جست‌وجوی استاد و پژوهشگر

Ryan Browneمشاهده پروفایل

استادیار

• Model-based Clustering
• Classification
• Measurement Models
• +۴ مورد دیگر

Ryan Browne is an Assistant Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. He holds a BMath (2004), MMath (2006), and PhD (2009) from the same institution. His research focuses on model-based clustering , classification , and measurement system quality assessment , with applications in multivariate analysis and statistical inference. He is particularly known for contributions to mixture models and their computational optimization. Education: BMath in Statistics, University of Waterloo (2004) MMath in Statistics, University of Waterloo (2006) PhD in Statistics, University of Waterloo (2009) Ryan’s work emphasizes flexible statistical methodologies , including advancements in skewed distributions, high-dimensional data analysis, and robust algorithms for clustering and classification. He has received the prestigious 2011 W.J. Youden Award from the American Statistical Association for his PhD research on measurement system evaluation. His research trends span computational statistics (e.g., sketching algorithms for big data) and model-based clustering innovations (e.g., mixtures of generalized hyperbolic distributions). Recent work explores parsimonious models, nested Gaussian structures, and efficient parameter estimation for complex datasets. Key Awards: 2011 W.J. Youden Award (American Statistical Association) Ryan collaborates on applied projects, including industrial ecology and sensory data analysis. He has developed R packages like mixture and MixGHD , which implement his methodological contributions.

Grant Weddellمشاهده پروفایل

دانشیار

• Database Technology
• Real-Time Applications
• Schema Management
• +۵ مورد دیگر

Grant Weddell is an Associate Professor in the David R. Cheriton School of Computer Science at the University of Waterloo. His research focuses on database technology for real-time applications, including large-scale schema management, information clustering, dependency theory, and query optimization for heterogeneous data sources. He teaches courses such as CS338 (Introductory Databases), CS348 (Advanced Databases), CS446 (Software Engineering), and CS848 (Advanced Database Systems). His research interests emphasize the interplay between description logics and database systems, particularly in optimizing query processing and managing complex schemas. Recent work explores path agreements, functional dependencies, and ontology-mediated querying to enhance data integration and schema management efficiency. Teaching responsibilities include foundational database courses (CS338/348), software engineering (CS446), and advanced topics in information integration (CS848). No scientific awards are explicitly listed, though his contributions to database theory and optimization are extensive.

Chaopeng Shenمشاهده پروفایل

استاد

• Hydrology
• Deep Learning
• Physics-Informed Machine Learning
• +۹ مورد دیگر

Chaopeng Shen is a Professor in the Department of Civil and Environmental Engineering at Pennsylvania State University. His research bridges hydrology with state-of-the-art deep learning and differentiable modeling techniques, focusing on advancing our understanding of hydrologic cycles and their interactions with ecosystems, energy, and carbon cycles. He leads the Multi-scale Hydrology, Processes and Intelligence group (MHPI) and has developed the Process-based Adaptive Watershed Simulator (PAWS) for large-scale hydrologic modeling. Shen's work emphasizes physics-informed machine learning , where deep learning components are integrated with process-based equations through differentiable modeling. This approach enables training neural networks using big data while respecting physical laws, leading to improved generalizability and robustness. His group has demonstrated advantages of differentiable models in rainfall-runoff prediction, routing, ecosystem modeling, and water quality studies. Notably, his team's deepLDB project addresses landslide prediction using AI and big datasets. Recent publications highlight his contributions to global water modeling (grid-LSTM, differentiable Muskingum-Cunge routing), extreme flood forecasting (probabilistic diffusion models), and hydrologic uncertainty quantification . Shen actively engages in interdisciplinary collaborations through the PRISM Cooperative Institute, which aims to integrate multi-domain data for systemic risk assessment. His group has advised students including Dapeng Feng, Wen-Ping Tsai, Kuai Fang, Xinye Ji, and Tasnuva Mahjabin. Shen's research is supported by the National Science Foundation (NSF), Department of Energy (DoE), USGS, Google.org, and the Gates Foundation. He serves as Editor for the Journal of Geophysical Research - Machine Learning & Computation and Chief Editor for Frontiers in Water: Water & AI. His open-source software tools like PAWS and deepLDB are available through dedicated project websites.

