جست‌وجوی استاد و پژوهشگر

Ranit Deمشاهده پروفایل

پژوهشگر

• Global carbon cycle
• Eddy covariance
• Model-Data assimilation
• +۶ مورد دیگر

Ranit De is a Doctoral Researcher in the Department of Biogeochemical Integration at the Max Planck Institute for Biogeochemistry in Jena, Germany. De is affiliated with the International Max Planck Research School for Global Biogeochemical Cycles (IMPRS-gBGC) and works in the Model-Data Integration research group under Dr. Nuno Carvalhais. De's educational background includes: Ph.D. candidate (2021-present) at the International Max Planck Research School for Global Biogeochemical Cycles M.Sc. in Geo-information Science and Earth Observation (Water Resources and Environmental Management) from the University of Twente, Netherlands (2019-2021) B.Tech. in Agricultural Engineering from Sam Higginbottom University of Agriculture, Technology and Sciences, India (2015-2019) De's research focuses on understanding the global carbon cycle through advanced hybrid modeling techniques that combine physically based models with machine learning approaches. De specializes in simulating gross primary production at sub-daily scales using eddy-covariance data and light use efficiency-based models, with particular expertise in understanding interannual variability in terrestrial ecosystem fluxes. Analysis of De's recent publications reveals a strong research trajectory examining how temporal variation of hydrological parameters can improve model performance, investigating parametric uncertainties in land surface models, and exploring the relationship between spatiotemporal variability of model parameters and their controlling factors. De's work bridges the gap between traditional physical modeling and modern data-driven approaches. De has presented research at notable conferences including the EGU Mary Anning conference in June 2025 and the ICOS Science Conference 2024, demonstrating active engagement with the global scientific community. De's methodological innovations in model-data integration contribute significantly to improving biogeochemical models for better climate predictions and understanding carbon cycle dynamics under changing environmental conditions.

Dan Casasمشاهده پروفایل

دانشیار

• Computer Graphics
• Computer Vision
• Machine Learning
• +۵ مورد دیگر

Dan Casas is a Senior Applied Scientist at Amazon in Seattle and an Associate Professor (Profesor Titular) on leave from King Juan Carlos University in Spain. His research spans the intersection of Computer Graphics, Computer Vision, and Machine Learning with a focus on 3D reconstruction, modeling, and animation of virtual humans and clothing. He has authored over 40 high-impact publications in top venues including SIGGRAPH, CVPR, and NeurIPS, and holds 3 international patents. Dr. Casas received his M.Sc. degree (2009) from Universitat Autònoma de Barcelona (Spain), including a research visit at Carnegie Mellon University. He earned his Ph.D. in Computer Graphics (2014) from the University of Surrey (UK), supervised by Prof. Adrian Hilton. He completed postdoctoral research at the University of Southern California's Institute for Creative Technology (2014-2015) and the Max Planck Institute in Saarbrücken (2015-2016). His research interests center on creating realistic virtual humans and digital clothing through advanced techniques in computer vision and machine learning. Casas has pioneered methods for 3D reconstruction of humans and garments from video input, physics-based simulation of soft-tissue deformations, and data-driven approaches to character animation. His work bridges the gap between theoretical computer graphics and practical applications in virtual reality, digital fashion, and immersive communication. Analysis of his recent publications reveals a consistent focus on human digitization, with increasing emphasis on machine learning approaches. His work has evolved from traditional computer graphics techniques toward neural representations and diffusion models, particularly in the areas of 3D garment simulation and human avatar creation. The trend shows growing integration of physics-based modeling with data-driven approaches to achieve both realism and computational efficiency. Marie Skłodowska-Curie Individual Fellowship (2015) FBBVA Leonardo Fellowship (2021) Medal from the Royal Academy of Engineering of Spain for Young Researcher Award (2023) i3 certification (outstanding researcher) from Spanish Ministry of Universities (2022) Winner of 2021 IEEE Retail Digital Transformation Grand Challenge Multiple Outstanding Reviewer Awards at top conferences (CVPR, BMVC, 3DV) Dan Casas has successfully advised multiple PhD students including Suzanne Sorli, Cristian Romero, Raquel Vidaurre, and Igor Santesteban (now at Meta Reality Labs), with several ongoing students including Melania Prieto-Martin, Gonzalo Gómez-Nogales, and Andrés Casado-Elvira. He has secured significant research funding as Principal Investigator, totaling over €1.2 million from Spanish Ministry of Science projects, EU H2020 programs, and industry fellowships including the FBBVA Leonardo Fellowship. His leadership extends to conference organization as Area Chair for ICCV 2023 and General Chair for ACM i3D 2020. Dr. Casas leads research in digital human modeling with applications in virtual reality, fashion technology, and immersive communication. His team develops advanced techniques for creating personalized 3D avatars from minimal input (like smartphone videos), addressing challenges in geometry, appearance, and physical simulation of virtual humans and their clothing.

