جست‌وجوی استاد و پژوهشگر

• Climate Modeling
• Atmospheric Physics
• Numerical Weather Prediction
• +۵ مورد دیگر

Monika Esch is a Researcher in the Department of Climate Physics at the Max Planck Institute for Meteorology in Hamburg, Germany, where she has been employed since 1991. She works in the Wave Driven Circulations research group, focusing on climate modeling and atmospheric physics. Her career began with a Diploma in Physics from the University of Hamburg in March 1988, followed by positions as a Scientist at the MPIfM (1988-1990) and GKSS Forschungszentrum (1990-1991) before transitioning to her current role as Scientific Programmer. Esch's research interests span climate modeling, atmospheric physics, numerical weather prediction, climate dynamics, tropical cyclone simulation, Earth system modeling, climate variability, and cloud physics. Her work primarily involves developing and implementing climate models, particularly focusing on the ICON (Icosahedral Nonhydrostatic) modeling framework. She has made significant contributions to understanding how microphysical processes affect tropical condensate, the simulation of Earth System components at high resolution, and the representation of quasi-biennial oscillation in climate models. Her publication record demonstrates consistent research activity from the late 1980s through 2025, with recent work focusing on high-resolution climate modeling, Earth system interactions, and the atmospheric response to increasing CO2. Her research shows a clear evolution from early work on ice sheet modeling and tropical cyclones toward more complex Earth system modeling at increasingly higher resolutions. While no specific scientific awards are mentioned in the available information, her extensive publication record in high-impact journals and consistent involvement in major climate modeling projects suggests recognition within the climate science community. Her collaborative work extends across multiple international research teams focused on advancing climate modeling capabilities. Esch has been instrumental in the development of the ICON modeling system and its various components, contributing to both the theoretical foundations and practical implementation of these models. Her technical expertise in scientific programming has enabled significant advances in climate simulation capabilities at the Max Planck Institute for Meteorology.

Anastasia Markinaمشاهده پروفایل

پژوهشگر ارشد

• Organic Photovoltaics
• Non-Fullerene Acceptors
• Charge Generation
• +۳ مورد دیگر

Anastasia Markina is a Research Fellow at the Max Planck Institute for Polymer Research, specializing in the design of non-fullerene acceptors for organic solar cells. She joined the institute's theory group in 2018, focusing on advancing photovoltaic efficiency through material science and computational modeling. Her research integrates experimental and theoretical approaches to understand exciton dynamics, charge generation mechanisms, and interfacial properties in organic semiconductors. Markina holds a PhD in Physics from Moscow State University (2017), where she developed hybrid simulation schemes for molecular systems. Prior to her postdoc, she interned at Schlumberger Moscow Research Center, optimizing nanopore flow estimation methods. Her academic background includes a Diploma in Condensed Matter Physics (Polymer Physics) from Moscow State University (2014). Her research interests span organic electronics, energy conversion materials, and nanoscale mechanics. Key contributions include identifying chemical design rules for non-fullerene acceptors, elucidating exciton diffusion in organic materials, and reducing charge recombination losses in solar cells. Recent work explores semitransparent photovoltaics leveraging intrinsic charge generation and stochastic resonance in nanoscale systems. Publications highlight advancements in charge transport physics, molecular architecture optimization, and device performance limits. Collaborations with groups led by Denis Andrienko and others emphasize interdisciplinary approaches to renewable energy materials. Current projects aim to bridge fundamental photophysics with practical applications in next-generation solar technologies.

