جست‌وجوی استاد و پژوهشگر

Melanie Schienleمشاهده پروفایل

استاد

• Econometrics
• Statistical Methods
• Financial Risk Analysis
• +۴ مورد دیگر

Prof. Melanie Schienle is a Professor and Chair of Statistical Methods and Econometrics at the Department of Economics and Management, Karlsruhe Institute of Technology (KIT). She also holds a professorship in the Department of Mathematics at KIT since 2021. Her expertise spans statistical methods, econometrics, financial risk analysis, and forecasting. She leads the HKMetrics Network and the RespiNow Hub for respiratory disease forecasting. She serves as a Senior Fellow at the Rimini Center for Economic Analysis (RCEA), a steering committee member of the German Economic Association, and a member of the University Research Council at KIT. Education: Ph.D. (Dr. rer. pol.) in Economics from Mannheim University (2008), summa cum laude; Diploma in Mathematics (University of Karlsruhe, 2003) with a minor in theoretical physics. She has held academic positions at Leibniz University Hannover (2012–2015) and Humboldt University of Berlin (2008–2012). Research interests focus on financial networks, systemic risk, time series analysis, and machine learning applications in economics. She co-leads projects on nowcasting and forecasting, including collaborative efforts during the pandemic to predict hospitalizations. Her work integrates advanced statistical techniques with real-world policy implications. Prof. Schienle is an Associate Editor for the International Journal of Forecasting and Journal of Time Series Analysis . She has authored over 50 peer-reviewed publications and contributed to high-impact journals like Nature Communications and Journal of Business & Economic Statistics . She leads the Institute of Statistics at KIT and chairs the MathSEE initiative for interdisciplinary mathematical applications.

• Spatial Statistics
• Spatiotemporal Modeling
• Environmetrics
• +۳ مورد دیگر

Philipp Otto is a Professor of Statistics and Data Science at the University of Glasgow. Previously, he was a Reader in Statistics and Data Analytics (2023–2024) and held a Junior Professorship in Big Geospatial Data at Leibniz University Hannover (2018–2023). He earned his PhD in Statistics (summa cum laude) from European University Viadrina in 2016 and a B.Sc. in International Economics, with study visits to Saint Petersburg State University. His research focuses on spatial and spatiotemporal statistics, environmetrics, network modeling, and machine learning applications. Education: PhD in Statistics (2016), European University Viadrina, Frankfurt (Oder) B.Sc. in International Economics (with study visits to Saint Petersburg) Research Interests: Philipp’s work centers on spatial statistics, spatiotemporal volatility modeling, environmental data analysis, and network processes. He develops statistical methods for geo-referenced and network data, with applications in climatology, finance, and environmental risk assessment. His contributions include advancements in GARCH models, spatiotemporal clustering detection, and statistical process monitoring for AI systems. Grants & Projects: He has secured €1,038,847 in research grants, leading projects on historical map time series analysis, agricultural air quality impacts, and high-dimensional spatial dependence structures. Industry collaborations include survival analysis for building information models. Awards: 2017 Fellowship to attend the Lindau Nobel Laureate Meeting (Economic Sciences) 2017 Best Presentation Award (Data Science, Statistics, and Visualisation) Teaching: He teaches statistics and data science across disciplines, including economics, engineering, and mathematics, at both undergraduate and postgraduate levels. Professional Activities: Editorial Boards: Environmetrics (2021), AStA Advances in Statistical Analysis (2020) Member of German Statistical Society (Treasurer, 2013)

Jörg Hackermüllerمشاهده پروفایل

استاد

• Bioinformatics
• Omics Data Analysis
• Toxicology
• +۳ مورد دیگر

Prof. Dr. Jörg Hackermüller is a computational biologist with expertise in Omics data integration Toxicology Environmental risk assessment Non-coding RNA biology . He serves as Head of the Department of Computational Biology and Chemistry at the Helmholtz Centre for Environmental Research (UFZ) since 2024 and holds a Professorship at the Faculty of Mathematics and Computer Science at Leipzig University since 2021. His research focuses on Developing AI methods for chemical toxicity prediction Multi-omics integration for mechanistic toxicology Data standardization in environmental monitoring Non-coding RNAs as biomarkers in disease and toxicity and has produced 15+ recent publications spanning tools like multiGSEA and deepFPlearn+ . He collaborates with teams across UFZ Leipzig University Novartis Fraunhofer Institute and leads projects like InCeTo and SafePol , integrating exposome research with systems biology.

