جست‌وجوی استاد و پژوهشگر

• Computational Mathematics
• Inverse Scattering
• Wave Propagation
• +۲ مورد دیگر

Alexander Mamonov is an Associate Professor in the Department of Mathematics at the University of Houston's College of Natural Sciences and Mathematics. He holds a Ph.D. in Computational and Applied Mathematics from Rice University and a Diploma with Distinction from Lomonosov Moscow State University. His research focuses on computational mathematics, inverse problems, wave propagation, and model order reduction. His research interests span computational mathematics, inverse scattering, wave propagation, model order reduction, and numerical linear algebra, with applications in geophysical imaging and scientific computing. Mamonov's publications demonstrate sustained focus on inverse problems and model reduction techniques, with recent work advancing tensor-based methods for parametric systems and data-driven approaches for waveform inversion. His articles consistently integrate mathematical theory with computational applications in wave propagation and imaging. Mamonov has received the ICERM Research Fellowship and maintains active research collaborations, including visiting positions at the University of Coimbra and ICERM. He coordinates the Seminar on Computational Mathematics in Oil & Gas Exploration and Imaging.

• Computational Engineering
• Numerical Methods for PDEs
• High-order Methods
• +۶ مورد دیگر

Matteo Giacomini is an Associate Professor of Computational Engineering at Universitat Politècnica de Catalunya (UPC), affiliated with the Laboratori de Càlcul Numèric (LaCàN). He is also an affiliated researcher at CIMNE (Severo Ochoa Excellence Centre) and affiliated faculty at IMTech (Institute of Mathematics of UPC-BarcelonaTech). His research focuses on numerical methods for PDEs, including high-order and low-order methods, error estimation, and reduced-order modeling. Applications span computational fluid dynamics, solid mechanics, image segmentation, and industrial sustainability. Education : PhD in Applied Mathematics, École Polytechnique (2016) MSc & BSc in Mathematical Engineering, Politecnico di Milano (2013 & 2010) Research Interests : High-order methods: finite element, discontinuous Galerkin Error & adaptivity: a posteriori estimates, mesh adaptation Dimensionality reduction: reduced order models, scientific ML PDE-constrained optimization: topology/shape optimization Software development: open-source CSE tools Recent Work Trends : Recent articles emphasize multi-fidelity surrogate modeling, domain decomposition for parametric PDEs, and robust finite volume methods for incompressible/compressible flows. He also contributes to HDG method implementations (e.g., HDGlab) and industrial applications of CFD. Labs & Affiliations : LaCàN - UPC CIMNE - Innovative Algorithms & Credible Data-Driven Models groups IMTech - Mathematical Modelling research line

Xiaodong Chengمشاهده پروفایل

استادیار

• Control Systems
• Optimization
• Machine Learning
• +۵ مورد دیگر

Xiaodong Cheng is an Assistant Professor at the Mathematical and Statistical Methods (Biometris) group in the Department of Plant Science at Wageningen University & Research. His research focuses on control systems, optimization, and machine learning, with applications in agricultural and energy systems. He holds a Ph.D. (cum laude) from the University of Groningen, under Prof. Jacquelien Scherpen, and prior roles include Research Associate at the University of Cambridge and Postdoctoral Researcher at Eindhoven University of Technology. Education: B.S. and M.E. from Northwestern Polytechnical University, China (2011, 2014) Ph.D. (cum laude) in Engineering from the University of Groningen, Netherlands (2018) Research Interests: Data-driven modeling, dimensionality reduction, learning-based control, system identification, and applications in agriculture and energy. He emphasizes practical implementations through tools like SYSDYNET and Bayesian neural ODEs for greenhouse systems. Key Contributions: His work spans model reduction for network systems, fault-tolerant control, and stochastic MPC for greenhouse production. Recent trends in his publications highlight advancements in resilient microgrid control, precision agriculture via drone-based sensing, and Bayesian methods for dynamic systems. Awards: Automatica Paper Prize Award (2017–2019) IEEE Transactions on Control Systems Technology Outstanding Paper Award (2020) Labs/Teams: Leads the Biometris group's efforts in integrating control theory and machine learning for sustainable systems. His work often collaborates with agricultural and energy sector stakeholders for real-world impact.