• Materials Science
• Semiconductor Physics
• Nanotechnology
• +۳ مورد دیگر

Prof. Huixin (Helen) Xiu is an Associate Professor at the School of Materials and Chemistry, University of Shanghai for Science and Technology, China. She concurrently serves as a visiting scholar at the NG group, Max-Planck Institute for Sustainable Materials. Her research focuses on semiconductor materials, optoelectronics, and nanotechnology, with an emphasis on degradation mechanisms in GaN-based devices and advanced materials characterization techniques like atom probe tomography. She holds a PhD from the University of Cambridge (UK) and completed postdoctoral research at the National Institute for Materials Science (Japan). Education: PhD, University of Cambridge, UK (2004-2009) Bachelor and Master, University of Science and Technology Beijing, China (2001-2004) Research interests include semiconductor device reliability, nanomaterials for photocatalysis, and sustainable materials engineering. Her work spans atom-scale analysis of defects, optoelectronic device optimization, and environmental applications of advanced materials. Her publications highlight advancements in GaN laser diodes, graphene-based composites, and hydrogen evolution catalysts. She has contributed to understanding stacking fault impacts, Mg cluster formation in lasers, and synergistic effects in photocatalytic systems. Labs/Teams: Member of the Nanoanalytics and Interfaces research group at her university and collaborates with the NG group at Max-Planck Institute.

• Quantum Computing
• High-Performance Computing (HPC)
• Optimization Algorithms
• +۳ مورد دیگر

Katarzyna Rycerz is an academic researcher specializing in quantum computing, high-performance computing (HPC), and optimization algorithms. Her work spans hybrid quantum-classical systems, complex network analysis, and multiscale simulations. Collaborating with institutions such as AGH University of Science and Technology (inferred via co-authors like Marian Bubak), her research focuses on advancing quantum algorithms for optimization problems, quantum annealing applications, and software frameworks for distributed computing environments. Her notable contributions include developing libraries like QHyper for hybrid quantum-classical optimization, analyzing quantum walk-based image segmentation, and exploring the application of quantum computing to classical problems like the Traveling Salesman Problem. She has also contributed to foundational studies in quantum game theory and functional programming paradigms in HPC. Rycerz's publications reflect a sustained focus on interdisciplinary research, bridging quantum mechanics, computer science, and applied mathematics. Her work often addresses practical challenges in computational efficiency, algorithm design, and scalable simulation frameworks for complex systems.

• High Performance Computing
• Network Simulation
• Parallel Discrete Event Simulation
• +۵ مورد دیگر

Kevin A. Brown is an active researcher in the field of High Performance Computing with a strong publication record spanning over a decade. His work primarily focuses on HPC network simulation, performance analysis, and optimization of parallel computing systems. He has collaborated extensively with researchers including Christopher D. Carothers, Robert B. Ross, and Satoshi Matsuoka across multiple institutions. Dr. Brown's research interests center around network simulation techniques, particularly Parallel Discrete Event Simulation (PDES) for modeling HPC networks. His recent work explores multi-fidelity network simulation frameworks, surrogate modeling for performance prediction, and machine learning applications for network traffic forecasting. He has made significant contributions to understanding network congestion, quality-of-service mechanisms, and the interference between different types of traffic in HPC environments. His publication record shows consistent output with 19 publications documented between 2014 and 2025, with increased productivity in recent years. The 2023-2025 period shows particularly strong activity with 12 publications, indicating ongoing research momentum. His work appears primarily in top HPC conferences including SIGSIM-PADS, CLUSTER, and ICPP. Notable recent contributions include the development of MFNetSim for multi-traffic modeling of Dragonfly systems, research on zombie packet techniques for hybrid PDES simulation, and work on steady-state fluid models for HPC networks. His research demonstrates a clear trajectory from fundamental network performance analysis toward more sophisticated simulation frameworks incorporating machine learning techniques.

• High Performance Computing
• Energy Efficiency
• Parallel Computing
• +۶ مورد دیگر

Laura Carrington is a researcher at the University of California, San Diego, specializing in High Performance Computing (HPC) with a focus on energy efficiency, memory management, and performance optimization. She has contributed to the development of tools like PEBIL for binary instrumentation, ADAMANT for data movement analysis, and frameworks for power management in large-scale systems. Her research spans multiple domains including ARM processor evaluation, Xeon Phi vectorization, and communication reduction in graph algorithms. Key collaborations include work with Michael Laurenzano, Allan Snavely, Ananta Tiwari, and Pietro Cicotti. Laura's work addresses critical challenges in HPC such as DVFS configuration optimization, workload colocation, and energy-aware algorithm design. While no explicit academic rank is stated, her extensive publication record across 2002-2019 in top venues like SC, IPDPS, and IJHPCA establishes her as a significant contributor to HPC research. Her work has influenced practices in system-level power management, scientific application characterization, and energy-efficient computing for both CPU/DRAM domains and emerging memory technologies.

