جست‌وجوی استاد و پژوهشگر

• Land-Atmosphere Interactions
• Storm-Resolving Earth System Models
• Climate Modeling
• +۳ مورد دیگر

Junhong Lee is a Research Fellow at the Max Planck Institute for Meteorology (MPI-M) in Hamburg, Germany, specializing in land-atmosphere interactions within storm-resolving Earth System Models. His work investigates how soil moisture conditions influence precipitation triggering and carbon cycle dynamics in high-resolution climate simulations. His academic background includes: Ph.D. and M.S. in Atmospheric Sciences from Yonsei University, Korea (2012-2019), with thesis on "The Representations of Vegetation for Surface Wind in the WRF Model" B.S. in Atmospheric Sciences and B.S. in Earth Science Education from Kongju National University, Korea (2008-2012), with thesis on "Analysis of Arctic and Antarctic Sea-Ice Concentration Variation Retrieved from Aqua/AMSER-E" Lee's research centers on improving boundary-layer processes and land-surface representations in climate models. He has implemented critical model components including the 3D Smagorinsky turbulence scheme in ICON's Sapphire configuration and the roughness sublayer scheme in WRF. His work bridges observational data with high-resolution modeling to advance understanding of land-atmosphere feedback mechanisms, particularly in storm-resolving frameworks where traditional parameterizations break down. Analysis of his 12 publications (2015-2023) reveals consistent focus on boundary-layer meteorology, model development, and land-surface interactions. His recent work emphasizes kilometer-scale Earth System Modeling (ICON-Sapphire), vegetation-atmosphere coupling, and innovative parameterizations for surface-layer processes. Key trends include integration of observational data into models, evaluation of model physics across scales, and exploration of land-surface feedbacks in extreme weather contexts. Lee actively contributes to MPI-M's modeling infrastructure including the ICON Earth System Model and participates in collaborative projects like nextGEMS and WarmWorld. His research leverages the institute's observational assets such as the Barbados Cloud Observatory and HALO research aircraft, though no specific lab leadership or student advising is documented. The institute's structure—particularly the Climate Physics and Climate Dynamics departments—provides the framework for his storm-resolving model development work.

Sebastián Ortega Arangoمشاهده پروفایل

پژوهشگر

• Climate Modeling
• Atmospheric Dynamics
• Meteorology
• +۳ مورد دیگر

Sebastián Ortega Arango is a Researcher at the Max Planck Institute for Meteorology, Department of Climate Physics, working in the Wave Driven Circulations group. His research focuses on kilometer-scale climate modeling, specifically analyzing the implications of physical parameterizations and diffusive processes in high-resolution non-hydrostatic models like ICON. This work aims to optimize model configurations for kilometer-scale simulations and understand their impact on large-scale circulations. Education: Ph.D. Earth and Atmospheric Sciences, Georgia Institute of Technology (2016) M.Sc. Earth and Atmospheric Sciences, Georgia Institute of Technology (2016) B.Sc. Civil Engineering, Universidad Nacional de Colombia (2010) Research Interests: Sebastián’s work centers on the planetary-scale circulation of the upper tropical troposphere, tropical weather dynamics, and the role of potential vorticity in monsoonal systems. He investigates quasi-biweekly oscillations in the South Asian monsoon and their co-evolution across atmospheric layers. His studies also explore the African Easterly Jet’s influence on Atlantic hurricanes and the impermeability constraints in tropical-extratropical communication. Publications: His research spans topics like potential vorticity flux effects on tropical circulation, monsoon dynamics, and the design of storm-resolving climate models. Recent work includes analyses of the African Monsoon’s impact on hurricane seasons and the behavior of tropical rainbelts under varying surface heat flux conditions. Advising & Grants: No formal advisees or grant leadership roles explicitly stated in the text. Labs & Teams: Part of the Wave Driven Circulations group within the Department of Climate Physics, contributing to projects like ORCESTRA and the ICON climate model development.

Gopal Panduranganمشاهده پروفایل

استاد

• Distributed Computing
• Networks
• Big Data
• +۷ مورد دیگر

Gopal Pandurangan is the Moores Professor in the Department of Computer Science at the University of Houston, affiliated with the Hewlett Packard Enterprise (HPE) Data Science Institute. He holds a PhD from Brown University, an MS from SUNY Albany, and a B.Tech. from IIT Madras, all in Computer Science. His research focuses on distributed computing, networks, big data, and quantum algorithms. He has advised numerous PhD students and holds IEEE Fellow and AAIA Fellow distinctions. Education: PhD (Brown University), MS (SUNY Albany), B.Tech (IIT Madras), all in Computer Science. Notable academic service includes roles as Program Committee Member for DISC, SPAA, and PODC conferences. He directs the Algorithms and Distributed Computing Lab, leading research in distributed algorithms and networks. Research interests encompass distributed algorithms, graph theory, peer-to-peer networks, social network analysis, and quantum computing applications. Awards include the University of Houston Research Excellence Award and contributions to doctoral dissertation awards. His work emphasizes fault-tolerant systems, communication efficiency, and large-scale graph processing. Professional contributions include editorial roles at Journal of Parallel and Distributed Computing and guest editing special journal issues. He teaches advanced courses like Distributed Network Algorithms and Probabilistic Methods in Algorithm Design.