Christoph Hertrichمشاهده پروفایل

استادیار

• Discrete Mathematics
• Theoretical Computer Science
• Machine Learning
• +۳ مورد دیگر

Christoph Hertrich is a tenure-track professor for Applied Discrete Mathematics at University of Technology Nuremberg, where he conducts research at the intersection of discrete mathematics, theoretical computer science, and machine learning. His work particularly focuses on applying polyhedral geometry and combinatorial optimization techniques to neural network theory, with significant contributions to understanding the computational complexity and expressivity of neural networks. Hertrich received his BSc and MSc degrees from TU Kaiserslautern (2013-2018) working with Sven O. Krumke, followed by his PhD at TU Berlin (2018-2022) under the supervision of Martin Skutella. His doctoral thesis, titled "Facets of Neural Network Complexity," laid foundational work for his current research direction. Prior to joining UTN, he held postdoctoral positions at Université libre de Bruxelles (2023-2024) with a Marie Skłodowska-Curie fellowship under Samuel Fiorini, and at LSE London (2022-2023) with László Végh. He also served as a substitute professor for discrete mathematics at Goethe-Universität Frankfurt during the winter semester of 2023/24. Hertrich's research interests center on the mathematical foundations of neural networks, with particular emphasis on polyhedral geometry approaches. His work explores computational complexity questions related to neural network training and architecture, expressivity bounds, and connections to combinatorial optimization problems. He has made significant contributions to understanding the relationship between neural network depth and function representation, the complexity of counting linear regions in ReLU networks, and the application of extended formulations to neural network theory. His approach combines rigorous theoretical analysis with practical implications for neural network design and optimization. His recent publication record reveals a strong trend toward establishing fundamental theoretical limits and connections between deep learning and discrete mathematics. A significant portion of his work examines computational complexity of various neural network problems, often proving hardness results or establishing bounds on expressivity. He has also developed novel connections between polyhedral combinatorics and neural network architecture, demonstrating how techniques from operations research can inform deep learning theory. Marie Skłodowska-Curie fellowship Since February 2025, Hertrich has been supervising PhD student Moritz Stargalla at UTN. His research has been supported by prestigious fellowships including a Marie Skłodowska-Curie fellowship during his postdoctoral period in Brussels. He is organizing a workshop on "Polyhedral Geometry for Neural Networks" in March 2026 in Nuremberg, highlighting his leadership in this emerging interdisciplinary field.

Christian Engwerمشاهده پروفایل

استاد

• Partial Differential Equations
• Numerical Analysis
• Finite Element Methods
• +۷ مورد دیگر

Christian Engwer is a full Professor at the University of Muenster in the Institute for Applied Mathematics, specializing in Analysis and Numerics. He leads the Engwer Group focused on Applications of Partial Differential Equations and is actively involved in the Cells in Motion initiative as a supervisor in the CiM-IMPRS Graduate Programme. His research centers on developing numerical methods for partial differential equations, particularly addressing challenges in complex geometries and multi-physics applications. He specializes in Unfitted Discontinuous Galerkin methods, which allow simulations on complex geometries without requiring domain-fitted meshes. His work spans porous media modeling, biological systems, and bioelectromagnetism applications, with significant contributions to EEG/MEG forward modeling in neuroscience. Analysis of his recent publications reveals a strong focus on model order reduction techniques, stabilized numerical schemes for cut-cell meshes, and applications in bioelectromagnetism. His work demonstrates a consistent trajectory toward developing robust, efficient numerical methods applicable to real-world problems in medical imaging and biological modeling, with increasing emphasis on high-performance computing implementations. Professor Engwer actively supervises doctoral students, with recent completions including Lukas Renelt (2025), Michael Wenske (2021), and Maria Carla Piastra (2019), among others working on topics related to numerical methods and biomedical applications. He leads several major research projects including BrainStorm: Highly Extensible Software for Advanced Electrophysiology and MEG/EEG Imaging (NIH-funded since 2019), multiple EXC 2044 Cluster of Excellence projects through 2025, and the InterKI interdisciplinary teaching program on machine learning and artificial intelligence. His group develops several important software packages including DUNE (Distributed and Unified Numerics Environment), duneuro (for bioelectromagnetism applications), and TPMC (Topology Preserving Marching Cubes). These tools support research in numerical methods and their applications to complex scientific problems.