• Art Criticism
• Contemporary Art
• Digital Art
• +۳ مورد دیگر

Dawn Chan is a Senior Lecturer at the Center for Curatorial Studies, Bard College. She is a New York-based writer contributing to major publications like The New York Times, Artforum, and The New Yorker. Her work focuses on contemporary art, digital media, and curatorial practices. She has held editorial roles at MoMA and co-curated the exhibition Phantom Plane, Cyberpunk in the Year of the Future at Tai Kwun museum in Hong Kong. Chan’s research interests include intersections of art, technology, and politics. She has been affiliated with NYU’s Center for Experimental Humanities and has spoken at institutions like the Guggenheim Museum. Her writing has been anthologized by leading cultural institutions and she serves on the editorial team for November , a publication exploring art, media, and politics. Recipient of prestigious awards including the Warhol Arts Writers Grant and Fulbright Fellowship, her work emphasizes critical discourse in digital art and curatorial innovation. She has advised on exhibitions and contributed to monographs on artists like Ai Weiwei and Matthew Wong. Her recent articles analyze AI's role in art, transhumanism in religion, and futurist cultural movements.

Christiane Jockwitzمشاهده پروفایل

پژوهشگر

• Neuroimaging
• Cognitive Neuroscience
• Genetics of Brain Disorders
• +۳ مورد دیگر

Christiane Jockwitz is a Postdoctoral Researcher at the Institute of Neuroscience and Medicine (INM-1) within the Research Center Jülich GmbH, Germany. Her work focuses on the structural and functional organization of the brain, particularly in aging populations and neurodegenerative conditions. She leads research in neuroimaging genetics, cognitive neuroscience, and the effects of genetic variants on brain structure. Her studies frequently involve large-scale population cohorts such as the 1000BRAINS study to investigate aging biomarkers and vascular risk factors. Key research areas include brain aging mechanisms, the impact of lifestyle and environmental factors (e.g., air pollution, bilingualism) on neural networks, and the genetic underpinnings of neuropsychiatric disorders. She employs advanced methods like federated learning, machine learning, and multimodal neuroimaging analysis to predict cognitive decline and identify neurobiological correlates of diseases such as Alzheimer’s and Parkinson’s. Her work bridges clinical neuroscience and population health, with contributions to understanding sex differences in cognitive aging and the development of predictive models for neurodegenerative outcomes. Ongoing projects explore brain network reorganization in aging and the application of virtual brain modeling to simulate cognitive aging processes. Christiane Jockwitz’s findings have been published in high-impact journals, emphasizing translational applications of neuroimaging data for early disease detection and personalized medicine strategies.

Sebastian Peitzمشاهده پروفایل

استاد

• Multiobjective Optimization
• Optimal Control
• Machine Learning
• +۳ مورد دیگر

Sebastian Peitz is Professor (previously Assistant Professor) at Paderborn University's Department of Computer Science, leading the Data Science for Engineering group. He obtained his PhD in Multiobjective Optimization from Paderborn University and MSc in Mechanical Engineering from RWTH Aachen. His research develops computational methods for multiobjective optimization, optimal control, and machine learning with applications in fluid dynamics, autonomous systems, and industrial processes. He leads the BMBF-funded Multicriteria Machine Learning group. Research trends show consistent focus on Koopman operator theory, reinforcement learning applications in control systems, and physics-informed machine learning across publications. Achievements include the 2019 PRECEDE Best Paper Award and leadership in international optimization conferences.