• Global Optimization
• Optimization Algorithms
• Mathematical Analysis
• +۴ مورد دیگر

James Calvin is a Professor in the Department of Computer Science at the New Jersey Institute of Technology (NJIT). His research focuses on optimization algorithms, global optimization techniques, convergence rate analysis, and mathematical modeling. He has led multiple federally funded projects, including studies on optimization algorithms for decision problems, efficient simulation of large-scale systems, and stochastic optimization methods. Education : Not explicitly detailed in the text. His research interests emphasize algorithmic development for complex systems, with specializations in global optimization algorithms, convergence rate analysis, and applications in image processing and mathematical modeling. Recent work includes advancements in centroid-based clustering algorithms and bi-objective decision-making frameworks. He has received grants from the National Science Foundation for projects such as 'Optimization Algorithms For Decision Problems With Many Variables' and 'Efficient Simulation of Large-Scale Systems.' Media coverage highlights his contributions to understanding mitochondrial proteins and wood elasticity. Grants & Projects : Optimization Algorithms For Decision Problems With Many Variables (2016–2019) Efficient Simulation of Large-Scale Systems (1999–2004) Calvin collaborates internationally on optimization challenges and has produced over 66 peer-reviewed publications since 1988.

Rohit Kothariمشاهده پروفایل

پژوهشگر

• Thermal Energy Storage
• Packed Bed Systems
• Phase Change Materials
• +۳ مورد دیگر

Rohit Kothari serves as a Researcher in the Department of Civil and Mechanical Engineering at the Technical University of Denmark (DTU), contributing to sustainable energy solutions through advanced thermal systems research. His work directly supports UN Sustainable Development Goals in clean energy infrastructure. His core expertise spans Thermal Energy Storage Systems , Packed Bed Modeling , and Phase Change Material Applications , with specialized focus on Numerical Heat Transfer Analysis and Battery Thermal Management . Research methodologies emphasize computational fluid dynamics and transient thermal modeling to optimize energy storage efficiency, particularly in molten salt systems and metal foam composites. Recent publications (2024-2026) reveal concentrated research on thermal instability mitigation in high-temperature storage, constrained melting phenomena in PCM capsules, and battery thermal management under extreme discharge conditions. Key trends include computational optimization of fin geometries for melting enhancement and cold packed-bed heat transfer prediction. Kothari actively contributes to three major energy projects: Best4Grid (Nordic vehicle-grid integration), LAES-Egypt (cryogenic storage for Egypt), and GridScale (large-scale power-to-power storage). These initiatives involve cross-institutional collaboration with industry partners to develop grid-stable renewable energy solutions.

Thiab Tahaمشاهده پروفایل

استاد

• Numerical Analysis
• Scientific Computing
• Parallel Algorithms
• +۷ مورد دیگر

Thiab Taha is a Professor of Computer Science at the University of Georgia. He holds a Ph.D. in Mathematics from Clarkson University (1982). His research focuses on Numerical Analysis, Scientific Computing, Parallel Algorithms, and applications in Bioinformatics, Health Informatics, and Nonlinear Waves. He has held editorial roles including Senior Editor of the International Journal Mathematics and Computers in Simulation and Guest Editor for multiple special issues on Nonlinear Waves. Prof. Taha’s work spans computational methods for nonlinear evolution equations, GPU-accelerated algorithms, and deep learning applications in medical imaging and video captioning. Notable achievements include the 3rd IMACS 2011 Most Successful Papers Award and his role as Vice President of IMACS. His research also intersects with high-performance computing and big data challenges, including grants like the NSF EAGER initiative for Big Data Consulting Services (2013). Education: Ph.D. in Mathematics (Clarkson University, 1982) Key Contributions: Development of parallel numerical schemes for nonlinear PDEs, GPU-optimized algorithms, and AI-driven medical imaging solutions. Labs/Teams: Involved in computational biology projects like KINSOLVER for simulating biochemical networks and DrDRV3 for ophthalmic lesion detection. His interdisciplinary research bridges computational mathematics, AI, and real-world applications in healthcare and data science. Current efforts include advancing GAN-based meta-universe creation and energy-preserving fractional differential equation solvers.

