جست‌وجوی استاد و پژوهشگر

Ali Shojaieمشاهده پروفایل

استاد

• Biostatistics
• Statistical Learning
• Machine Learning
• +۴ مورد دیگر

Ali Shojaie is a Professor of Biostatistics & Statistics at the University of Washington , currently serving as Interim Chair of Biostatistics. His research bridges statistical learning, network analysis, and high-dimensional data modeling with applications in biological and health sciences. Research Focus: Shojaie develops advanced methodologies for causal inference, spatial statistics, and semi-supervised learning. His recent work includes network-based gene set analysis, Granger causality estimation, and regularization techniques for complex data structures. Scientific Contributions: Received the 2022 Leo Breiman Award for innovative statistical learning research Elected as Fellow of the Institute for Mathematical Statistics (IMS) and American Statistical Association (ASA) Secured major NIH grants for projects on gene-phenotype associations and explainable AI in neuroscience Advising & Leadership: His students have won multiple awards at ASA and AISTAT conferences. He co-developed the netgsa and spacejam R packages for network-based analysis and spatial modeling. Current Projects: Shojaie leads NIH-funded research on prefrontal brain stimulation and gene knockout studies, combining statistical theory with interdisciplinary applications in biology and bioengineering.

• Urban Modeling
• Urban Morphology
• Explainable AI
• +۳ مورد دیگر

Dongsheng Chen is a researcher at the Technical University of Munich (TUM) within the Chair of Cartography and Visual Analytics under Prof. Dr.-Ing. Liqiu Meng. His work focuses on urban modeling, urban morphology, and explainable AI (XAI) for sustainable urban systems, applying GIS, remote sensing, and deep learning techniques. Research Interests: Urban mechanism, geo-knowledge graphs, sustainable development goals (SDGs), and integration of AI with urban studies. Key Projects: Modeling urban morphology for SDG 11 compliance, developing frameworks for carbon emission forecasting (CarbonVCA), and urban slum mapping via data fusion. Publications: Recent works include explainable graph neural networks for urban function analysis, vector landscape indices (VecLI), and causality-driven approaches to AI ethics and spatial modeling. Supervision: Advised Master's theses on slum mapping and participatory urban planning. Contact: Room 0507.01.767, dongsheng.chen@tum.de

Oldrich Rakovecمشاهده پروفایل

پژوهشگر

• Hydrology
• Climate Change
• Drought Analysis
• +۳ مورد دیگر

Oldrich Rakovec is a Researcher at the Department of Computational Hydrosystems (Helmholtz Centre for Environmental Research - UFZ, Germany). His work focuses on hydrological modeling , climate change impacts , drought analysis , and flood forecasting using the Mesoscale Hydrologic Model (mHM) . Key collaborations: NCAR (USA), Czech University of Life Sciences Prague, Kansas University, Bocconi University Research Interests : Parameter regionalization in large basins Paleo-hydrology and continental water cycles Model sensitivity analysis and uncertainty quantification Flood impact assessments Climate change effects on water resources Article Trends : His recent publications (2021-2025) emphasize climate change impacts on droughts/floods, global hydrological modeling , data assimilation , and multi-model intercomparison . Notable work includes drought monitoring tools for South Asia, flood counterfactuals for disaster preparedness, and high-resolution soil moisture datasets . Scientific Awards : ASCE-EWRI 2023 Best Case Study Award Education : PhD in Hydrology (Wageningen University, 2014), MSc in Hydrology and Water Quality (Wageningen), MSc in Environmental Modeling (Czech University of Life Sciences Prague).