Stephan Thoberمشاهده پروفایل

پژوهشگر

• Hydrology
• Climate Modeling
• Environmental Informatics
• +۳ مورد دیگر

Dr. Stephan Thober is a Group Leader of the HydroScientific Software Development (HSD) group at the Helmholtz Centre for Environmental Research - UFZ in Leipzig. He holds a PhD in Geosciences from Friedrich Schiller University of Jena and a Diploma in Mathematics from the University of Greifswald. His work focuses on advancing computational hydrosystems, particularly through the development and maintenance of the mesoscale Hydrologic Model (mHM). He is also a visiting scientist at the European Centre for Medium-Range Weather Forecasts (ECMWF) and leads the DestinE Climate Adaptation Digital Twin project. His research interests include hydrologic process representation, multiscale parameter regionalization, and climate change impacts on water resources. Thober has led projects such as HOKLIM, investigating high-resolution climate projections for Europe, and contributed to the Ulysses project for global hydrological forecasting. His expertise spans statistical downscaling, land-surface modeling, and drought analysis. He has secured over €4 million in funding for initiatives like calibration of ECLand models and hydrological seasonal prediction services. His work emphasizes interdisciplinary applications, integrating environmental informatics and climate adaptation strategies. Key achievements include developing drought indices for future climates, improving reservoir modeling techniques, and advancing metadata practices for simulation workflows. His team’s contributions to tools like HydroRiver and the mHM model have enhanced global hydrological understanding and climate resilience planning.

• 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.

• Oceanography
• Climate Dynamics
• Sea Ice Modeling
• +۳ مورد دیگر

Dr. Martin Losch is a Senior Scientist at the Alfred Wegener Institute (AWI) in the Climate Sciences department. His research focuses on numerical modeling of sea ice and ocean circulation, with expertise in sea ice dynamics, climate modeling, and data assimilation. He is a core developer of the MIT General Circulation Model (MITgcm) and has contributed to advancing sea ice rheology and ocean mixing processes. Education: Ph.D. in Physics from the University of Bremen (2000) and Diplom in Physics from the University of Hamburg (1997). Professional Experience: Postdoc at MIT (2001–2003) and AWI (2003–2008), transitioning to a permanent research role at AWI in 2008. Research Interests: Antarctic Circumpolar Current, sea ice-ocean interaction, turbulence parameterization, and physical controls on primary production. His work integrates computational modeling, observational data, and climate feedback mechanisms to address Arctic and Southern Ocean dynamics. Grants: Principal investigator on projects such as 'Response of the Southern Ocean biological carbon pump to climate-related changes in iron supply' and 'Energy transfers in gravity currents.' Collaborations include the MITgcm development team and international climate research initiatives. Teaching: Courses include 'Fundamentals of Ocean Circulation Theory' and 'Advanced Ocean Modelling.' Active in mentoring graduate students and postdocs in climate and ocean science.

• Interfacial dynamics
• Multiphase flow
• Pattern formation
• +۲ مورد دیگر

Dr.-Ing. Karin Schwarzenberger is Group Leader for Interfacial Phenomena at Helmholtz-Zentrum Dresden-Rossendorf (HZDR) and since 2020 deputy group leader for the Chair of Transport Processes at Interfaces at TU Dresden. She has been continuously affiliated with TU Dresden’s Faculty of Mechanical Engineering since 2003, advancing from diploma studies through PhD to post-doctoral and leadership roles. Education: Doctoral studies (Dr.-Ing.), TU Dresden, Faculty of Mechanical Engineering, 2010–2015 Thesis: “Experimental Investigations on Stationary Solutal Marangoni Instability” Diploma in Process Engineering, TU Dresden, 2003–2010 Diploma Thesis: “Calculation of Interaction Energies between Fractal Aggregates” Research Interests Karin Schwarzenberger’s work centers on interfacial transport phenomena, pattern formation, and multiphase flows. Key areas include: Interfacial dynamics at liquid/liquid and liquid/gas boundaries Particle–particle and particle–interface interactions Solutal Marangoni instabilities and convective structures Coacervation and complexation of polymers and surfactants Microgravity effects on buoyancy-driven instabilities Her experimental approach combines high-resolution optical diagnostics with numerical simulations to unravel the coupling between mass transfer, surface tension gradients, and hydrodynamic instabilities. Publication Trends Between 2012 and 2025 Schwarzenberger has published more than 30 peer-reviewed articles. Recent work (2022–2025) demonstrates a clear shift toward applied interfacial engineering—surface functionalization of Ti64 alloys, foam fractionation for protein purification, and coacervation-based membrane formation—while maintaining fundamental studies on Marangoni convection and buoyancy-driven instabilities under microgravity. Scientific Awards No specific awards are mentioned in the provided materials. Advising & Funding While the text does not list individual students, her role as group leader at HZDR and deputy chair leader at TU Dresden implies supervision of doctoral researchers and technical staff. Funding is implicitly provided through Helmholtz Association programmes and collaborative projects with TU Dresden. Labs & Teams She heads the Interfacial Phenomena group at HZDR, located in building 310 at Bautzner Landstrasse 400, Dresden. The group operates state-of-the-art optical measurement techniques (PIV, LIF, high-speed imaging) and performs experiments both in the laboratory and under microgravity conditions (parabolic flights).