Daniela Bortolettoمشاهده پروفایل

استاد

• Particle Physics
• Experimental High-Energy Physics
• Silicon Detectors
• +۵ مورد دیگر

Professor Daniela Bortoletto is Professor and Head of the Particle Physics subdepartment at the University of Oxford, holding a Nicholas Kurti Senior Research Fellowship at Brasenose College. She leads the UK ATLAS collaboration and serves as Deputy Scientific Coordinator of the EU Network AIDA. Her academic journey includes distinguished professorships at Purdue University prior to joining Oxford in 2013. Her research focuses on experimental particle physics within the ATLAS experiment at CERN's Large Hadron Collider. Bortoletto was a key member of the team that discovered the Higgs boson in 2012 and continues to investigate Higgs boson properties, dark matter couplings, and higher-mass Higgs-like particles. Her expertise spans silicon detector development, particularly for the ATLAS inner tracking system upgrade, and she has made significant contributions to Higgs physics through precision measurements of production mechanisms and decay channels. Bortoletto's recent publications demonstrate leadership in cutting-edge detector technology (MALTA2 sensors), novel statistical methods (neural simulation-based inference), and comprehensive Higgs boson characterization. Her work addresses fundamental questions in particle physics through both experimental innovation and sophisticated data analysis techniques. Fellow of the Institute of Physics (2015) Fellow of the American Association for the Advancement of Science (2013) Fellow of the American Physical Society (2004) Alfred P. Sloan Fellow (1994-1996) Ruth and Joel Spira Award for Excellence in Undergraduate Education (2004) Bortoletto maintains extensive leadership in international collaborations, serving on numerous advisory committees including the UK STFC LBNF DUNE PIP-II Oversight Committee and the Scientific Advisory Panel for Belgium's be.h Excellence of Science program. Her editorial work for Nuclear Instruments and Methods reflects her commitment to advancing detector physics methodology. She actively participates in major detector upgrade projects for the High-Luminosity LHC, focusing on silicon pixel technology that will handle the increased collision rates from 2022 onward.

• Model-Driven Engineering
• Software Product Lines
• Variability Management
• +۵ مورد دیگر

Jean-Marc Jézéquel is a prominent professor and researcher at IRISA (Institut de Recherche en Informatique et Systèmes Aléatoires) in Rennes, France, a joint research unit of INRIA, CNRS, and the University of Rennes. His extensive publication record spanning over two decades demonstrates his significant contributions to software engineering, particularly in model-driven engineering and software product lines. His research expertise encompasses several critical areas in contemporary software engineering. Jézéquel has pioneered work on managing uncertainty in model-based development, developing frameworks like DataTime for temporal reasoning in models. He has made substantial contributions to deep variability management across multiple layers of software systems, investigating how compile-time and run-time configuration options interact. His recent work increasingly integrates machine learning techniques with traditional software engineering approaches, creating innovative hybrid methodologies for software development and analysis. Jézéquel's publication trends reveal a clear evolution in his research focus, moving from foundational model-driven engineering concepts toward addressing uncertainty, incorporating temporal aspects into models, and leveraging machine learning for software analysis and development. His work spans both theoretical foundations and practical industrial applications, with notable collaborations including projects with Airbus on specialized software product line engineering. The interdisciplinary nature of his research connects software engineering with artificial intelligence, data science, and systems engineering. His significant contributions to the field include foundational work on model transformation, software language engineering, and variability management. Jézéquel has helped advance model-driven engineering from what he describes as 'craft' to a more systematic engineering discipline, as evidenced in his publications spanning from early 2000s to the present. As an academic leader, Jézéquel has supervised numerous PhD students and maintained extensive international collaborations. His work demonstrates a consistent commitment to bridging the gap between academic research and industrial software development practices, with applications ranging from aerospace systems to urban transportation solutions.