Bersimis Sotiriosمشاهده پروفایل

دانشیار

• Statistical Process Control
• Public Health Surveillance
• Health Analytics
• +۵ مورد دیگر

Sotirios Bersimis is an Associate Professor at the University of Piraeus, Department of Business Administration. He holds additional roles as an elected member of the board of directors of the National Statistical Institute (Greece) and representative for FenSTATs and ECAS. Previously, he served as President of the Hellenic Organization for Health Care Services (EOPYY) and as President of the European Healthcare Fraud & Corruption Network (EHFCN). His education includes a PhD in Statistics from the University of Piraeus, an MSc in Statistics from Athens University of Economics and Business, and a BSc in Statistics and Insurance Science from the University of Piraeus. His research focuses on stochastic models for process monitoring, statistical process control, and health analytics. He has published over 60 peer-reviewed articles in journals like Journal of Quality Technology , Statistics in Medicine , and Annals of the Institute of Statistical Mathematics . His work emphasizes applications in healthcare surveillance, quality management, and fraud detection. Notable contributions include the development of the Process Monitoring Group and the 'Multivariate Statistical Process Control Charts: An Overview' paper, which remains highly cited in the field. Bersimis has received awards such as the 2018 ENBIS Best Manager Award and a public honor from the Greek Prime Minister. He actively collaborates with healthcare institutions, pharmaceutical companies, and international organizations, contributing to projects on health expenditure modeling and anti-fraud initiatives. His teaching spans undergraduate and postgraduate programs in statistics, biostatistics, and data science.

Christian Schlichtkrullمشاهده پروفایل

استاد

• Algebraic Topology
• Homotopy Theory
• Topological Hochschild Homology
• +۳ مورد دیگر

Christian Schlichtkrull is a Professor at the Department of Mathematics, University of Bergen, Norway. His contact information includes email (christian.schlichtkrull@uib.no), phone (+47 55582828), and addresses: Visitor address at Allégaten 41, 5007 Bergen, and Postal address at P.O. Box 7803, NO-5020 Bergen. His primary research focuses on advanced topics in algebraic topology, with emphasis on: Homotopy theory including stable homotopy and parametrized homotopy Topological Hochschild homology (THH) and its logarithmic variants Symmetric spectra and diagram spaces Algebraic K-theory connections via cyclotomic trace Generalized Thom spectra and virtual vector bundles Schlichtkrull's publications (2005-2020) demonstrate sustained exploration of spectra, homology theories, and K-theory connections. Recent work emphasizes multiplicative structures in parametrized homotopy and logarithmic THH, frequently collaborating with researchers from institutions like Radboud University and University of Bonn. His research has been supported by the Research Council of Norway (Grant 213458). No advising relationships, laboratory affiliations, or scientific awards are documented in the provided materials.

Daniel Axehillمشاهده پروفایل

استاد

• Autonomous Systems
• Optimization-Based Control
• Sensor Fusion
• +۴ مورد دیگر

Professor Daniel Axehill is affiliated with the Department of Electrical Engineering (ISY) at Linköping University, specializing in planning and optimization-based control for autonomous systems. His research bridges theoretical developments and industrial applications. His recent work focuses on robust motion planning for autonomous vehicles, optimal task and motion planning algorithms, execution-time analysis for model predictive control, and high-performance solvers for multi-parametric quadratic programming. Key methods include lattice-based planning, disturbance estimation, and real-time optimization. He contributes to the Wallenberg Autonomous Systems Program (WASP), collaborating on advancements in robotics, sensor fusion, and complex network control systems.