• Fluid Mechanics
• Environmental Fluid Dynamics
• Industrial Fluid Dynamics
• +۳ مورد دیگر

Professor Steven Armfield is a faculty member at the University of Sydney's School of Aerospace, Mechanical and Mechatronic Engineering. He holds a BSc in Applied Mathematics from Flinders University and a PhD from the University of Sydney. His research focuses on fluid mechanics, particularly buoyancy-driven flows, computational modeling, and thermal convection in environmental and industrial contexts. He has led major projects on river management strategies, building ventilation systems, and large-scale fluid dynamics models. As FluD Director, he leads fluid dynamics research initiatives. Education: BSc Applied Mathematics, Flinders University PhD, University of Sydney Research Interests: Professor Armfield's work spans computational fluid dynamics (CFD), natural convection boundary layers, turbulent mixing in stratified flows, and heat transfer applications. His studies address environmental challenges (e.g., river stratification) and engineering systems (e.g., HVAC efficiency). He employs experimental, theoretical, and numerical methods to advance understanding of complex fluid behaviors. Publications: His recent work emphasizes parameterization of turbulent flows, buoyancy effects in stratified systems, and scaling laws for natural convection. Key themes include improving predictive models for environmental and industrial fluid dynamics. Awards: Australian National Research Fellowship Stanford University UPS Visiting Professorship Saitama University Visiting Scholarship Shundoh International Foundation Research Scholarship Advising & Grants: Supervises PhD students in topics like urban fluid dispersion and Navier-Stokes solvers. Secured funding for projects involving CFD analysis of data centers and thermal stratification in open channels. Labs/Teams: Led the School of Aerospace, Mechanical and Mechatronic Engineering (2008–2015) and currently directs the Fluid Dynamics (FluD) research group.

David de Sancho Sánchezمشاهده پروفایل

پژوهشگر ارشد

• Protein Folding
• Biomolecular Conformational Dynamics
• Computational Chemistry
• +۲ مورد دیگر

David de Sancho Sánchez is a Ramón y Cajal Research Fellow at the University of the Basque Country (UPV/EHU) and the Donostia International Physics Center (DIPC) in Spain. His research employs computational methods to study protein dynamics, folding mechanisms, and biomolecular interactions, with a focus on quantitative comparisons between theory, simulation, and experimental data. Education & Training: Biophysical Chemistry training at San Pablo CEU and Complutense Universities PhD under Antonio Rey at Complutense University Postdoctoral work with Victor Muñoz (Spanish Research Council) and Robert Best (University of Cambridge) Research Focus: David's work spans protein folding landscapes, metal-binding proteins, mechanical unfolding, and phase separation. Recent investigations include amyloid-beta aggregation, titin mechanics in cardiac disease, and computational drug design for cancer and neurodegeneration. His methodologies integrate molecular dynamics, Markov state models, and force spectroscopy simulations. Scientific Awards: Ramón y Cajal Fellowship (UPV/EHU) Ikerbasque Research Fellowship (CIC nanoGUNE) Affiliations: He leads computational research at UPV/EHU's Theoretical Chemistry Unit and collaborates extensively with experimental groups at DIPC, focusing on protein engineering and disease mechanisms.

Christian Zemanمشاهده پروفایل

مدرس

• Climate Modeling
• Weather Simulation
• Deep Convection
• +۳ مورد دیگر

Dr. Christian Zeman serves as a Lecturer at the Department of Environmental Systems Science, ETH Zurich, based at the Institute for Atmospheric and Climate Science (CHN N 17.1) in Zurich, Switzerland. His research focuses on high-resolution atmospheric modeling with emphasis on kilometer-scale climate simulations and deep convection processes. Zeman's work centers on advancing regional climate modeling capabilities through rigorous model verification techniques and convection-resolving simulations. His research addresses critical challenges in simulating complex phenomena like island-induced vortex streets, tropical climate dynamics, and the impacts of reduced floating-point precision on model accuracy. Key methodologies include ensemble-based statistical verification and systematic model calibration for improved predictive reliability. Analysis of his 2020-2025 publications reveals a consistent trajectory toward higher-resolution climate modeling, with significant contributions to model intercomparison frameworks and computational efficiency optimization. His research bridges theoretical atmospheric science with practical applications in regional climate projection, particularly for island systems and tropical regions. Zeman operates within Prof. Christoph Schär's research group at ETH Zurich, contributing to cutting-edge developments in the Consortium for Small-scale Modeling (COSMO) and Integrated Forecasting System (IFS) frameworks. His work supports ETH Zurich's leadership in high-resolution climate simulation and verification methodologies.