Karl Craryمشاهده پروفایل

دانشیار

• Programming Languages
• Security and Privacy
• Type Theory
• +۴ مورد دیگر

Karl Crary is an Associate Professor at the Computer Science Department of Carnegie Mellon University , where he also serves as Director of Doctoral Programs . His research focuses on Programming Languages , Security and Privacy , and Mechanized Metatheory , with emphasis on applying Type Theory to software verification and compiler design. Research Type Safety Proofs Certified Code Compiler Implementation His recent publications explore Substructural Parametricity , Hashgraph Consensus Verification , and Focused Logic applications. He has advised students including Derek Dreyer , Chris Martens , and Tom Murphy , with software projects like Istari proof assistant and CM-Lex/CM-Yacc for hygienic code generation. Current teaching includes Constructive Logic (15-317/657) and HOT Compilation (15-417).

Natalya Pya Arnqvistمشاهده پروفایل

دانشیار

• Statistical Regression Modelling
• Functional Data Analysis
• Shape Preserving Smoothing
• +۷ مورد دیگر

Natalya Pya Arnqvist is an Associate Professor in Mathematical Statistics at Umeå University, Sweden. She is affiliated with the Department of Mathematics and Mathematical Statistics and has active roles in research groups focused on Functional Data Analysis, Semiparametric Regression, and Statistical Learning for Spatio-Temporal Data. Her career includes prior positions at Nazarbayev University (2015-2020), University of Bath (2011-2015), and KIMEP University (2000-2005). Education : PhD in Statistics (University of Bath, UK, 2007-2010), CSc in Physical and Mathematical Sciences (Institute for Mathematics, Kazakhstan, 2000-2005). Her research centers on statistical regression modelling and functional data analysis , with significant contributions to shape constrained additive models (SCAMs) and model-based functional clustering . She has developed R packages such as scam , fdaMocca , and nilde for these methodologies. Recent publications highlight applications in applied demography , defect detection , and nonlinear state space modeling , reflecting her focus on bridging statistical theory with industrial and ecological challenges. She teaches undergraduate and graduate courses in probability, regression, and statistical learning.

Michael McDermottمشاهده پروفایل

استاد

• Biostatistics
• Clinical Trials Methodology
• Statistical Inference
• +۳ مورد دیگر

Michael McDermott, PhD, is a Professor of Biostatistics, Neurology, and the Center for Health + Technology at the University of Rochester Medical Center. He holds an academic appointment in the Department of Biostatistics and Computational Biology. His research focuses on statistical methodology including order-restricted inference, clinical trial design, diagnostic test evaluation, and meta-analysis. Dr. McDermott has collaborated extensively on neurological disorders such as Parkinson’s, Huntington’s, and multiple sclerosis, and has a joint appointment with the Department of Neurology. Educated at the University of Rochester (PhD, 1989), his work bridges statistical theory and clinical application. He has authored influential papers on hypothesis testing under order constraints and developed methods for verification bias correction in diagnostic studies. McDermott serves on editorial boards for journals like International Statistical Review and Movement Disorders , and has held leadership roles in professional societies. His research interests span statistical theory (e.g., multivariate analysis, ROC curves) and applied biostatistics (e.g., dose-response modeling, clinical trial optimization). McDermott’s methodological contributions address challenges in medical research design, including adaptive trial phases and missing data imputation techniques. He has been recognized as a Fellow of the American Statistical Association and an Elected Member of the International Statistical Institute. Collaborative efforts include leading national/international groups studying neurological diseases and serving as an advisor for the T32 Training Grant in Biostatistics. His lab focuses on advancing statistical tools for translational medical research while maintaining active involvement in neurology-related clinical studies.

• Mathematical Physics
• Quantum Mechanics
• Information Geometry
• +۱۷ مورد دیگر