• Applied Probability
• Insurance Mathematics
• Finance
• +۳ مورد دیگر

Mogens Bladt is a Professor of Applied Probability and Insurance Mathematics at the Department of Mathematical Sciences, University of Copenhagen. He has held positions since 2018 after 24 years as Principal Researcher at the National University of Mexico (1994-2018), with visiting professorships at Technical University of Denmark and University of Copenhagen since 2001. Research: Focuses on time-inhomogeneous phase-type distributions, matrix-oriented life insurance models, heavy-tailed distributions, and diffusion bridge simulation Teaching: Offers graduate/undergraduate courses in Applied Probability, Stochastic Processes, Risk Theory, and Numerical Analysis Scientific Contributions: Developed R packages for Markov jump processes, phase-type distributions, and diffusion bridges. Holds grants from Mexico and Denmark, including Danish Research Council funding (2007–2008). Supervised 5 PhD, 8 Master’s, and 13 Bachelor’s theses Organized academic workshops and served as Associate Editor for Stochastic Models since 1997

Jarryd Plaمشاهده پروفایل

دانشیار

• Quantum Information Processing
• Quantum Technologies
• Spin-based Quantum Computation
• +۳ مورد دیگر

Associate Professor Jarryd Pla is an experimental physicist and electrical engineer at the University of New South Wales, specializing in quantum information processing and quantum technologies. He holds a PhD in Electrical Engineering from UNSW (2013) and a first-class honors BEng in Photonic Engineering (2009). Current ARC Future Fellow Former Bragg Gold Medal recipient His research focuses on: Spin-based quantum computation in silicon Superconducting quantum circuits Quantum-noise-limited microwave amplifiers Hybrid quantum systems for quantum memory Quantum sensing and spectroscopy Recent publications highlight: Room-temperature maser amplifiers Kinetic inductance parametric amplifiers Coherent control of donor spins Quantum-limited electron spin resonance Scientific Awards: ARC Future Fellowship (2024-2028) Bragg Gold Medal His grants include: ARC DECRA (2019-2022): Superconducting hybrid quantum technologies ARC Discovery Project (2021-2024): Quantum sensing with semiconductor devices ARC Future Fellowship (2024-2028): Room-temperature diamond-based microwave detection

Joshua Speagleمشاهده پروفایل

استادیار

• Astrostatistics
• Data Science
• Machine Learning
• +۷ مورد دیگر

Joshua Speagle is an Assistant Professor jointly appointed in the Department of Statistical Sciences and the David A. Dunlap Department of Astronomy & Astrophysics at the University of Toronto. He is also an Associate Member of the Dunlap Institute for Astronomy & Astrophysics and a Member of the Data Sciences Institute. His research lies at the intersection of statistics, astronomy, and computer science, focusing on astrostatistics and data-intensive astrophysics. His research interests include astrostatistics, data science, machine learning, statistical inference, and Bayesian methods. He develops novel statistical learning techniques to extract insights from large, complex datasets, particularly from astronomical surveys. His work emphasizes interpretability, robust inference, and computational efficiency, with applications to galaxy formation, stellar photometry, and 3D dust mapping. The trends in his recent publications reflect a strong focus on interdisciplinary methodologies, particularly in Bayesian inference, nested sampling, and machine learning applied to astrophysical problems. His work consistently bridges theoretical statistics with practical applications in astronomy, emphasizing open-source software and reproducible research. Banting Postdoctoral Fellowship Dunlap Fellowship Joshua Speagle is deeply committed to mentorship and collaboration. He co-leads the Astrostatistics Research Team (ART) with Gwen Eadie, mentoring students and postdocs across disciplines. He is involved in graduate and undergraduate research programs, including the Astronomy & Astrophysics Summer Undergraduate Research Program (SURP). He teaches courses in statistics and astronomy and serves on committees within the University of Toronto and professional societies such as the AAS, ASA-AIG, and SSC-DSA. He co-leads the interdisciplinary Astrostatistics Research Team (ART), which fosters a collaborative, inclusive environment focused on cutting-edge research at the intersection of statistics and AI. The team emphasizes open and accessible science, releasing open-source tools like dynesty and brutus , and mentoring the next generation of data scientists.

