جست‌وجوی استاد و پژوهشگر

Emiliano Ippolitiمشاهده پروفایل

پژوهشگر

• Molecular Dynamics
• High Performance Computing
• Quantum Mechanics/Molecular Mechanics (QM/MM)
• +۳ مورد دیگر

Dr. Emiliano Ippoliti is a Senior Scientist at the Institute of Neuroscience and Medicine (INM-9: Computational Biomedicine) at Forschungszentrum Jülich GmbH , Germany. His primary focus is on advancing high-performance computing methodologies for biological systems, particularly classical and ab initio molecular dynamics , and developing tools like the MiMiC framework for multiscale simulations. He holds a Master's in Physics (University of Pisa, 2001) and a PhD in Theoretical Physics (University of Trieste, 2005), followed by postdoctoral research at SISSA/ISAS and the German Research School for Simulation Sciences. His research integrates quantum mechanics/molecular mechanics (QM/MM) , exascale computing , and drug design , addressing challenges in membrane transport, enzyme mechanisms, and drug permeation. Recent work includes studies on CLC transporters, PETase enzyme dynamics, and computational drug screening. He has authored over 40 publications and serves as a referee for PRACE and journals such as Journal of Chemical Theory and Computation . Key contributions include: Developing MiMiC and MiMiCPy for scalable QM/MM simulations Advancing understanding of proton transport in biological systems Modeling drug interactions with cell membranes and ion channels No specific awards are listed, but his work is recognized through extensive peer-reviewed output. He collaborates internationally in exascale computing initiatives and contributes to the Computational Biomedicine group at Jülich.

• Seismology
• Geophysics
• Probabilistic Seismic Hazard Assessment (PSHA)
• +۴ مورد دیگر

Pablo Cristián Iturrieta is a Researcher at the Section 2.6 Seismic Hazard and Risk Dynamics , GFZ German Research Centre for Geosciences , affiliated with Universität Potsdam . His work focuses on Seismology , Probabilistic Seismic Hazard Assessment (PSHA) , and Earthquake Forecasting , with a specialization in modeling seismicity rates, stress field disruption, and fault interactions in tectono-volcanic systems. Education : PhD in Earth Science (2024), Universität Potsdam Research Affiliations : GFZ German Research Centre for Geosciences, Department of Earth Sciences, Universidad Potsdam His recent publications address seismic hazard models for New Zealand , statistical power of forecast tests , geothermal fluid dynamics , and non-Poissonian seismicity . Collaborative work spans the CSEP (Collaboratory for the Study of Earthquake Predictability) framework and tectonic implications of fault zones in the Andean Southern Volcanic Zone . Key contributions include multi-resolution grid approaches and uniform rate zone forecasts for hazard assessment. His articles emphasize earthquake rate variability , fluid-magma interactions , and software modernization for forecast testing. Collaborations include institutions in Germany, New Zealand, Italy, and Chile. No scientific awards or formal student advising details are explicitly mentioned in the provided texts.

Andreea Molnarمشاهده پروفایل

دانشیار

• Computing for Social Good
• Gender and Diversity in Information Technology
• Healthcare Information Systems
• +۳ مورد دیگر

Dr. Andreea Molnar is an Associate Professor at Swinburne University of Technology's School of Science, Computing and Emerging Technologies. She holds the Anna Boyksen Fellowship at the Technical University of Munich (TUM-IAS) and is a Fellow of the International Science Council. Her research focuses on computing for social good, gender diversity in IT, and health-related information systems. Dr. Molnar earned her PhD from the National College of Ireland and has held positions at Lancaster University, University of Portsmouth, and Arizona State University. Education: PhD (Technology Enhanced Learning, National College of Ireland), MSc (Modelling & Simulations), BSc (Computer Science) Professional Roles: Co-Chair of Anita Borg Institute's Pass It On Awards (2015-2020), Global Health Project Lead at Global Young Academy Her work addresses gender equity in IT education and the ethical use of AI. Key publications include analyses of diversity initiatives and generative AI ethics in computing education. She has secured grants such as the Identifying the Initiatives that Influence 1st Year Female Students Choosing Computing Degrees (ACDICT, 2018-2019) and collaborates on projects like the Glen Eira Social Data Analytics Dashboard. Awards include the Science & Technology Australia scholarship, Adobe Innovation Award (2021), and recognition as a Senior Editor for Information Technology & People .