Anna Kuparinenمشاهده پروفایل

استاد

• Ecological Modeling
• Fisheries Science
• Aquatic Ecosystems
• +۳ مورد دیگر

Anna Kuparinen is a Professor at the University of Jyväskylä's Faculty of Mathematics and Science, Department of Biological and Environmental Science. Her research group, EcoEvoAqua, focuses on aquatic species and their ecosystems, particularly eco-evolutionary dynamics. Key research areas include ecological modeling, fisheries-induced evolution, food web stability, and environmental impacts on species survival. Recent work examines predator reintroduction effects, climate-driven selection pressures, and parasite-host interactions in aquatic systems. Publications span theoretical ecology, conservation biology, and fisheries management. Her research emphasizes interdisciplinary approaches to address complex ecological challenges. Education details are not explicitly listed, but her academic career reflects extensive contributions to aquatic ecology and conservation. Awards are not mentioned in the text, though her prolific publication record indicates recognition in her field. She leads the EcoEvoAqua research team, which integrates computational models with empirical data to study ecosystem resilience and biodiversity. Ongoing projects include modeling Lake Oulujärvi dynamics and exploring manganese toxicity in fish populations.

Nick Moseyمشاهده پروفایل

دانشیار

• Computational Chemistry
• Theoretical Chemistry
• Materials Science
• +۷ مورد دیگر

Nick Mosey is an Associate Professor in the Department of Chemistry and Associate Dean (Research) in the Faculty of Arts & Science at Queen’s University. He holds a PhD from the University of Western Ontario and completed postdoctoral research at Princeton University. His research focuses on computational and theoretical chemistry, particularly in developing simulation methods to study chemical reactions under mechanical stress and tribological conditions. Key areas include molecular dynamics, exact exchange calculations in periodic systems, and the design of electrocatalysts for fuel cells. His work integrates method development (e.g., contracted planewave basis functions, temporal QM/MM) with applied studies in tribology (e.g., friction modifiers, lubricants) and electrocatalysis (nickel-based materials for alkaline fuel cells). He has led interdisciplinary projects involving collaborations across chemistry, materials science, and engineering. Awards include the NSERC Doctoral Prize (2007), Early Researcher Award (2009-2014), and leadership in securing funding from NSERC and Ontario government programs. Mosey teaches courses in quantum mechanics and computational chemistry, with a focus on bridging theory and practical applications. His group has produced over 70 peer-reviewed publications and trained numerous graduate students and postdoctoral researchers. Current research emphasizes extending simulation time-scales for reactive systems and exploring novel materials for energy applications.

Fernando Camelliمشاهده پروفایل

دانشیار

• Computational Fluid Dynamics (CFD)
• Urban Environmental Modeling
• Contamination Dispersion Modeling
• +۳ مورد دیگر

Fernando Camelli is an Associate Professor in the Physics & Astronomy Department at George Mason University, holding dual roles as Instructional Faculty and Faculty. His research focuses on computational fluid dynamics (CFD), urban environmental modeling, and high-performance computing. He specializes in simulating complex fluid flows in urban environments, subway systems, and industrial applications, with particular emphasis on turbulence modeling, fluid-structure interaction, and GPU-accelerated algorithms. Key research areas include: CFD for urban airflow and contamination dispersion Meshless and immersed boundary methods Integration of geographic information systems (GIS) with CFD Large-scale simulations using parallel computing His work addresses practical challenges such as subway ventilation optimization, emergency contaminant dispersion prediction, and urban infrastructure design. Recent studies emphasize scalability improvements for fluid-structure interaction simulations and GPU-based code modernization.

• Psychometrics
• Network Theory
• Psychopathology
• +۲ مورد دیگر

Denny Borsboom is Professor in the Psychological Methods Group at the University of Amsterdam, where he directs the Social and Behavioural Data Science Centre. His research program develops network theories of psychopathology, novel psychometric methods, and formalized psychological theory through initiatives like the Psychosystems Project and Theory Methods Lab. Research focuses on conceptual foundations of measurement, complex systems approaches to mental disorders, and computational modeling of psychological phenomena. Recent publications advance network psychometrics, validity theory, and integration of intra/interindividual dynamics. Articles demonstrate strong methodological innovation through computational psychiatry applications, network theory development, and philosophical engagement with psychological foundations. Recurring themes include psychopathological networks, measurement realism, system transitions, and collaborative theory-building frameworks.

