جست‌وجوی استاد و پژوهشگر

Carolin Mehlmannمشاهده پروفایل

پژوهشگر

• Applied Mathematics
• Computational Physics
• Climate Modeling
• +۳ مورد دیگر

Dr. Carolin Mehlmann is a Group leader at the Institute for Analysis and Numerical Analysis (IAN) at Otto-von-Guericke University Magdeburg. Her research focuses on sea ice dynamics, numerical methods, and computational physics. She holds a PhD in Mathematics from the same university (2019). Her work integrates applied mathematics with climate modeling, emphasizing high-resolution simulations and finite element methods. Education: PhD in Mathematics, Otto-von-Guericke University Magdeburg, 2019 Research Interests: Dr. Mehlmann specializes in sea ice dynamics, numerical discretization techniques (e.g., CD-grids, finite elements), and their applications in climate and ocean modeling. Her projects often address challenges in viscous-plastic rheology, ocean-atmosphere interactions, and high-resolution earth system modeling using frameworks like FESOM and ICON. Publications: Her recent work explores CD-type discretization for sea ice models, robust solvers for viscous-plastic systems, and hybrid sea ice model development. These studies highlight advancements in computational methods and their impact on climate system accuracy. Awards: Karin-Witte Preis (2022) Advising & Funding: She supervises multiple students (e.g., Saskia Kahl’s ongoing PhD) and leads projects funded by DFG and the German Federal Environmental Foundation. Her work includes mentorship programs for early-career female researchers in mathematics. Outreach: Dr. Mehlmann promotes mathematics in climate science through events like the thematic year “Mathematics and Climate Change” and public engagement initiatives at the University of Magdeburg.

Sergii Skakunمشاهده پروفایل

دانشیار

• Remote Sensing
• Agricultural Monitoring
• Machine Learning
• +۴ مورد دیگر

Sergii Skakun is an Associate Professor at the University of Maryland, College Park with a joint appointment in the Department of Geographical Sciences and the College of Information Studies (iSchool). He holds a PhD in Computer Science from the National Academy of Sciences of Ukraine (2005) and an M.S. in Applied Mathematics from Kyiv Polytechnic Institute (2004). His research focuses on advancing remote sensing data fusion, machine learning applications for Earth observation, and agricultural monitoring. Skakun has led projects funded by NASA, NSF, and international agencies, including initiatives on war-induced agricultural damage mapping and climate change impacts in conflict zones. Education: PhD in Computer Science, National Academy of Sciences of Ukraine (2005) M.S. (Hons.) Applied Mathematics, Kyiv Polytechnic Institute (2004) B.S. Applied Mathematics, Kyiv Polytechnic Institute (2002) Research Interests: Remote sensing of land cover/use change Machine learning in geospatial analysis Disaster monitoring and conflict zone agriculture Earth observation for food security His work has produced over 70 peer-reviewed papers and collaborations with NASA Harvest, CEOS, and international teams. Notable recent projects include artillery crater mapping in Ukraine and climate analog velocity estimation using optical flow. Grants & Leadership: Two-time NASA FINESST grant recipient (2021–2025) PI for NASA Rapid Response projects on Ukraine’s agricultural monitoring Co-leads CEOS Cloud Masking Inter-comparison Exercise (CMIX) Labs & Teams: Active contributor to NASA Harvest and Terrestrial Information Systems Laboratory (NASA Goddard), focusing on operationalizing satellite intelligence for global food security challenges.