• Quantum Computing
• Optimization Methods
• Smart Grid Technologies
• +۵ مورد دیگر

Junpeng Zhan is an Assistant Professor of Renewable Energy Engineering at the Inamori School of Engineering, Alfred University, NY, USA. He holds a Ph.D. and B.Sc. in Electrical Engineering from Zhejiang University (2014 and 2009). Before joining Alfred, he worked as a Research Associate at Brookhaven National Laboratory (2018–2020), a Postdoctoral Fellow at the University of Saskatchewan and Hong Kong Polytechnic University, and a Visiting Research Assistant at the University of Liverpool. His research focuses on quantum computing, optimization methods, smart grid technologies, machine learning, and renewable energy integration. Notable contributions include developing the Quantum Feasibility Labeling algorithm and the hybrid Variational Quantum Search, which address NP-complete problems like the Vertex Coloring problem. He currently leads NSF-funded projects on variational quantum algorithms for power system simulation and ISO-New England-funded work on quantum computing for unit commitment. Zhan is an Associate Editor of IET Generation, Transmission & Distribution and actively reviews for 20+ journals. His grants include $199,980 from the NSF (2022), $50,000 from ISO-New England (2024–2026), and a co-PI role in a $1.37M NYSERDA-funded workforce training initiative. His work bridges fundamental research and practical energy system challenges, emphasizing computational innovation and scalability. Labs and collaborations include the Engineering Laboratories at Alfred University, with partnerships extending to institutions like RIT and GE. His teaching spans courses in power systems, Python for research, and mechatronics.

• Social Hierarchy
• Social Networks
• Anthropology
• +۳ مورد دیگر

Daniel Redhead is a Researcher at the Max Planck Institute for Evolutionary Anthropology within the Department of Human Behavior, Ecology and Culture. He holds a PhD in Psychology from the University of Essex and advanced degrees in Anthropology from Durham University. His research examines social hierarchy dynamics through interdisciplinary approaches combining anthropology, psychology, and network analysis. Research interests focus on: Evolution of social hierarchies and status acquisition Cross-cultural analysis of cooperation networks Computational modeling of group dynamics Longitudinal studies in diverse populations including indigenous communities Development of methodological tools for social network analysis His publications (2019-2025) demonstrate consistent focus on social networks, inequality, and cultural transmission, with methodological innovation in statistical modeling and field research. Recent works emphasize computational approaches to network analysis and cross-cultural examinations of cooperation. Awards and honors include: ESRC Research Methods Development Grant (Co-I, £200K, 2020) European Human Behavior & Evolution Association Award (2017) Experimental Psychology Society Study Visit Award (2016) Multiple University of Essex research grants and scholarships Teaching experience includes graduate instruction in Social Network Analysis and Research Methods at University of Essex. Professional activities encompass peer review for major journals and memberships in anthropological/psychological associations. Fieldwork spans four continents including ongoing longitudinal research with Mayangna communities in Nicaragua.