John Farnsworthمشاهده پروفایل

دانشیار

• Fluid Dynamics
• Aerodynamics
• Flow Control
• +۵ مورد دیگر

John Farnsworth is an Associate Professor in the Department of Aerospace Engineering Sciences at the University of Colorado Boulder. He serves as Director of the Aerospace Mechanics Research Center (AMReC) and the Research and Engineering Center for Unmanned Vehicles (RECUV). His expertise lies in experimental fluid dynamics, particularly in flow control, unsteady aerodynamics, and wind tunnel testing. Farnsworth holds a PhD, MS, and BS in Aeronautical Engineering from Rensselaer Polytechnic Institute (2011, 2007, 2006). His research focuses on image-based flow velocimetry, fluid-structure interaction, and biologically inspired flight. Notable contributions include studies on wind turbine aerodynamics, synthetic jet dynamics, and stall flutter mechanisms. He has developed novel wind tunnel systems like the WindCline for wildfire combustion analysis and the unsteady low-speed wind tunnel with louver systems. Awards include the Michael A. Sadowsky Prize (2007) and NSF Graduate Fellowship Honorable Mention (2006–2007). His work bridges computational and experimental methods, emphasizing practical applications in aerospace systems and environmental fluid dynamics. Key collaborations include investigations into wind turbine blade aerodynamics under varying inflow conditions and active flow control strategies for flexible wings. His labs emphasize interdisciplinary approaches to address challenges in aerodynamics, propulsion, and unmanned vehicle systems.

Zhilang Zhangمشاهده پروفایل

استاد

• Computational Mechanics
• Numerical Simulation
• Advanced Manufacturing
• +۳ مورد دیگر

Dr. Zhilang Zhang is a Professor at ETH Zurich, holding the Professorship for Advanced Manufacturing within the Department of New Manufacturing Technologies. His research focuses on computational mechanics, numerical simulation methods (SPH, FEM), advanced manufacturing processes, and process modeling. He specializes in high-fidelity modeling of complex phenomena such as additive manufacturing, material sintering, and fluid-structure interactions. Key research areas include multiscale modeling, CFD-DEM coupling, and novel numerical methods for extreme mechanics problems. His work integrates advanced computational techniques with experimental validations, addressing challenges in manufacturing, materials science, and fluid dynamics. Recent projects involve operando synchrotron tomography for melt pool analysis and parallelized SPH frameworks for large-scale simulations. Notable contributions include developing the Direct FE2 method for multiscale simulations and improving hydroelastic modeling via meshless methods. He actively explores applications of physics-informed neural networks and surrogate models in engineering optimization. His research group collaborates on cutting-edge projects at the intersection of computational engineering and advanced manufacturing, with a focus on sustainable and high-performance materials processing.

Farnaz Heidar-Zadehمشاهده پروفایل

استادیار

• Computational Chemistry
• Quantum Chemistry
• Machine Learning Applications in Chemistry
• +۳ مورد دیگر

Farnaz Heidar-Zadeh is an Assistant Professor in the Department of Chemistry at Queen's University, affiliated with the Faculty of Arts and Science. She holds cross-appointments at the Carbon to Metal Coating Institute. Her research integrates computational methods, quantum chemistry, and machine learning to predict chemical phenomena. Key areas include developing ChemTools, an open-source software package for interpreting quantum chemistry data, and adapting machine learning for molecular screening and dynamics simulations. Education: BSc in Pure Chemistry (Shahid Beheshti University), MSc in Physical Chemistry (Shahid Beheshti University and McMaster University), PhD in Theoretical Chemistry and Physics (McMaster University and Ghent University). Research Interests: Quantum chemistry, molecular dynamics, machine learning applications in chemistry, chemical reactivity theory, and software development for computational chemistry. Her group focuses on rigorous mathematical frameworks for empirical chemical concepts and efficient computational tools like ChemTools. Publications highlight contributions to Hirshfeld partitioning, Fukui functions, and software libraries such as Grid and GBasis. Her work addresses challenges in molecular interaction modeling and computational efficiency for large systems.

Kevin Slagleمشاهده پروفایل

استادیار

• Theoretical Quantum Matter
• Quantum Computing
• Quantum Simulators
• +۳ مورد دیگر

Kevin Slagle is an Assistant Professor in the Department of Electrical and Computer Engineering at Rice University. His research focuses on deep learning and emergent phenomena in quantum matter and fundamental physics, including quantum computing, quantum simulators, and neural network architectures. He holds a Ph.D. in theoretical condensed matter physics from the University of California, Santa Barbara (2016) and B.S. degrees in Physics and Mathematics from the University of California, Irvine (2011). Prior to Rice, he was a Postdoctoral Fellow at the University of Toronto and a Sherman Fairchild Postdoctoral Scholar at Caltech. Research Interests Theoretical Quantum Matter — Studying superconductivity, topological orders, and their experimental realizations in quantum simulators (e.g., Rydberg arrays and moiré materials). Quantum Computing — Exploring foundational aspects and applications of quantum computers, including testing quantum mechanics and characterizing noise. Neural Networks — Investigating architecture improvements and their intersections with physics. Publications Trends His recent work emphasizes quantum dynamics simulations using tensor networks, gauge theories in quantum systems, and the interplay between quantum spin liquids and topological phases. He also advances methodologies for testing quantum mechanics on NISQ devices and analyzing noise in quantum hardware. Advising & Grants Dr. Slagle's academic trajectory includes postdoctoral roles focusing on quantum matter and foundational physics. His research has been supported by grants exploring topics such as fracton order, moiré materials, and quantum simulation. He currently advises students in Rice University's Electrical and Computer Engineering department.