Boragan Aruobaمشاهده پروفایل

استاد

• Macroeconomics
• Monetary Economics
• Econometrics
• +۱ مورد دیگر

Boragan Aruoba is a Professor in the Department of Economics at the University of Maryland, where he has been a faculty member since 2004. He is a macroeconomist with theoretical and empirical interests in monetary economics, nonlinear macroeconomic dynamics, and econometric methods. His research has been supported by the National Science Foundation, and he serves as an editor for the Journal of Monetary Economics and as a Research Associate at the National Bureau of Economic Research (NBER). Education: PhD in Economics, University of Pennsylvania, 2004 Research Interests: Aruoba's work spans both theoretical and empirical macroeconomics. On the theoretical side, he studies nonlinearities in macroeconomic models, particularly dynamics near the zero lower bound (ZLB) on nominal interest rates. His empirical research focuses on data revisions, yield curve modeling, and real-time macroeconomic measurement. His tools include dynamic stochastic general equilibrium (DSGE) models, state-space methods, and machine learning techniques applied to policy shocks. Scientific Contributions: Developed the Aruoba-Diebold-Scotti Business Conditions Index , now produced by the Federal Reserve Bank of Philadelphia. Created Aruoba Term Structure of Inflation Expectations (ATSIX) , also implemented by the Philadelphia Fed. Co-developed GDPplus , a new measure of U.S. GDP growth, adopted by the Federal Reserve Bank of Philadelphia. Advising and Grants: Aruoba has advised students such as Pablo Cuba-Borda, now at the Federal Reserve Board of Governors. His research is supported by the National Science Foundation and he teaches macroeconomics, computational methods, and forecasting at both undergraduate and graduate levels. Affiliations and Roles: Editor, Journal of Monetary Economics Research Associate, NBER (Monetary Economics Program) Faculty member, Department of Economics, University of Maryland

Kay Schäferمشاهده پروفایل

پژوهشگر

• Turbulent Convection
• Climate Modeling
• Computational Fluid Dynamics
• +۳ مورد دیگر

Dr. Kay Schäfer is a scientific assistant at the University of Hamburg's Meteorological Institute within the Faculty of Earth System Sciences. His work focuses on atmospheric turbulence and boundary layer dynamics, particularly in the context of climate modeling and computational fluid dynamics. He contributes to the Warmworld project, advancing turbulence parameterization in weather and climate models like ICON. His research interests include turbulent convection, high-performance computing, and numerical simulations of complex flow phenomena. He has conducted studies on secondary flows in turbulent channels, heterogeneous roughness effects, and parametric forcing approaches for modeling such systems. His work bridges fundamental fluid dynamics with applied climate science, leveraging advanced computational methods. Schäfer collaborates on datasets and validation of numerical approaches for direct numerical simulations (DNS) of rough wall flows. He is affiliated with the Atmospheric Turbulence and Boundary Layers research group and actively maintains profiles on ResearchGate, Google Scholar, and LinkedIn.

Jean-Pierre FRADINمشاهده پروفایل

پژوهشگر

• Thermal Management
• Power Electronics
• Electronics Cooling
• +۴ مورد دیگر

Jean-Pierre FRADIN is a Researcher Lecturer at ICAM, based in Toulouse, France. His work focuses on thermal management of electronic systems, particularly in power electronics and additive manufacturing applications. He collaborates with prestigious research laboratories globally to develop innovative cooling solutions for industries like rail transport and electric vehicles. His research explores topics such as thermal-adjoint optimization, 3D power module defect detection, and SiC MOSFET thermal behavior. He emphasizes applied research with strong industry ties, addressing challenges in energy efficiency, material science, and thermal simulation techniques. His contributions span both academic publications and practical applications in cooling system design. Notably, he investigates lattice structure heat sinks for liquid cooling, Monte Carlo methods for junction temperature estimation, and the impact of additive manufacturing on thermal performance. His work bridges computational modeling with experimental validation, ensuring practical relevance to industrial needs. While no awards are explicitly mentioned, his extensive publications reflect a prolific and impactful career in thermal management. His research also extends to collaborative projects with companies, enhancing engineering education and innovation at ICAM.

Navid Zobeiryمشاهده پروفایل

دانشیار

• Composite Materials
• Advanced Manufacturing
• Scientific Machine Learning
• +۳ مورد دیگر

Dr. Navid Zobeiry is an Associate Professor in Materials Science & Engineering at the University of Washington, with an adjunct role in Aeronautics & Astronautics. His research integrates materials science, data science, and advanced manufacturing to address aerospace challenges, focusing on smart testing, manufacturing, and engineering methods using machine learning and physics-based models. He holds patents in AI software for aerospace applications and has been recognized with awards such as the Boeing Certificate of Appreciation (2024) and the SAMPE URS 3rd Best Paper Award (2022). Education: PhD from University of British Columbia (UBC), MSc from UBC, BSc from University of Tehran. Prior to UW, he was a Research Associate and Lecturer at UBC. Research interests span composite materials, failure analysis, scientific machine learning, and uncertainty quantification in aerospace. His work emphasizes probabilistic ML frameworks for material characterization and process optimization. Recent articles focus on composite manufacturing optimization, process-induced deformations, and ML-driven thermal modeling. His publications highlight advancements in pyrolysis characterization, tool-part interaction analysis, and outlier detection in manufacturing processes. Awards include Boeing’s D-DRASS recognition (2024, 2019), a teaching award (2020), and UBC’s President’s Award (2009). He advises graduate students like C. Schoenholz and collaborates with aerospace companies on patented technologies. Labs/Teams: Composites Group at UW, leading projects on AI in manufacturing and material certification. Active in industry partnerships for applied research.