Pia Ebelingمشاهده پروفایل

پژوهشگر

• Hydrology
• Environmental Science
• Catchment Hydrology
• +۶ مورد دیگر

Dr. Pia Ebeling is a Researcher at the Department of Hydrogeology, Helmholtz Centre for Environmental Research - UFZ in Leipzig, Germany. She holds a Master of Science in Hydrology (2018) and a Bachelor of Science in Hydrology (2014), both from Technical University Dresden. Her research focuses on water quality dynamics, catchment-scale nutrient interactions, data-driven hydrological analysis, and groundwater dynamics under climate variability. She has been involved in international research stays, including at INRAE in France (2018-2022), and has contributed to projects such as CAMELS-DE and QUADICA, which compile extensive hydro-meteorological datasets for German catchments. Dr. Ebeling has received the Karl-Franz-Busch Medal (2018) for her Master’s thesis on seawater intrusion remediation. She collaborates with interdisciplinary teams in the Helmholtz program 'Changing Earth - Sustaining our Future', particularly in topics like coastal zones, landscapes of the future, and sustainable bioeconomy. Her work emphasizes developing Python-based modeling tools, integrating machine learning for environmental prediction, and addressing global challenges in water security and ecosystem health. Education: Bachelor of Science in Hydrology, Technical University Dresden (2014) Master of Science in Hydrology, Technical University Dresden (2018) Research Interests: Quantifying groundwater dynamics and drought patterns Examining nutrient depletion and stoichiometric imbalances in streams Advancing large-sample hydrological analysis through data synthesis Modeling subsurface processes under climate change scenarios Developing open-source tools for hydro-ecological research Advising & Grants: Completed PhD under the supervision of Prof. Jan Fleckenstein at UFZ (2022) Contributed to interdisciplinary research initiatives like Helmholtz TRACER PhD school Involved in international collaborations, including with the University of Texas at Austin’s hydrology team Labs/Teams: Member of the Hydrogeology Department team at UFZ Contributor to the 'Healthy Planet' and 'Landscapes of the Future' research topics Developed the HydroEcoLSTM package for hydro-ecological modeling

• Earth System Modeling
• Scientific Programming
• Climate Science
• +۳ مورد دیگر

Nadine Wieters is a Researcher at the Alfred Wegener Institute (AWI) in Bremerhaven, Germany, specializing in Climate Dynamics. Her primary affiliation is within the Climate Sciences division, focusing on Earth System Modeling and Scientific Programming. She contributes to the development of modular infrastructure for coupled Earth system models through projects like ESM-Tools. Her research emphasizes advancing computational frameworks to enhance model interoperability and efficiency. Key collaborations include work on the AWI-CM model prototype and the esm-interface library. Wieters actively participates in international conferences such as EGU General Assemblies and deRSE events, presenting advancements in software tools for climate research. With a focus on software engineering for climate science, her work bridges computational methods and environmental modeling, supporting both standalone and coupled simulations. She is based at the AWI's Bremerhaven site, contributing to the institute's broader mission in polar and marine research through innovative technical solutions.

• Bioinformatics
• Cryptography
• Genetic Algorithms
• +۵ مورد دیگر

Mohammad Nassef is an academic researcher with a focus on interdisciplinary applications combining computer science, bioinformatics, and cryptography. His work spans multiple domains, including Developing bio-inspired algorithms for audio and image encryption Optimization techniques in genetic data analysis Automated scoring systems in educational software Edge detection methods for biomedical imaging Research Trends: His publications highlight a consistent integration of biological sequence modeling into computational algorithms, with recent work emphasizing cybersecurity, data compression, and machine learning applications in genetics. Collaborations with researchers like Ibrahim Farag, Amr Badr, and Monagi H. Alkinani indicate a networked approach to solving complex problems in data security and medical informatics.

• Data Science
• Machine Learning
• Distributed Computing
• +۴ مورد دیگر

Chris Mattmann is an academic affiliated with the University of Southern California, specializing in data science, machine learning, and high-performance computing. His work spans interdisciplinary applications in Earth sciences, climate modeling, and distributed systems. He has contributed to projects like the Earth System Grid Federation and Apache open-source software. His research emphasizes scalable data management, secure parser development, and automated machine learning systems. Education & Background: While specific educational details aren't listed, his extensive publication record in top-tier journals and conferences indicates advanced training in computer science and computational science. Research Interests: Focus areas include distributed computing infrastructure for scientific applications, machine learning for geosciences, and data-intensive systems. He explores challenges in data movement, scalable supercomputing, and open-source tools for scientific workflows. Publications: Over 150 peer-reviewed articles since 2003, with recent work on super-resolution satellite data, deep learning in geosciences, and secure parser systems. His contributions highlight advancements in both foundational computing and applied environmental science.