MARIA ALONSO PENAمشاهده پروفایل

استادیار

• Nonparametric Statistics
• Circular Regression
• Statistical Modeling
• +۳ مورد دیگر

María Alonso-Peña is an Assistant Professor at the University of Santiago de Compostela , affiliated with the Faculty of Biology and the Department of Statistics, Mathematical Analysis, and Optimization . Her research focuses on nonparametric statistical methods, particularly in circular regression models and their applications in neuroscience and animal behavior analysis. She holds a PhD in Statistics from the University of Santiago de Compostela (2022), with a thesis titled New approaches to nonparametric circular regression models . Education: PhD in Statistics (2022), University of Santiago de Compostela Research Interests: Nonparametric statistics, circular regression, optimization, and statistical modeling in neuroscience and ecology Her work bridges theoretical statistics with applied problems, such as analyzing neuronal spike counts and animal escape behavior using advanced circular regression techniques. She has collaborated with institutions like KU Leuven and Universidad de Granada. Recent research includes a grant from the Xunta de Galicia (ED481A-2019/139) for neuroscientific applications. Key contributions include frameworks for circular local likelihood regression and parametrically guided kernel density estimators for spherical data. These methods address challenges in modeling directional and multimodal datasets, with implications for fields like neuroscience and environmental science.

Bernard Haasdonkمشاهده پروفایل

استاد

• Model Reduction
• Parametrized Partial Differential Equations (PDEs)
• Kernel Methods
• +۷ مورد دیگر

Bernard Haasdonk is a Professor at the University of Stuttgart, affiliated with the Institute of Applied Analysis and Numerical Simulation (IANS), part of the Faculty of Mathematics and Computer Science. His research focuses on model reduction techniques for parametrized partial differential equations (PDEs), kernel-based methods, numerical analysis, and machine learning applications in scientific computing. He leads a research group in numerical mathematics and has contributed to software tools like RBMatlab and KerMor. Affiliations: Institute of Applied Analysis and Numerical Simulation, University of Stuttgart Roles: Academic Researcher, Software Developer, Grant Principal Investigator His work bridges numerical simulation, machine learning, and reduced basis methods, addressing challenges in optimal control, fluid dynamics, and biomechanics. Haasdonk has held multiple funded projects, including those on kernel methods for model reduction and certified RB-ML-ROM surrogate models. Research Interests: Model reduction for PDEs, kernel methods, greedy algorithms, numerical analysis, optimal control, and applications in fluid dynamics and porous media. He emphasizes structure-preserving methods for Hamiltonian systems and data-driven approaches for surrogate modeling. Publications: Over 200 articles in journals like SIAM, BIT Numerical Mathematics, and Physica D, focusing on convergence analysis, kernel-based approximation, and reduced-order modeling. Recent trends include adaptive greedy algorithms, symplectic model reduction, and energy-conserving surrogates. Awards: IEEE PerCom 2017 Best Paper Award, Teaching Excellence Awards (2012-2017), and early-career research grants. Grants: DFG-funded projects on model reduction, SimTech Cluster contributions, and collaborations on fuel cells and biomechanics. Teams: Leads the Numerical Mathematics Research Group at IANS, collaborating with interdisciplinary teams on projects like MORCOS (Model Order Reduction of Coupled Systems) and KerMor (Kernel Methods for Model Reduction).

Kelvin Ngمشاهده پروفایل

پژوهشگر ارشد

• Meteorological Extremes
• Climatological Extremes
• Tropical Cyclones
• +۳ مورد دیگر

Dr Kelvin Ng is a Postdoctoral Research Fellow at the University of Birmingham's School of Geography, Earth and Environmental Sciences. His research focuses on meteorological and climatological extremes, particularly tropical cyclones and their impacts. He holds an MSci in Physics from Imperial College London and a PhD in Atmospheric Sciences from the University of Hong Kong. Key roles include contributions to projects such as INPAIS (NERC-funded collaborative research with Swiss Re and Beijing Normal University), PRE-CAX (Newton Fund-supported projects with the University of Reading), Ex-Storms (NERC-funded European windstorm predictability study), and HURACAN (UK-US consortium exploring cyclone risks). He is also involved in initiatives like the Met Office Summer Testbed 2023 and SR-Hazards (Swiss Re-funded). Ng's research interests span tropical cyclone intensity dynamics, extreme rainfall prediction, and climate model assessment (e.g., CMIP6). He is a member of the Royal Meteorological Society (RMetS) and European Geosciences Union (EGU). Recent publications highlight advancements in Mei-yu front analysis, storm surge impact modeling, and causal-guided statistical approaches for extreme weather prediction. His work bridges academic research with industry collaboration, aiming to improve parametric insurance thresholds for typhoons and enhance climate change adaptation strategies for regions like China and Europe.