Georg Heinمشاهده پروفایل

استاد

• Algebraic Geometry
• Vector Bundles
• Moduli Spaces
• +۲ مورد دیگر

Prof. Dr. Georg Hein is a Professor in the Department of Mathematics at the University of Duisburg-Essen, Campus Essen. His office is located at WSC-O-3.58, Thea-Leymann-Str. 9, 45117 Essen, with office hours held every Tuesday from 2-3 PM and by appointment. He serves as Director of the Research Group Hein, focusing on Algebraic Geometry. His research centers on Algebraic Geometry with specific expertise in vector bundles, moduli spaces, elliptic surfaces, and sheaf theory. Hein's work demonstrates deep engagement with stability conditions, Fourier-Mukai transforms, and geometric invariant theory. His publications reveal a consistent focus on foundational structures in algebraic geometry, particularly through the lens of vector bundles on curves and surfaces. Analysis of his 15 most recent publications (2017-2007) shows a strong thematic continuity in moduli space theory and vector bundle classification. His work bridges abstract algebraic constructions with computational approaches, as evidenced by algorithmic developments for elliptic surfaces and lattice-theoretic investigations. The publications collectively emphasize stability criteria across diverse geometric contexts. Hein actively supervises PhD students including Asbjørn Michelsen, with former advisees Quyet Thang Truong and Dr. Dario Weißmann. He contributes to academic outreach through the Essen Math Circle for students, organizing weekly sessions for grades 5-13 covering advanced mathematical topics beyond standard curricula. His teaching portfolio includes courses such as Mathematische Miniaturen and Topologie .

Bassam Bamiehمشاهده پروفایل

استاد

• Control Systems
• Dynamical Systems
• Quantum Control
• +۷ مورد دیگر

Bassam Bamieh is a Professor of Mechanical Engineering at the University of California, Santa Barbara (UCSB), with affiliate roles in Electrical and Computer Engineering and the Center for Control, Dynamical Systems and Computation (CCDC). His research focuses on control systems, dynamical systems, and their applications in fluid mechanics, quantum control, and network science. He holds fellowships from IEEE and IFAC and has received accolades such as the NSF Early Career Award and IEEE Distinguished Lecturer designation. Education: B.Sc. in Electrical Engineering and Physics from Valparaiso University (1983), M.Sc. and Ph.D. in Electrical and Computer Engineering from Rice University (1986, 1992). Formerly an Assistant Professor at the University of Illinois at Urbana-Champaign (1991–98). Research interests span robust and optimal control, distributed systems, shear flow turbulence, and thermoacoustic energy conversion. He has authored over 200 publications and pioneered work in spatially invariant systems and network controllability. His teaching includes courses on linear systems, vibrations, and control systems design. Awards include the IEEE Axelby Award (twice), Hugo Schuck Best Paper Award, and recognition for student research mentorship (e.g., Outstanding Student Paper Awards at CDC and IFAC NecSys22). His group collaborates across disciplines, integrating mathematical analysis with engineering applications.

Stephan Raveمشاهده پروفایل

پژوهشگر

• Numerical Analysis
• Scientific Computing
• Machine Learning
• +۴ مورد دیگر

Dr. Stephan Rave is a Researcher in the Institute for Analysis and Numerics at the University of Münster. He is affiliated with the Applied Mathematics Münster cluster and serves as an Investigator in Mathematics Münster. His work focuses on numerical analysis, scientific computing, and machine learning, with a strong emphasis on model reduction techniques for complex systems. Education : PhD in Mathematics (2012), University of Münster, thesis on finitely summable K-homology. Master's and Bachelor's degrees in Mathematics from the University of Münster. Research Interests : Dr. Rave specializes in model order reduction (MOR) methods, including reduced basis techniques, localized orthogonal decomposition (LOD), and nonlinear approximation strategies. His work addresses challenges in multiscale modeling, domain decomposition, and parametrized partial differential equations. He also develops open-source software tools like pyMOR for MOR and contributes to initiatives like the MaRDI (Mathematical Research Data Initiative) to enhance interoperability in scientific computing. Projects : Key initiatives include the MaRDI project (2021–2026), EXC 2044 Cluster of Excellence (Geometry-based modeling), and MULTIBAT (lithium-ion battery simulation). His research bridges theoretical developments with practical applications in battery modeling, electrochemistry, and computational fluid dynamics. Grants & Awards : Funded by DFG, the German Federal Ministry of Research, and internal university grants, his work addresses strategic areas like sustainable research software and energy storage systems. He leads projects on distributed model reduction and communication-avoiding algorithms. Teaching : Dr. Rave teaches advanced numerical methods courses, including Model Order Reduction, Numerical Methods for PDEs, and Python-based computational labs. He co-organizes seminars and workshops on MOR and scientific software engineering.