Florio Maria Ciaglia is a researcher in the Department of Mathematics at Carlos III University of Madrid (UC3M), affiliated with the Applied Mathematics, Control Systems, and Signals research group. His work lies at the intersection of mathematical physics, quantum mechanics, and information geometry, with a focus on geometric and algebraic structures in quantum theory. His research interests include quantum mechanics (particularly Schwinger's picture) , information geometry , groupoids and higher categorical structures , Jordan and C*-algebras , and geometric methods in quantum information . He investigates the differential geometric foundations of quantum and classical states, monotone metrics, parametric estimation, and the role of symmetry and causality in quantum dynamics. His work often bridges abstract mathematics with foundational physical concepts. The trends in his recent publications reveal a deep and consistent exploration of geometric formulations of quantum mechanics , especially through groupoids and algebraic structures, alongside information-theoretic approaches in quantum theory . He frequently publishes in journals such as Journal of Geometry and Physics , Entropy , and International Journal of Geometric Methods in Modern Physics , reflecting a strong emphasis on mathematical rigor and physical interpretation. He has led significant research projects including: QUITEMAD-CM - QUITEMAD (2025–2028), funded by Comunidad de Madrid Classical and Quantum Information Theory and Functional Analysis: Foundations and applications (2021–2024), funded by CAM Groupoids, Von Neumann Algebras and the Mathematical Foundations of Quantum Mechanics (2021–2025), funded by AEI These projects underscore his leadership in advancing the mathematical foundations of quantum information and quantum mechanics. He collaborates within interdisciplinary teams focusing on applied mathematics and quantum systems. While no formal students or awards are listed in the provided text, his extensive publication record and principal investigator roles indicate a significant research impact.

• Mathematical Optimization
• Algorithmic Game Theory
• Multi-Agent Systems
• +۳ مورد دیگر

Max Klimm is an Assistant Professor for Discrete Optimization at Technische Universität Berlin, affiliated with the Department of Mathematics within Faculty II – Mathematics and Natural Sciences. He holds a PhD in Mathematics from TU Berlin (2012) and previously served as an Assistant Professor for Operations Research at Humboldt-Universität zu Berlin (2014–2016) and led the Junior Research Group for Optimization under Uncertainty at the Einstein-Center for Mathematics (2014–2016). His research focuses on algorithmic game theory, mathematical optimization of multi-agent systems in traffic, telecommunications, and economics. Key areas include network flows, congestion games, mechanism design, and infrastructure networks. He actively contributes to academic activities as an associate editor for journals like International Journal of Game Theory and Operations Research Forum , and has served on program committees for conferences such as AAAI, EC, and WINE. Recent work includes studies on evolutionary road network reconstruction, robustness of potential-based flows, and algorithmic solutions for knapsack and assignment problems. His research bridges theoretical foundations with practical applications in transportation and resource allocation.

Dennis Hohlfeldمشاهده پروفایل

استاد

• Silicon-based microsystem technology
• Energy-autonomous systems
• Energy Harvesting
• +۳ مورد دیگر

Prof. Dr.-Ing. Dennis Hohlfeld serves as Professor and Chair of Micro- and Nanotechnology of Electronic Systems at the University of Rostock's Institute of Device Systems and Circuit Technology. He holds key administrative roles as Study Advisor and Chairman of the Computational Science and Engineering (CSE) Examination Board, while actively contributing as a reviewer for research funding organizations and scientific publishers. His research spans five core domains: Silicon-based microsystem technology focusing on nanoscale material behavior for high-performance electronics Energy-autonomous systems leveraging environmental energy sources through harvesting techniques Energy-efficient circuit design for miniaturized applications Microoptics development for photonic integration Modeling and simulation techniques enabling system-level analysis of complex multiphysics phenomena Analysis of his 15 most recent publications reveals a dominant trend toward biomedical energy solutions, particularly thermoelectric generators for implantable devices. His work demonstrates strong interdisciplinary integration across electrical engineering, materials science, and neuroscience, with increasing emphasis on model order reduction for efficient simulation of MEMS devices and optogenetic applications. The publications consistently address miniaturization challenges while maintaining system-level performance. No scientific awards were documented in the provided materials. As Study Advisor and Examination Board Chairman for CSE, Prof. Hohlfeld oversees academic progression for computational science students. His research leadership encompasses: Technical Domains Energy harvesting systems for medical implants Photonic integration in microelectronics Multiphysics modeling frameworks Application Areas Autonomous environmental sensors Neuroprosthetic interfaces Power-efficient microsystems The Institute of Device Systems and Circuit Technology houses specialized laboratories for microsystem fabrication, energy harvesting characterization, and optical circuit development, supporting his research in silicon-based nanotechnology and energy-autonomous systems.