• Geospatial Digital Twins
• Virtual Geographic Environments
• Remote Sensing
• +۷ مورد دیگر

Jun Zhu is a professor at the Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, China. His research is centered on geospatial digital twins, virtual geographic environments, and intelligent visualization for disaster risk management, with strong interdisciplinary work in AI, remote sensing, and VR-based simulation. His research interests include: Geospatial Digital Twins Virtual Geographic Environments AI for Remote Sensing 3D and VR-based Disaster Visualization Knowledge Graphs in GIS Public Risk Communication The recent articles (2023–2025) demonstrate a strong trend in integrating large language models, knowledge graphs, and deep learning with geospatial data to build intelligent, interactive, and immersive systems for infrastructure monitoring, disaster simulation, and public engagement. His work emphasizes data-knowledge fusion, human-centered visualization, and real-world applicability in urban and environmental contexts. Scientific Awards: No awards listed in the provided text. Advising and Grants: While Jun Zhu has extensive collaboration with researchers such as Weilian Li, Qing Zhu, Yakun Xie, and Jianbo Lai, and appears to lead research projects, there is no explicit mention of student advising, grant funding, or project titles in the provided data. Labs and Teams: Jun Zhu is likely part of a research group focused on digital twins and geospatial AI at Southwest Jiaotong University, collaborating closely with colleagues in geoinformatics and remote sensing, though specific lab names are not mentioned.

Mohammad Sadoghiمشاهده پروفایل

استاد

• Database Systems
• Distributed Systems
• Blockchain Technology
• +۵ مورد دیگر

Mohammad Sadoghi is a Professor in the Department of Computer Science at University of California, Davis, where he leads the Exploratory Systems Lab. His research spans database systems, distributed computing, and blockchain technologies with over 54 publications from 2007-2025 and more than 900 citations. His primary research domains include: Distributed database transactions Byzantine fault tolerance Consensus protocols Event processing systems Blockchain applications Database indexing techniques Prof. Sadoghi's publication trajectory shows evolution from foundational work on boolean expression indexing and event processing to cutting-edge research on blockchain consensus mechanisms. His recent work (2023-2025) demonstrates significant contributions to understanding BFT protocols, with publications in top venues like VLDB, EuroSys, and IEEE TKDE. His research bridges theoretical analysis with practical implementations, particularly focusing on performance optimization and security in distributed environments. His notable recognition includes: ACM Senior Member (2020) Prof. Sadoghi has advised multiple doctoral students who have become active researchers in distributed systems, including Suyash Gupta and Thamir M. Qadah. His lab has secured research funding for projects spanning database engines, consensus protocols, and blockchain infrastructure. The Exploratory Systems Lab maintains strong industry and academic collaborations worldwide, with recent work focusing on edge-cloud consensus applications and high-performance data management systems.

• Database Systems
• Data Management
• Recommender Systems
• +۴ مورد دیگر

Sihem Amer-Yahia is a distinguished Research Professor at the University of Grenoble Alpes (affiliated with Grenoble Informatics Laboratory ), with significant contributions to database systems , data exploration , and fairness in AI . Her work bridges human-computer interaction and machine learning to create systems that enhance data-driven decision-making. Research Pillars : Algorithmic fairness, interactive data mining, recommender systems, and human-AI collaboration Recent Advances : 2023-2025 publications focus on statistically sound hypothesis testing , multi-objective recommendation , and conversational analytics Leadership : Co-organized major conferences (DASFAA 2024) and led DEI initiatives in database communities Her 15 most recent articles (2020-2025) span topics like producer fairness in recommendation , statistical hypothesis frameworks , and AI-powered education systems , with keywords covering database optimization , reinforcement learning , and ethical data mining . She actively contributes to ACM/IEEE journals and VLDB/SIGMOD conferences.

• Data Visualization
• Digital Humanities
• Urban Complexity
• +۷ مورد دیگر

Prof. Dr. Marian Dörk is a Research Professor for Information Visualization & Management at the Potsdam University of Applied Sciences , where he co-directs the Urban Complexity Lab (UCLAB) since 2013. His work bridges data visualization , digital humanities , and critical design through projects like GraDiM (2023–2025) and Restaging Fashion (2020–2023). He teaches interface design and urban futures , supervising students on topics like OpenStreetMap cartography and graph-based narratives . PhD in Computer Science (2008–2012) from University of Calgary, supervised by Sheelagh Carpendale and Carey Williamson Prior roles: Research Associate at Newcastle University (2012–2013), internships at Google, Microsoft, and IBM His research focuses on critical data visualization , cultural heritage , and urban complexity , with a specific emphasis on ethical design , intersectional frameworks , and collaborative tools . Recent publications explore unfoldable graph visualizations (2023), climate journalism (2023), and discursive annotations (2025). Collaborators include institutions like Jewish Museum Berlin , SMB/SPK , and Open-Access-Büro Berlin . Key scientific awards include the Best Paper Award at VDA 2011 and Best Poster Award at AITF Tech Futures Summit 2011. His projects often involve students, exemplified by the Close-Up Cloud (2018–2020) and Amazonien as Future Lab (2021–2024). He advocates for freelance web development in academic contexts since 2001.

