and central bank publications, emphasizing rigorous statistical methods and their implications for policy. Mertens has presented at leading conferences such as the NBER Summer Institute and the ECB Conference on Forecasting Techniques. As an Associate Editor at the Journal of Money, Credit, and Banking, he actively participates in shaping econometric literature. His work reflects a commitment to advancing both theoretical frameworks and their real-world applications in central banking and economic policy.

• Algebra
• Differential Equations
• Geometry
• +۳ مورد دیگر

Jose Ferrer Llop is a Professor at the Department of Mathematics, Polytechnic University of Catalonia, affiliated with the Barcelona School of Industrial Engineering (ETSEIB). He leads research in the SCL-EG group (Linear Control Systems: Geometric Study) and collaborates with the Institute of Industrial and Control Engineering (IOC). Research Focus: His expertise spans algebraic structures, differential equations, geometric methods in control theory, and bifurcation analysis. Key domains include: Linear control systems and invariant subspaces Bifurcation theory in dynamical systems Matrix deformations and canonical forms Projects & Collaborations: He has contributed to competitive grants like the thematic network 'Red de Álgebra Lineal, Análisis Matricial y Aplicaciones' and 'Estructuras Geométricas de Sistemas de Control'. Collaborators include Marta Peña, Albert Compta, and Fernando Puerta. Educational Contributions: Authored pedagogical works such as 'Learning Engineering to Teach Mathematics' to integrate applied mathematics into engineering curricula.

Leon Bungertمشاهده پروفایل

استاد

• Machine Learning
• Applied Mathematics
• Partial Differential Equations
• +۵ مورد دیگر

Leon Bungert is a W2 Professor with Tenure Track to W3 Professorship for Mathematics III (Mathematics of Machine Learning) at the University of Würzburg, appointed since 2023. He leads the Mathematics of Machine Learning group within the Institute of Mathematics. His academic journey includes a Ph.D. (summa cum laude) from the University of Erlangen-Nürnberg in 2020, focusing on nonlinear spectral theory with variational methods, followed by postdoctoral research at TU Berlin, Bonn, and Erlangen-Nürnberg. Research Interests: Bungert's work bridges applied analysis and machine learning, emphasizing PDEs on graphs, adversarial robustness, inverse problems, optimization, and nonlinear eigenvalue problems. His research often employs variational methods and explores the intersection of geometry, probability, and numerical analysis. Notable areas include adversarial machine learning, Lipschitz learning on graphs, and regularization techniques for inverse problems. Professional Activities: He serves as a guest editor for the European Journal of Applied Mathematics , associate editor for Advances in Continuous and Discrete Models , and co-organizes major conferences like MIA'25 and SSVM 2025. His work has been published in top journals such as Annals of Applied Probability , Journal de Mathématiques Pures et Appliquées , and SIAM Journal on Imaging Sciences . Upcoming Engagements: Invited speaker and organizer at events including the IHP Paris conference (January 2025), Osaka workshop on machine learning and numerics (March 2025), and the BIRS workshop on adversarial machine learning (August 2025). His research team collaborates with institutions globally, including MIT, University of Cambridge, and University of Bonn. Lab/Team: The Mathematics of Machine Learning group at Würzburg focuses on theoretical and applied aspects of machine learning, with active projects in adversarial robustness, PDE-based optimization, and geometric data analysis. Bungert’s team includes researchers like Eloi Martinet and Tim Roith, advancing interdisciplinary work in mathematical foundations of AI.

Carlos JS Alvesمشاهده پروفایل

دانشیار

• Applied Mathematics
• Numerical Analysis
• Inverse Problems
• +۴ مورد دیگر

Carlos JS Alves is an Associate Professor at the Department of Mathematics, Instituto Superior Técnico, University of Lisbon. His research focuses on Applied Mathematics, Numerical Analysis, and Inverse Problems, with expertise in Meshless Methods and Wave Scattering. He has held academic positions since 1996, including roles as Head of the Department of Mathematics at IST (2005–2006). He earned his PhD from École Polytechnique (France) in 1995 and has been recognized with awards such as the 1st Prize in the UTL/Santander-Totta Scientific Awards (2009). Alves has supervised numerous PhD and MSc students, including Svilen Valtchev, Pedro Antunes, and Nuno Martins. His academic contributions include editorial roles in journals like Computers, Materials & Continua and co-editing books on Meshfree Techniques and Optimization in Medicine. His work spans projects funded by FCT and international collaborations, addressing inverse problems, numerical methods, and computational fluid dynamics. Key research interests include the Method of Fundamental Solutions (MFS), inverse source problems, and boundary integral equations. His publications explore applications in acoustics, elasticity, and wave propagation, with over 30 peer-reviewed articles in journals like Inverse Problems and SIAM Journal on Control and Optimization .

• Computational Mathematics
• Applied Mathematics
• Finite Element Methods
• +۵ مورد دیگر

Frédéric Valentin is a senior researcher at the National Laboratory for Scientific Computing (LNCC) in Brazil, where he led the Department of Computational and Mathematical Methods from 2015 to 2021. He also held an INRIA International Chair (2018-2023) and served on the applied mathematics board of Brazil's CNPq (2017-2020). Valentin currently leads the IPES Research Group and acts as the Brazilian scientific director for the Inria-Brasil partnership. Fields of Interest: Computational Mathematics, Applied Mathematics, Finite Element Methods, Domain Decomposition, Multiscale Modeling, High-Performance Computing, Machine Learning Valentin's research focuses on developing and analyzing numerical methods for multiscale phenomena in engineering and life sciences. He has published over 60 high-impact journal articles and book chapters, emphasizing local conservation properties, Petrov-Galerkin formulations, and parallel computing. His work bridges multiscale hybrid-mixed (MHM) methods with hybrid high-order approaches, enabling robust solutions for heterogeneous media and complex geometries. Scientific contributions include foundational work on MHM methods for Darcy, Stokes, and elasticity equations, with applications to heterogeneous coefficients and polytopal meshes. He co-developed error estimators for adaptive refinement and explored connections between stabilization and multiscale enrichment. Scientific Awards: INRIA International Chair (2018-2023) Valentin's group at LNCC collaborates internationally and contributes to Latin America's most advanced supercomputing center, which houses the Santos Dumont petaflop system. His work integrates numerical analysis with machine learning for next-generation computing architectures.