Rajiv Ranganathanمشاهده پروفایل

دانشیار

• Motor Learning
• Biomechanics
• Robotics
• +۳ مورد دیگر

Rajiv Ranganathan is an Associate Professor in the Department of Kinesiology at Michigan State University. He holds a Ph.D. from The Pennsylvania State University. His research focuses on motor learning and biomechanics, particularly investigating how humans produce skilled movements and adapt to changes caused by aging, development, or movement disorders. Ranganathan employs experimental techniques like motion capture, robotics, and virtual reality alongside biomechanical modeling and simulations to uncover mechanisms of motor control. His work aims to develop novel training methods for motor skill learning and rehabilitation of movement disorders. Education: Ph.D., The Pennsylvania State University. Research Interests: Motor learning dynamics, human movement control, neurorehabilitation robotics, and the application of machine learning in motor skill assessment. He explores how variability and redundancy in motor tasks influence learning and adaptation. His studies often involve developing assistive technologies such as body-machine interfaces and semi-passive robotic platforms to enhance motor function in individuals with impairments. Recent work highlights themes like motor memory consolidation after stroke, age-related deficits in motor learning, and the design of open-source platforms for motor skill experiments. His research bridges biomechanics, robotics, and neuroscience to address clinical challenges in rehabilitation. Key contributions include advancements in understanding how error compensation occurs in redundant motor systems and the role of implicit motor adaptation across age groups. He emphasizes interdisciplinary approaches, advocating for stronger connections between theoretical models and empirical studies in motor learning research.

• Multiscale Modeling
• Fluid Dynamics
• Numerical Analysis
• +۳ مورد دیگر

Stefano Piccardo is a Research Fellow at RMIT Europe in Barcelona, Spain, as part of the ALCOAT Project. His affiliation with RMIT Europe highlights his role in advancing computational and mathematical research within the institution. While biographical details are limited, his work centers on multiscale modeling, fluid dynamics, and numerical methods such as hybridizable discontinuous Galerkin (HDG) approaches and NURBS-based geometries. Research Interests: Multiscale Modeling Fluid Dynamics Numerical Analysis Scientific Computing Computational Mathematics Advection-Diffusion-Reaction Systems His recent publications (2023–2025) focus on developing high-order numerical methods to address complex fluid flow problems, including two-fluid Stokes dynamics and advection-diffusion-reaction phenomena. These studies emphasize computational efficiency and accuracy in handling intricate geometries using NURBS and unfitted mesh techniques. No scientific awards or formal grants are explicitly mentioned in the provided texts. Stefano collaborates closely with researchers like Conni G, Perotto S, Giacomini M, and Huerta A, contributing to advancements in hybrid high-order methods and level-set schemes for multiphase flow simulations. He is affiliated with RMIT Europe’s research group, working on projects like ALCOAT that bridge theoretical and applied computational science.

Trine Mykkeltvedtمشاهده پروفایل

پژوهشگر

• Numerical Simulation
• Computational Geosciences
• Flow and Transport in Porous Media
• +۵ مورد دیگر

Trine Mykkeltvedt is a Researcher II at NORCE (formerly IRIS) specializing in numerical simulation and modeling of flow and transport in porous media. She earned her master's and doctorate in applied mathematics from the University of Bergen (2014) and joined NORCE in 2015. Affiliated with the "Computational Geosciences and Modelling" group Department of Energy and Technology Her research focuses on CO2 storage, hydrogen storage, and enhanced oil recovery (EOR) through reservoir modeling. Applications include carbon capture and storage (CCS), subsurface energy systems, and environmental flow dynamics. Recent publications highlight her work on convective mixing in CO2 storage (2025), pressure interference effects (2024), and hydrogen leakage quantification (2024). Her methodological expertise includes WENO schemes, unstructured grids, and multiphase flow modeling. She contributes to projects like HyPE (Hydrogen Storage) and CSSR (Sustainable Subsurface Resources) while applying computational methods to address climate challenges through subsurface energy solutions.