Erkan Günpınarمشاهده پروفایل

دانشیار

• Additive Manufacturing
• Design Space
• Printing Path Optimization
• +۵ مورد دیگر

Erkan Günpınar is an Associate Professor at the Department of Mechanical Engineering, Istanbul Technical University (ITU), specializing in Additive Manufacturing and Computer-Aided Design. His research focuses on optimizing printing paths, material properties, and geometric precision in additive manufacturing processes. He has pioneered work on lattice structures, patient-specific medical systems, and structural optimization for marine applications. Key research interests include generative design methodologies, support structure optimization, and machine learning applications in manufacturing. His work bridges engineering disciplines, integrating mechanical principles with computational techniques to solve complex design challenges. Awards: Best Presentation Award (2016, 2018), Outstanding Contribution in Reviewing (2016), Session Best Paper (2017) Projects: Includes initiatives on yacht hull optimization, cross-derivative surface modeling, and liquid flow-inspired printing paths Advising: Supervises 8 ongoing theses in additive manufacturing and geometric modeling Dr. Günpınar’s recent projects emphasize interdisciplinary collaboration, particularly in biomedical and marine engineering contexts. He actively contributes to journals like Journal of Manufacturing Processes and Computer-Aided Design .

• Sustainable Structural Engineering
• Additive Manufacturing in Construction
• Biobased Materials
• +۳ مورد دیگر

Benjamin Kromoser is a Professor for Resource Efficient Structural Engineering at the Institute of Green Civil Engineering (BOKU Vienna). He leads research projects on sustainable construction methods, including additive manufacturing of biobased materials, timber-concrete composites, and topology optimization of structural elements. His work bridges digital fabrication, circular economy principles, and industry 4.0 in civil engineering. Head of Institute of Green Civil Engineering (2022–present) Speaker of Doctoral School Build.Nature (2021–2022) University Professor for Biobased Design (2018–2022) His research focuses on reducing environmental impacts through automated design, 3D printing of concrete, and non-metallic reinforcement. Recent projects include 3DP Biowalls (biobased recyclable walls) and prestressed carbon-UHPC elements. Scientific awards include the fib Achievement Award (2019) , Inits Award (2016) , and Bundespreis für Ecodesign (2016) .

• Computational Design
• Parametric Design
• Digital Fabrication
• +۵ مورد دیگر

Eduardo Costa serves as a Senior Lecturer in Computational Architecture at the University of the West of England's College of Arts, Technology and Environment, where he bridges computational methodologies with architectural design and fabrication. His academic credentials include a PhD in Architecture specializing in Digital Design and recognition as a Fellow of the Higher Education Academy (FHEA). Research centers on parametric and procedural modeling for automating architectural shape generation, analysis, and optimisation. Costa integrates advanced digital fabrication techniques—including stone CNC milling, ceramic 3D printing, and concrete reconfigurable forming—with design workflows, while developing user-friendly software tools to implement these processes. Key research collaborations include: Bath University Cambridge University University of Dundee Pennsylvania State University University of Lisbon No scientific awards were documented in the source material. Information regarding student advising, research grants, and laboratory facilities was either not provided or insufficiently detailed for inclusion.

Felix Schindlerمشاهده پروفایل

پژوهشگر

• Numerical Analysis
• Machine Learning
• Scientific Computing
• +۳ مورد دیگر

Felix Schindler is a Researcher at the Institute for Analysis and Numerical Analysis , part of the Department of Mathematics and Computer Science at the University of Münster . His work bridges numerical analysis, machine learning, and scientific computing, with a focus on model reduction for partial differential equations (PDEs), adaptive algorithms, and computational efficiency. Research Interests include: Numerical analysis of parametric and multiscale PDEs Localized reduced basis methods (LRBM) and adaptive enrichment Integration of model order reduction (MOR) with machine learning (ML) Conservative flux reconstruction techniques Development of software libraries like dune-xt and pyMOR Recent Publications highlight trends in applying deep kernel models for surrogate modeling, localized training strategies for PDE-constrained optimization, and hybrid full/reduced-order pipelines for reactive flow prediction. His work emphasizes certified error control, hierarchical adaptivity, and cross-disciplinary computational frameworks. Collaborations span institutions such as AIMS Senegal, Springer Nature, and DUNE project teams. He actively contributes to conferences like GAMM, ENUMATH, and Algoritmy.