• Machine Learning
• Biomedical Engineering
• Human-Computer Interaction
• +۴ مورد دیگر

Woojin Kim is a Professor in the Department of Mathematics at Duke University, Durham, NC, USA. His research spans interdisciplinary domains including Machine Learning , Biomedical Engineering , Human-Computer Interaction , and Wireless Sensor Networks . He has made significant contributions to medical imaging analysis , autonomous driving systems , and AI in education , with recent work focusing on fairness evaluation in machine learning and vision-language models for healthcare applications . His publications demonstrate expertise in: AI-driven medical diagnostics (CT scans, MRIs) Exoskeleton and robotic systems Autonomous vehicle transition dynamics Privacy-preserving clinical data processing Memory hardware testing frameworks Knowledge tracing algorithms in education Recent article trends highlight increasing emphasis on AI ethics (fairness evaluation), multimodal models (vision-language), and knowledge graph applications for scientific data organization. Collaborations with interdisciplinary teams across biomedical, engineering, and computer science domains underscore his cross-domain impact.

• Internet of Things
• Software Engineering
• Machine Learning
• +۵ مورد دیگر

Jorge Eduardo Ibarra Esquer is an Associate Professor in the Department of Computer Science at the School of Engineering, Universidad de Sonora, Mexico. With a publication record spanning nearly two decades from 2006 to 2023, he has established himself as an active researcher in multiple domains of computer science. His work demonstrates consistent collaboration with colleagues Brenda Leticia Flores Ríos, María Angélica Astorga Vargas, and Félix Fernando González-Navarro across numerous publications. His research interests focus on the Internet of Things , Software Engineering , Machine Learning , and Educational Technology . Recent work has explored IoT object categorization, user engagement analysis on social media for scientific dissemination, and software development education during the pandemic. Earlier research examined biosensor modeling, game playing preferences, and driving safety through gamification. His publications appear in reputable venues including IEEE Access, Sensors, and the Colombian Journal of Computing, as well as at national conferences like ENC (Encuentro Nacional de Computación). Analysis of his recent publications (2020-2023) reveals a strategic focus on applying machine learning to practical problems in IoT, educational technology, and social media analytics. His work bridges theoretical computer science with real-world applications, particularly in the Mexican higher education context as evidenced by studies on Facebook engagement at a higher education institution. The interdisciplinary nature of his research spans from healthcare applications (biosensors, physiological monitoring) to educational technology and software engineering practices. While no specific scientific awards are documented in the available publication records, his consistent publication output across multiple high-quality venues demonstrates recognition within his academic community. His research has practical implications for software development education, IoT applications, and human-computer interaction design. Dr. Ibarra Esquer's work shows strong collaborative patterns, particularly with researchers from his home institution. His recent publications suggest active involvement in research teams focusing on educational technology applications during the pandemic, social media analytics for scientific communication, and IoT applications. The continuity of his research program since 2006 indicates sustained scholarly productivity and relevance in evolving technological domains.