• Plate Tectonics
• Geology
• Geophysics
• +۴ مورد دیگر

Maryline Moulin is a prominent geophysics researcher specializing in plate tectonics, continental margin evolution, and integrated seismic analysis. She has led major oceanographic campaigns including PAMELA-MOZ05/03 and MAGIC projects, focusing on passive margins in the South Atlantic and East African regions. 2003 PhD from University of Western Brittany 2016 Project Leader for PAMELA campaigns Key collaborator in 15+ international seismic studies Her research spans diverse methodologies: Wide-angle seismic (OBS) profiling Multi-channel seismic (MCS) interpretation Gravity and magnetic anomaly analysis 3D structural modeling (Placa3D) Seismo-stratigraphic analysis Recent publications (2023-2025) reveal patterns in: Proto-oceanic crust formation Passive margin segmentation Quaternary contourite systems Transform margin tectonics Deep crustal architecture

Thomas Villmannمشاهده پروفایل

استاد

• Computational Intelligence
• Machine Learning
• Vector Quantization
• +۷ مورد دیگر

Thomas Villmann is a Professor of Computational Intelligence and Techno-Mathematics at Mittweida University of Applied Sciences in Germany. He serves as Deputy Spokesperson for the Mathematics Department, AI Coordinator at the university, Director of the Saxon Institute for Computational Intelligence and Machine Learning (SICIM), and President of the German Chapter of the European Neural Network Society (GNNS). His academic credentials include the German 'Dr. rer. nat. habil.' designation, indicating both doctoral and habilitation qualifications in natural sciences. Professor Villmann's research focuses on computational intelligence with particular emphasis on vector quantization, learning vector quantization, neural networks, and interpretable machine learning. His work spans theoretical developments in mathematical foundations of machine learning algorithms as well as practical applications in bioinformatics, remote sensing, medical diagnostics, and autonomous systems. He has developed mathematically sound methods for data-based analysis (clustering), decision support systems, data-based prediction models, and visualization of complex data. His recent publication record demonstrates a strong trend toward interpretable and explainable AI, with emphasis on vector quantization techniques applied across diverse domains including medical diagnostics (particularly breast cancer detection), fairness in machine learning, satellite remote sensing, and autonomous vehicle systems. Villmann's work consistently bridges theoretical mathematics with practical applications, maintaining a focus on making machine learning models more transparent, reliable, and ethically sound. As Director of SICIM and leader of the Computational Intelligence Research Group, Professor Villmann oversees an integrated research ecosystem focused on problem-oriented intelligent data analysis. His institutes aim to stimulate interest in computational intelligence among students and young researchers while supporting public institutions, authorities, and companies in data analysis both regionally and internationally. His leadership extends to organizing academic events and workshops, including serving as president of the German Chapter of the European Neural Network Society.

Moritz Herrmannمشاهده پروفایل

پژوهشگر

• Empirical Machine Learning
• Manifold Learning
• Representation Learning
• +۵ مورد دیگر

Dr. Moritz Herrmann is a postdoc researcher and Reproducibility & Open Science Transfer Coordinator at the Munich Center for Machine Learning (MCML). He is affiliated with the Biometry in Molecular Medicine working group led by Prof. Anne-Laure Boulesteix at Ludwig-Maximilians-Universität München, and contributes to initiatives like the LMU Open Science Center , Open Science Initiative in Statistics (OSIS) , and Open Science Initiative in Medicine (OSIM) . Ph.D. in Statistics from LMU (2022), M.Sc. in Statistics (2018), and B.Sc. in Mathematics/Sports Science (2014) His research focuses on Empirical Machine Learning , Manifold Learning , and Metascience , with emphasis on epistemological foundations and reliability in ML research. He advocates for open science practices and data literacy, as outlined in his ICML 2024 position paper on rethinking empirical ML research. As a member of the Empirical Machine Learning research focus group and the Statistical Learning and Data Science Chair , Herrmann bridges statistical methodology with biomedical applications. His work spans outlier detection, cluster analysis, and reproducibility frameworks, reflected in his recent publications in journals like Biometrical Journal and Data Mining and Knowledge Discovery .