• Artificial Intelligence
• Machine Learning
• Data Science
• +۷ مورد دیگر

Dr. Alireza Moayedikia is a Lecturer in Information Systems at the School of Business, Law and Entrepreneurship, Swinburne University of Technology. His research focuses on Artificial Intelligence, Machine Learning, and Data Science, with expertise in social network analysis, natural language processing, and back-end software development. He has over ten years of professional experience in AI and ML, emphasizing data preparation for machine learning tasks. Research Interests: Dr. Moayedikia’s work spans optimization algorithms for crowdsourcing, microtask management, and feature selection. His recent contributions include advancements in matrix factorization, dynamic payment systems for microtask platforms, and community detection in attributed networks. His methodologies often integrate swarm intelligence (e.g., bee colony optimization) and probabilistic models. Publications: His 19 publications reflect a strong focus on AI-driven solutions, including contributions to data-driven healthcare systems and green logistics. His articles address challenges in sparse data handling, worker reliability estimation, and multi-criteria decision-making frameworks. Grants: He contributed to the SCOPE 3 research contract (2020–2021), developing prototypes for tracking Green House Gas emissions in last-mile delivery systems. Labs/Teams: Collaborates within interdisciplinary teams at Swinburne, focusing on applied AI and sustainable logistics solutions.

Yanzhi Zhangمشاهده پروفایل

استاد

• Fractional Partial Differential Equations (PDEs)
• Scientific Computing
• Numerical Analysis
• +۶ مورد دیگر

Dr. Yanzhi Zhang is a Professor of Mathematics at the Department of Mathematics and Statistics, Missouri University of Science and Technology (Missouri S&T), where she has held academic positions since 2010. She earned a Ph.D. in Mathematics from the National University of Singapore and a B.Sc. in Applied Mathematics (with a Computer Science minor) from Chongqing University. Her research spans computational and applied mathematics, focusing on fractional PDEs, nonlocal models, machine learning applications in materials science and business, Bose-Einstein superfluids, and seismic wave inversion. Dr. Zhang has been awarded the Faculty Excellence Award from Missouri S&T and other accolades. Her work is supported by grants from the National Science Foundation, including projects on fractional viscoacoustic wave equations, Bose-Einstein superfluids, and dispersive wave equations. She advises Ph.D. students in interdisciplinary research, such as Yumeng Wang (machine learning for eye-tracking data) and Yixuan Wu (seismic inversion). Her research integrates mathematical rigor with computational innovation, addressing real-world challenges in geophysics, materials science, and business analytics. Collaborations with industry and academia enhance her contributions to data-driven modeling and open-source strategies for digital transformation.

Simone Alioliمشاهده پروفایل

دانشیار

• High-Energy Physics
• Theoretical Physics
• Fundamental Interactions
• +۳ مورد دیگر

Simone Alioli is an Associate Professor at the Department of Physics "Giuseppe Occhialini" at the University of Milano-Bicocca. His research focuses on high-energy theoretical physics, particularly in collider phenomenology and Monte Carlo event generator development. Role: Associate Professor Institution: University of Milano-Bicocca Department: Department of Physics "Giuseppe Occhialini" Research Interests: Theoretical physics of fundamental interactions, precision calculations for hadron colliders, resummation techniques, and Monte Carlo simulations. His work bridges quantum field theory with practical event generation tools for experiments like the LHC and future colliders. High-Energy Physics Quantum Chromodynamics Collider Phenomenology Monte Carlo Methods Effective Field Theories Future Collider Design Article Trends: Recent publications emphasize improving event generator accuracy through NNLO matching, resummation methods for top-quark and Higgs boson production, and feasibility studies for future colliders like FCC. Collaborative works often involve experimental design, software validation, and beyond-Standard-Model investigations. Grants & Projects: P4: Precise Predictions for Particle Phenomenology (2019, MIUR) Improved simulations for the LHC and future colliders (2017, CARIPLO Foundation) REINVENT: Resummation-Improved Monte Carlo Event Generator (2017, European Commission)

• Applied and Computational Electromagnetics
• Magnetic hysteresis
• Magnetic losses
• +۶ مورد دیگر