Mike Hudsonمشاهده پروفایل

استاد

• Observational Cosmology
• Galaxy Formation and Evolution
• Gravitational Lensing
• +۵ مورد دیگر

Mike Hudson is a Professor in the Department of Physics & Astronomy at the University of Waterloo, affiliated with the Waterloo Centre for Astrophysics and the Perimeter Institute. His research focuses on observational cosmology, galaxy formation/evolution, gravitational lensing, and cosmic flows. He measures dark matter/dark energy properties via these techniques and investigates galaxy star formation mechanisms. Education: PhD (Physics, University of Cambridge, 1993); BSc (Physics, McGill University, 1987). Research Interests: Gravitational lensing, cosmic flows, large-scale structure analysis, astrophysics/gravitation. His work includes satellite payload development and astronomy education initiatives. Recent Work Trends: Over 15+ publications (2020–2023) emphasize UNIONS survey data, weak/strong lensing analysis, dark matter halo modeling, and galaxy cluster dynamics. Key topics include dwarf galaxy discovery, quenching mechanisms, and peculiar velocity studies. Awards: 2005: Outstanding Performance Award (UW) 2003: Premier's Research Excellence Award 2002: CITA Senior Fellowship Teaching: Taught courses like PHYS 239 (Computational Physics), PHYS 349 (Advanced Computational Physics), and specialized astrophysics courses (PHYS 474, 782, 787). Focuses on analytical tools and observational techniques. Labs/Teams: Leads the Hudson Research Group and collaborates with the Thirty Meter Telescope Scientific Advisory Committee. Active in the Waterloo Centre for Astrophysics and Perimeter Institute networks.

Gustavo Assis da Silvaمشاهده پروفایل

استادیار

• Topology Optimization
• Stress Constraints
• Manufacturing Uncertainties
• +۵ مورد دیگر

Gustavo Assis da Silva is an Assistant Professor at Örebro University's School of Science and Technology, Department of Natural Sciences and Technology. His research focuses on topology optimization under uncertainties, stress constraints, and manufacturing tolerances. He leads research in the Mechanics and Materials group. Key research areas include robust design of compliant mechanisms, reliability-based optimization, and non-probabilistic methods for structural systems. His work addresses challenges in stress constraints, material variability, and geometric nonlinearities. His publications span top journals like Computer Methods in Applied Mechanics and Engineering and International Journal for Numerical Methods in Engineering , emphasizing computational mechanics and engineering design. No scientific awards or student advising records are listed. Active research groups include Mechanics and Materials.

Dong Ryeol Leeمشاهده پروفایل

استادیار

• Machine Learning
• Large-Scale Data Analysis
• Computational Geometry
• +۳ مورد دیگر

Dr. Dong Ryeol Lee is an Assistant Professor in the Data Science Department at Saint Peter's University. His research focuses on large-scale machine learning, geometric problems in natural science, web, and IoT applications. Previously, he worked as a research scientist on web search engines, fleet prognostics, and revenue forecasting models. He holds a Ph.D. in Computer Science from Georgia Institute of Technology (2012), M.S. in Mathematics (2011) from the same institution, and dual B.S. degrees in Mathematical Sciences and Computer Science from Carnegie Mellon University (2005). Education : B.S. Mathematical Sciences & Computer Science, Carnegie Mellon University (2005) M.S. Mathematics, Georgia Institute of Technology (2011) Ph.D. Computer Science, Georgia Institute of Technology (2012) Dr. Lee’s research bridges computational methods and real-world applications, emphasizing efficient algorithms for high-dimensional data and metric space problems. His work spans celestial body simulations, web search optimization, and predictive maintenance in IoT systems. Recent publications explore dual-tree algorithms, kernel summation frameworks, and multitree methods in astrostatistics. His articles reflect a focus on algorithm efficiency, statistical analysis, and cross-disciplinary applications. Notable trends include scalable machine learning infrastructure and geometric problem-solving in astronomy and computer vision. No scientific awards have been listed. He currently teaches DS-600 (Data Mining) and DS-630 (Machine Learning). No grants or advising records are mentioned. His academic contributions emphasize algorithmic innovation and industrial applicability without explicit lab affiliations detailed in the source text.