Volker Birkeمشاهده پروفایل

استاد

• Environmental Chemistry
• Groundwater Remediation
• Hazardous Waste Management
• +۳ مورد دیگر

Prof. Volker Birke is a Full Professor of Chemistry/Technical Chemistry at the Wismar University of Applied Sciences, Germany, since 2016. He has over 30 years of experience in environmental chemistry and remediation technologies, with a focus on groundwater and soil contamination. He teaches courses in general, physical, technical, and environmental chemistry for process and environmental engineers. Professor, Wismar University of Applied Sciences (2016–present) Extraordinary Professor, Ostfalia University of Applied Sciences (2009–2016) Research Interests: Specializing in permeable reactive barriers (PRBs) for groundwater pollution, mechanochemical dehalogenation of persistent organic pollutants (POPs), and emerging contaminants like PFAS and pharmaceuticals. His work spans ex situ and in situ remediation technologies for chlorinated ethenes, PCBs, and chromite ore processing residues. International Collaborations: Long-term research partnerships with University of Newcastle and CRC CARE Institute in Australia (since 2004), including student exchanges and joint FuE projects. Also collaborates with Indian Institute of Technology Delhi on groundwater remediation projects. Grants & Projects: Coordinated Germany's large-scale R&D cluster RUBIN for PRB technologies (2006–2012). Currently supervises a DAAD-funded internship in Australia (2024) and a BMWi-funded FuE project (2023–2025) with German companies on wastewater treatment. Labs & Teams: Leads the Chemistry and Environmental Analytics Laboratory in Malchow, Germany, focusing on innovative remediation methods. Has managed over 50 national and international remediation projects in USA, India, and Australia.

Dominic Sauerbreyمشاهده پروفایل

پژوهشگر

• Refugee Studies
• Social Geography
• Rural Development
• +۴ مورد دیگر

Dominic Sauerbrey is a Researcher at the Institute for Migration Research and Intercultural Studies (IMIS) at the University of Osnabrück. He contributes to the DFG-funded Collaborative Research Center 1604 'Production of Migration' as a research associate in Subproject A5 (2024-present), analyzing refugee-related figures since the 1970s. Additionally, he serves as an associated doctoral candidate at the Leibniz Centre for Contemporary History Potsdam (ZZF) , Department V (2024-present), and advises the Refugee Law Clinic Osnabrück (2022-present), including board membership (2024-2025). M.A. in International Migration and Intercultural Relations, University of Osnabrück (2021-2024) B.A. in Cultural Geography, FAU Erlangen-Nuremberg (2016-2020) His research focuses on refugee integration , labor market dynamics , and rural migration , employing discourse analysis and historical methodologies . Recent publications examine refugee chronicles (2022-2025), rural self-employment patterns, and participatory workshop frameworks. Key themes include the evolution of refugee-related data production, rural development challenges, and policy discourse. Scientific awards include the Alumni of the Social Sciences Award for his Master's thesis on refugee labor market integration in Germany. He has collaborated with institutions like the Franconian Geographical Society and contributed to EU H2020 projects such as MATILDE .

• Social Inequality
• Geospatial Sociology
• Survey Research & Methodology
• +۴ مورد دیگر