François Henrotte is a Professor at the University of Liège, affiliated with the Montefiore Institute of Electrical Engineering and Computer Science within the Department of Electrical Engineering, Electronics and Computer Science. He leads research in Applied and Computational Electromagnetics (ACE), focusing on electromagnetic modeling, magnetic materials, and computational methods. His work bridges theoretical electromagnetics with practical engineering applications in electrical machines and materials processing. Henrotte's research interests center on computational electromagnetics, with particular expertise in magnetic hysteresis modeling, iron losses computation, and electromagnetic force calculation. His recent work has increasingly incorporated machine learning techniques, especially neural networks, to enhance the accuracy and efficiency of electromagnetic simulations. He has made significant contributions to the modeling of ferromagnetic laminated cores in electrical machines, developing innovative homogenization techniques combined with neural network approaches. His research also extends to flash sintering of ceramics, electromagnetic levitation of molten metals, and cryogenic actuators, demonstrating the breadth of his expertise across multiple domains of applied electromagnetics. Over the past decade, Henrotte's publication record reveals a clear evolution toward integrating artificial intelligence with traditional electromagnetic modeling. His recent work (2022-2025) increasingly features neural network applications for material modeling and loss prediction in electrical machines. This represents a significant shift from his earlier focus on classical finite element methods and hysteresis modeling. His research maintains strong connections with industrial applications, particularly in electrical machine design and advanced materials processing. François Henrotte has mentored numerous researchers who have become his primary collaborators, including Florent Purnode, Jonathan Velasco, and Kevin Jacques. His research group actively collaborates with the Belgian Ceramic Research Centre and SUPRATECS (Services Universitaires pour la Recherche et les Applications Technologiques de Matériaux Électro-Céramiques, Composites, Supraconducteurs). These collaborations have resulted in significant contributions to both theoretical electromagnetics and practical engineering applications, particularly in the areas of electrical machines, magnetic materials, and advanced ceramic processing techniques.

Prince Chidyagwaiمشاهده پروفایل

دانشیار

• Numerical Analysis
• Scientific Computing
• Computational Fluid Dynamics
• +۵ مورد دیگر

Prince Chidyagwai is an Associate Professor of Mathematics at Loyola University Maryland, specializing in Numerical Analysis and Scientific Computing. His primary affiliation is the Department of Mathematics and Statistics. He holds a Ph.D. in Computational and Applied Mathematics from Rice University (2010), an M.A. in Mathematics from the University of Pittsburgh (2006), and dual B.S./B.A. degrees in Mathematics (Honors) and Computer Science from Lafayette College (2005). His research focuses on advanced numerical methods for coupled flow systems, including discontinuous Galerkin (DG) methods, finite element methods, and finite volume techniques applied to problems in porous media, fluid dynamics, and radiative transfer. Key areas include Stokes-Darcy coupling, multiphase flow, and the development of efficient multirate and decoupling algorithms for complex systems. Chidyagwai's publications span topics such as multilevel decoupling methods, constraint preconditioning for coupled systems, and high-order schemes for radiation transport. His work emphasizes practical applications in reservoir simulation, environmental flow modeling, and industrial fluid dynamics. Despite his prolific output, no scientific awards are explicitly mentioned in the provided materials. Teaching responsibilities include courses like Ordinary Differential Equations and Programming in Mathematics. His academic contributions extend to collaborative projects at the interface of computational mathematics and engineering, though no specific lab affiliations or grant details are disclosed.

Michael Meisterمشاهده پروفایل

استاد

• Wastewater Treatment Plant Modeling
• Numerical Flow Simulation
• Smoothed Particle Hydrodynamics (SPH)
• +۳ مورد دیگر

Michael Meister is a Professor at MCI – The Entrepreneurial School, holding a position in the Department of Environmental, Process & Energy Technology. He has held roles including Junior Professor and Post-Doc researcher at Universität Innsbruck and teaching positions at Imperial College London. His research focuses on wastewater treatment plant modeling, numerical flow simulation using SPH, and hydraulic simulations. Education: PhD in Environmental Technology (University of Innsbruck), MSc in Physics (Imperial College London), BSc in Physics (University of Innsbruck). Research interests include optimizing anaerobic digester mixing efficiency, CFD applications in wastewater processes, and developing SPH-based models for environmental systems. His work bridges computational fluid dynamics with practical engineering solutions for sustainable energy and water management. Key projects include OPTIFAUL (energy-efficient mixing optimization) and SPHAUL (SPH-based digester modeling). He has received multiple teaching awards from MCI and recognition for his doctoral and undergraduate work. Advised over 15 students on topics ranging from biogas potential estimation to CFD-based energy optimization in wastewater treatment plants. Active in peer review for journals and conferences, and serves on evaluation committees for international research programs. Labs/Teams: Leads the OptiFaul research initiative and collaborates with institutions like the University of Leeds and the Austrian Research Promotion Agency.