Jonathan Michael Kressمشاهده پروفایل

دانشیار

• Mathematical Physics
• Superintegrable Systems
• Differential Geometry
• +۱ مورد دیگر

Jonathan Kress is an Associate Professor in the School of Mathematics and Statistics at the University of New South Wales (UNSW). His research focuses on mathematical physics, particularly superintegrable systems in classical and quantum mechanics, differential geometry, and symmetries of partial differential equations. He has been with UNSW since 2001, progressing from Associate Lecturer to his current position. Education: PhD in Mathematics (University of Newcastle, 1998) B.Sc. (Hons.) (University of Adelaide, 1991) Research Interests: His work explores the algebraic and geometric foundations of superintegrable systems, separation of variables techniques, and applications to physical theories. Key areas include: classification of superintegrable systems, symmetry algebras, and connections to conformal geometry and twistor theory. Publications: His recent publications demonstrate a strong focus on developing classification theories for superintegrable systems using algebraic geometric methods, extending these concepts to higher dimensions and non-conformal spaces. Work frequently appears in leading mathematical physics journals. Awards and Fellowships: UNSW Fellowship in Innovative Teaching and Educational Technology (2003) University of Newcastle Postgraduate Research Award (1994-1996) Supervision and Grants: Supervised PhD student Joshua Capel (completed 2014). Received multiple UNSW Faculty Research Grants including: Polynomial algebras of superintegrable systems (2010) Nonlinear algebras in classical/quantum systems (2005) Closure of Symmetry Algebras (2004) Quadratic Algebras (2003)

Giulia Sambataroمشاهده پروفایل

پژوهشگر

• Numerical methods for partial differential equations
• Model order reduction
• Data-based methods
• +۷ مورد دیگر

Giulia Sambataro is a Postdoctoral Researcher in Applied Mathematics at Friedrich-Alexander-Universität, Erlangen (Germany), collaborating with Prof. Enrique Zuazua. Previously, she worked as a postdoctoral researcher at École Nationale des Ponts et Chaussées (Marne La Vallée, France) with Prof. V. Ehrlacher. She holds a PhD in Applied Mathematics from the University of Bordeaux, France, where her doctoral thesis focused on component-based parametric model order reduction for thermo-hydro-mechanical systems in geological radioactive waste storage. Her research spans several interconnected areas: Computational mathematics : Developing numerical methods for nonlinear partial differential equations and variational inequalities Model reduction : Creating efficient component-based techniques for multi-physics systems Data-driven modeling : Integrating machine learning with traditional simulation methods for complex systems Key research applications include: Thermo-hydro-mechanical behavior modeling for geological radioactive waste repositories (collaboration with French National Agency for Radioactive Waste Management) Hybrid reduced-order models for crowd dynamics and contact mechanics Optimization-based solutions for particle systems with obstacles

• Multidisciplinary Design Optimization (MDO)
• Aerospace Engineering
• Surrogate Models
• +۶ مورد دیگر

Nathalie Bartoli is a Senior Research Scientist (DR2) at ONERA and a Professor at ISAE-SUPAERO. She holds a Habilitation degree (HDR) in Applied Mathematics and specializes in Multidisciplinary Design Optimization (MDO), surrogate modeling, and Bayesian optimization. Her work focuses on aerospace applications, particularly aircraft design and optimization algorithms. She leads the Multidisciplinary Methods and Integrated Concepts Unit (DTIS/M2CI) at ONERA and teaches courses on optimization and MDO at ISAE-SUPAERO. Her research interests include surrogate models (Kriging/Gaussian Processes, mixture of experts), Bayesian optimization, and data fusion techniques. She is a member of the AIAA MDO Technical Committee and has organized workshops such as the MDO PhD Day (2019) and European MDO Workshops (2019, 2020). She has supervised over 25 PhD students and co-authored the SMT Python toolbox for surrogate modeling. Recent achievements include keynote speaking at NATO conferences, winning AIAA MDO Best Paper Awards (2022, 2020), and contributions to the AGILE H2020 project. Her work integrates manufacturing, supply chain, and aircraft design through MDO frameworks, addressing challenges in green aviation and hybrid propulsion systems. She collaborates internationally with universities like McGill and TU Delft, and industries like Airbus and DLR. Awards include the 2018 ICAS Award for AGILE project contributions and multiple best paper recognitions. Her research bridges theoretical optimization methods with practical aerospace engineering, emphasizing scalable algorithms for high-dimensional problems and industrial applications.