Simon Kühne is Professor of Applied Social Data Science at the Faculty of Sociology, Bielefeld University since May 2021. He also serves as Scientific Coordinator of the Leibniz-ScienceCampus SOEP-RegioHub. His work bridges social science with data science methodologies, focusing on innovative approaches to studying social phenomena through digital and computational methods. His educational background includes: PhD in Sociology from Berlin Graduate School of Social Sciences, Humboldt University of Berlin (2014-2018) MA in Survey Methodology from University of Duisburg-Essen (2011-2014) BA in Sociology from University of Duisburg-Essen (2008-2011) Professor Kühne's research spans several interconnected areas that reflect the growing intersection of social science and data science. His primary interests include Social Inequality, Geospatial Sociology, Survey Research & Methodology, Social Media Research, and Big Data applications in social sciences. His work often examines how digital footprints and new data sources can illuminate social patterns and inequalities. He has particular expertise in geospatial analysis of social media data and innovative survey methodologies that leverage digital platforms for recruitment and data collection. His recent publications demonstrate a strong focus on methodological innovations in social research, particularly around social media data collection and analysis. There's a clear trajectory toward examining how digital spaces reflect and shape social inequalities, with particular attention to regional variations in discourse and behavior. His work bridges computational social science with traditional sociological concerns about inequality, discrimination, and social cohesion. Professor Kühne is actively involved in several research communities: German Society for Online Research (DGOF), Board Member since 2023 Center for Uncertainty Studies (CeUS), Bielefeld University (since 2022) Center for Statistics (ZeSt), Bielefeld University (since 2022) Institute for Interdisciplinary Conflict and Violence Research (IKG), Bielefeld University (since 2020) Research Institute for Social Cohesion (FGZ), Bielefeld Campus (since 2020) Bielefeld Center for Data Science (BiCDaS), Bielefeld University (since 2018) He leads several significant research projects examining regional development dynamics, the regionalization of discriminatory discourses on social media, and the relationship between political attitudes and career advancement in public service. His methodological expertise in survey design, social media data collection, and geospatial analysis informs these substantive research areas.

Christian Martinمشاهده پروفایل

پژوهشگر

• Machine Learning
• Statistical Learning
• Life Science & Medicine
• +۷ مورد دیگر

Christian Martin is a Researcher at the University of Leipzig since 2019, affiliated with the ScaDS.AI Center for Scalable Data Analytics and Artificial Intelligence in Dresden/Leipzig. His work focuses on integrating Machine Learning and Biomedical Data Analysis for applications in Life Science & Medicine , particularly in Personalized Medicine and Medical Imaging . Current affiliation: ScaDS.AI Center, University of Leipzig Previous roles: Software Developer & Research Associate at Loeser/Meierhofer Medizintechnik GmbH (2008-2019), Research Associate at Biodata Mining Group, University of Bielefeld (2003-2008) His research spans Deep Learning , Radiomics , and Image Processing for medical applications, with notable projects like: GRAMMY (Integrative analysis of gastric cancer, microenvironment, and patient outcomes) MIRACLE (Machine learning for lung cancer relapse prediction) SaxoCell Omics (Systems biology for radiomics classification) He has supervised multiple master's students including: Mohammad Issa (Automotive lane detection) Wael Assy (Soft tissue sarcoma classification) Julius Ellermann (MRI image prediction) Jan Philipp Zimmer (Cervix cancer classification) Marlene Mertens (Cervical cancer analysis) Contact: christian.martin@informatik.uni-leipzig.de

Sarah Jelinskiمشاهده پروفایل

پژوهشگر

• Geovisualisations
• Visual Regional Geographies
• Geographies of belonging and difference
• +۵ مورد دیگر

Sarah Jelinski is a Researcher at the Leibniz Institute for Regional Geography (IfL) since August 2025, specializing in Cartography and Visual Communication . Her work bridges cultural geography with digital humanities, focusing on geovisualizations and urban research. Education: MA in Cultural Geography (2022-2024) at Friedrich-Alexander University Erlangen-Nuremberg with focus on urban research and regional development BA in Cultural Geography (2018-2022) at Friedrich-Alexander University Erlangen-Nuremberg Research Interests: Sarah explores geographies of belonging , mobilities and migration , and multiple geographies of regional development , with a particular emphasis on visual regional geographies and digital data practices. Her recent projects include IsoGW – Groundwater Isoscapes for Germany and applications of the SpaceLog mobile app for studying urban green space experiences. Publications: Her scholarly output includes analyses of video game geographies (2024) and GIS-based studies of urban greenness (2023), reflecting her interdisciplinary approach to cultural and digital geography. Professional Affiliation: She contributes to the IfL Research Workshop and collaborates with the ScienceCampus EEGA network, working at the intersection of cartography, social studies, and digital humanities.