• Multimodal Interaction
• Artificial Intelligence
• Human-Computer Interaction
• +۳ مورد دیگر

Hung-Hsuan Huang is an academic researcher focused on multimodal interaction, embodied conversational agents, and educational technology. His work spans topics such as virtual agent behavior, gamified learning systems, and nonverbal communication analysis. Collaborating frequently with institutions like Osaka University, he has published extensively in venues including HCI, ICMI, and IVA conferences. His research often integrates machine learning techniques with human-computer interaction challenges, particularly in educational and collaborative environments. Key research areas include: (1) Designing virtual agents for tutoring and teacher training, (2) Multimodal assessment systems using speech and body language analysis, (3) Gamification mechanisms for knowledge refinement and skill evaluation, and (4) Cross-cultural interaction frameworks for virtual guides. Recent work emphasizes applications in language proficiency assessment and teacher training simulations. Notable contributions include developing frameworks for integrating embodied conversational agents, simulating group discussion dynamics, and creating virtual classroom environments for skill evaluation. His methodologies often combine deep learning with behavioral analysis to address challenges in human-agent collaboration.

• Software Engineering
• Empirical Software Engineering
• Metamorphic Testing
• +۳ مورد دیگر

Dietmar Pfahl is a Professor affiliated with the University of Tartu, Estonia, and the University of Calgary, Canada. His work bridges academia and industry through empirical software engineering, metamorphic testing, and agile methodologies. Key Roles: Editor/Author in conferences like PROFES, ICST, MOBILESoft. Current Affiliation: University of Tartu (Estonia) and University of Calgary (Canada). Research Interests focus on metamorphic testing , automated driving system safety , dependency network analysis , and agile software development . Recent work includes: Metamorphic test pipelines for optimization software. Dependency ecosystem impacts of package managers. LLM-based feature-sentiment extraction from app reviews. Scientific Contributions span over 219 publications since 1994, with 2025 entries addressing: Safety testing frameworks for automated driving. Test data-driven metamorphic relation selection. Industry experiments on test-driven development. His awards and leadership include organizing PROFES conference proceedings and advocating for software freedom.

Mark Bangertمشاهده پروفایل

پژوهشگر

• Radiation Therapy Physics
• Intensity-Modulated Radiation Therapy
• Beam Angle Optimization
• +۴ مورد دیگر

Mark Bangert is a researcher in the Department of Medical Physics in Radiation Oncology at the German Cancer Research Center (DKFZ) in Heidelberg, Germany. His work focuses on optimizing radiation therapy treatments through advanced computational methods and physics principles, particularly for complex cases involving critical organs at risk. Dr. Bangert completed his PhD at Heidelberg University in 2011 with a dissertation titled 'New concepts for beam angle selection in IMRT treatment planning: From heuristics to combinatorial optimization.' His doctoral work was supervised by Prof. Uwe Oelfke and Prof. Wolfgang Schlegel, established experts in medical physics and radiation oncology. His research spans radiation therapy physics, intensity-modulated radiation therapy (IMRT), beam angle optimization, proton therapy, and treatment planning. Dr. Bangert pioneered methods for selecting optimal beam angles in IMRT, demonstrating that noncoplanar beam configurations could significantly reduce radiation doses to organs at risk while maintaining target coverage. His early work established the importance of integrating fluence optimization into beam angle selection. Analysis of his publication history reveals an evolution from traditional IMRT optimization to sophisticated multi-modality treatment approaches combining photons with particle therapies (protons, carbon ions, and helium). His recent work emphasizes probabilistic modeling to address range uncertainties in particle therapy, analytical dose calculation methods, and the integration of biological optimization into treatment planning. He has made significant contributions to open-source software development for radiation therapy planning education. Dr. Bangert has played a key role in developing matRad, an open-source multi-modality treatment planning toolkit designed for educational purposes. This software provides accessible tools for students and researchers to explore radiation therapy planning concepts without commercial constraints. His work bridges theoretical optimization methods with practical clinical applications, improving treatment precision while minimizing damage to healthy tissues.