• Computational Fluid Dynamics
• Aerospace Engineering
• Data-driven Modeling
• +۴ مورد دیگر

Rohit Deshmukh is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at the University of Central Florida. He specializes in computational aerospace sciences, with expertise in data-driven modeling, multiphysics simulations, and high-speed fluid-structure interactions. Education: B.Tech and M.Tech in Mechanical Engineering – Indian Institute of Technology, Madras (2010) Ph.D. in Aerospace Engineering – The Ohio State University (2016) Research Interests: Dr. Deshmukh's research focuses on advancing computational methods for aerospace applications. His work encompasses data-driven modeling techniques that leverage machine learning for complex system analysis, multiphysics simulations for coupled phenomena, and digital twin technologies for real-time system monitoring. He is particularly interested in high-speed fluid-structure interactions , where he investigates the dynamic coupling between aerodynamic forces and structural responses in supersonic and hypersonic regimes. Additionally, his work on scientific data compression and analysis aims to efficiently manage and extract insights from large-scale simulation datasets. Publications Overview: His recent publications demonstrate a consistent focus on computational methods applied to high-speed aerodynamics and fluid-structure interactions. The works span from fundamental studies on turbulence effects in aeroelastic systems to advanced numerical techniques for reduced-order modeling. Notable contributions include analyses of shock-induced vibrations, efficient algorithms for turbulent flow representation, and sparse coding approaches for model order reduction in fluid dynamics. Research Positions: Postdoctoral Scholar – The Ohio State University (2016-2018) Research Scientist – The Ohio State University (2018-2023) Assistant Professor – Florida Atlantic University (2023-2024) Assistant Professor – University of Central Florida (2024-present)

Yiming Xuمشاهده پروفایل

استادیار

• Mathematics of Data Science
• Random Sampling and Approximation
• Statistical Ranking
• +۳ مورد دیگر

Yiming Xu is an Assistant Professor in the Department of Mathematics at the University of Kentucky. He holds a Ph.D. from the University of Utah, completed a postdoc at the University of Waterloo, and worked as a Quantitative Analyst at Wells Fargo Bank. His research focuses on the mathematics of data science, including random sampling, statistical ranking, optimization-based uncertainty quantification, and multifidelity methods. Xu emphasizes problem-driven innovation and collaborates across disciplines. Education: Ph.D. in Mathematics, University of Utah, 2022 Postdoctoral Fellowship, University of Waterloo Quantitative Analyst, Wells Fargo Bank Research interests span computational statistics, numerical analysis, and machine learning applications. His work bridges theoretical foundations and practical data-driven problems, often integrating optimization and probabilistic techniques. Recent contributions include advancements in gradient descent for network analysis, multifidelity distribution estimation, and archetypal analysis using optimal transport. Publications reflect a trajectory in stochastic methods, statistical learning, and computational mathematics, with applications in PDE solvers and uncertainty quantification. Awards: None explicitly listed. Advising and grants: Actively seeks motivated students for collaborative projects. Teaching includes courses on matrix theory, data science, and numerical methods.

Onkar Jadhavمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Computational Science
• Applied Mathematics
• +۵ مورد دیگر

Onkar Jadhav is a Research Fellow at the University of Western Australia's School of Earth and Oceans, specializing in developing machine learning frameworks for oceanography and computational science. His work focuses on statistical downscaling techniques for ocean temperature predictions and reduced-order modeling for financial risk analysis. Previously, he was a postdoctoral researcher at the University of Luxembourg, where he applied machine learning to wind engineering and computational fluid dynamics. Education: He holds a Doctorate in Applied Mathematics from the Technical University of Berlin (2018–2022), with a thesis on physics-assisted machine learning for high-dimensional partial differential equations. His academic background bridges mathematics, engineering, and computational science. Research Interests: Jadhav’s expertise spans machine learning, computational science, and applied mathematics, with applications in oceanography, wind engineering, and financial risk analysis. He emphasizes interdisciplinary approaches to solve complex, high-dimensional problems. Publications: His research includes advancements in model order reduction for parametric systems in finance, wind load assessments using machine learning, and thermoelectric generator design for biomedical implants. Labs/Teams: Affiliated with the Oceans Graduate School and previously with the University of Luxembourg’s wind engineering team.