Martin Schrönمشاهده پروفایل

پژوهشگر

• Environmental Science
• Hydrology
• Remote Sensing
• +۳ مورد دیگر

Dr. Martin Schrön is a postdoctoral researcher at the Helmholtz Centre for Environmental Research (UFZ) in Leipzig, Germany, specializing in cosmic-ray neutron sensing (CRNS) for environmental monitoring. His work focuses on advancing CRNS technology to improve soil moisture and snow water equivalent measurements at various spatial and temporal scales. Schrön holds a PhD from the University of Potsdam and has conducted research at the University of Bristol. He is involved in projects such as MOWAX (water budget assessment in Saxony) and COCAP (pandemic coping strategies), and contributes to software development for neutron simulation (URANOS) and environmental modeling (mHM). His education includes a Physics degree from the University of Heidelberg and postdoctoral research at the University of Greifswald and UFZ. He has supervised multiple students and won awards like the Klartext Award for science communication (2018) and the UFZ Research Award (2017). His research bridges geophysics and environmental science, with applications in climate change, hydrology, and remote sensing. Education: Physics (Heidelberg University), PhD (University of Potsdam) Affiliations: Department of Computational Hydrosystems, UFZ Leipzig Key Projects: CRNS network in Saxony, Svalbard cosmic-ray observations, Low-water strategy for Thuringia Research interests include CRNS applications in soil moisture dynamics, neutron transport modeling, and environmental data integration. He has developed software tools and contributed to international collaborations, emphasizing science communication through FameLab and ScienceSlam.

Philip Joschkoمشاهده پروفایل

پژوهشگر

• Business Process Management
• Process Simulation
• IT Project Management
• +۳ مورد دیگر

Dr. Philip Joschko is an expert in Business Process Management (BPM), IT project management, and process simulation. He holds a PhD from the University of Hamburg (2014) and a Diplom in Computer Science with a focus on organizational systems. His academic work includes developing the DESMO-J simulation framework and researching offshore wind farm maintenance processes. Current roles include Senior IT Consultant at cimt ag (since 2023) and freelance business process analysis. PhD: University of Hamburg (2014) – Process simulation integration with BPMN Diplom: University of Hamburg (2008) – Computer Science with organizational systems focus Research interests focus on process optimization through digital transformation, including BPMN modeling, discrete event simulation (DESMO-J), and offshore wind operations. His work bridges academic research (e.g., maintenance decision support systems) with industry applications in logistics, energy, and IT. Publications emphasize practical applications of simulation tools and process analysis in renewable energy and logistics sectors. Projects include EU-funded offshore wind farm O&M optimization and DESMO-J framework development.

Katinka Wolterمشاهده پروفایل

استاد

• Dependable Distributed Systems
• Stochastic Models
• Performance Evaluation
• +۶ مورد دیگر

Prof. Katinka Wolter is a Professor at Freie Universität Berlin's Department of Mathematics and Computer Science, leading the Dependable Distributed Systems group. Her research focuses on adaptive and resilient distributed computing systems, leveraging stochastic models and machine learning. She is the Erasmus Coordinator for international student exchanges and serves on the department board and exam committee. Education: PhD in Computer Science (TU Berlin, 1999), Habilitation in Computer Science (Humboldt University Berlin, 2008). Previous roles include Visiting Professorships at Imperial College London and Cochin University, and positions at Newcastle University and Humboldt University. Research Interests: Evaluating dependability, performance, and security of complex systems, stochastic modeling, fault-tolerance, blockchain technology, and phase-type distributions. Tools developed include HyperStar and HyperStarC for distribution fitting. Awards: Best Paper Awards at ICPE 2015 and QEST 2004, DFG-funded research projects, and recognition as a top reviewer for the Journal of Systems and Software. Grants: Multiple DFG-funded projects on system evaluation and service-oriented architectures. Collaborations with institutions like Imperial College London and Technical University Budapest. Labs/Teams: Head of Dependable Systems Group, managing projects on cryptocurrency analysis, distributed restart algorithms, and mobile offloading systems.