Marco Cuturiمشاهده پروفایل

استاد

• Machine Learning
• Optimal Transport
• Optimization
• +۴ مورد دیگر

Marco Cuturi is a Research Scientist at Apple ML Research in Paris and Professor of Statistics at CREST-ENSAE, Institut Polytechnique de Paris. His work bridges machine learning , optimal transport , and optimization , with applications in time-series analysis , kernels , and multiresolution methods . He has held academic roles at Kyoto University and Princeton University, and previously worked in the financial industry. Research Interests: Optimal transport theory and computational methods Kernel design for structured data and histograms Time-series alignment and soft-DTW Entropic regularization in optimization Applications to computer vision and genomics Teaching: Cuturi has taught courses on linear optimization at Princeton, geometric methods in machine learning at Kyoto, and scientific English. He has also organized machine learning summer schools in Kyoto, Les Houches, and other international venues. Recent Trends: His 2024-2025 publications focus on entropic optimal transport solvers, disentangled representation learning via Gromov-Monge gaps, and applications to text-to-image diffusion models. Collaborative work with institutions like Google Research, MIT, and University of Tokyo highlights his interdisciplinary impact.

• Machine Learning
• Natural Language Processing
• Medical Informatics
• +۳ مورد دیگر

Stefan Riezler is a full professor of Statistical Natural Language Processing at Heidelberg University's Department of Computational Linguistics (since 2010), affiliated with the Faculty of Mathematics and Computer Science. Prior to this, he worked in Silicon Valley at Xerox PARC and Google Research. He holds a PhD in Computational Linguistics from the University of Tübingen (1998) and conducted postdoctoral research at Brown University (1999). His research spans machine learning, NLP, and medical informatics, focusing on interactive statistical learning. He co-leads the Interdisciplinary Center for Scientific Computing (IWR) and serves on the editorial boards of Computational Linguistics and Transactions of the Association for Computational Linguistics . Key research areas include neural machine translation, healthcare AI (e.g., sepsis prediction), data augmentation, and reproducibility in ML. He develops tools like JoeyNMT and explores ethical challenges in clinical machine learning. Notable recent work includes advancements in time series analysis, multimodal interfaces (e.g., NLMaps for OpenStreetMap), and ethical frameworks addressing validity in healthcare ML. His publications emphasize practical applications of NLP in healthcare, speech translation, and cross-lingual systems. Grants and collaborations include interdisciplinary projects on medical data science and training next-gen NLP researchers. He actively contributes to open-source toolkits and reproducible research practices.

• Geometallurgy
• Compositional Data Analysis
• Geostatistics
• +۳ مورد دیگر

Dr. Raimon Tolosana Delgado is a Research Fellow at the Helmholtz-Zentrum Dresden-Rossendorf (HZDR), affiliated with the Helmholtz Institute Freiberg for Resource Technology. He leads research in predictive geometallurgy and statistical analysis of mineral resources, focusing on translating geological data into processing insights. His research integrates geostatistics , compositional data analysis (CoDa) , and machine learning to model ore behavior and resource potential. Key areas include: Predictive geometallurgy for forecasting ore/waste behavior Bayesian statistics for parameter estimation and uncertainty analysis Development of R-based tools (e.g., compositions and gmGeostats packages) for mineral data analysis Particle-based process modelling for mineral separation optimization Recent publications emphasize machine learning integration (e.g., neural networks for geophysical tensor fields), tailings reprocessing (3D geostatistical assessment of resource potential), and advanced statistical methods for compositional data. A consistent trend involves enhancing predictive accuracy in mineral processing through multi-source data fusion. Dr. Tolosana Delgado coordinates the development of technology platforms for geometallurgical data analysis, including databases and interfaces for industrial applications. His work bridges ore geology, mineral processing, and metallurgy to optimize resource efficiency.

Yuyu Zhouمشاهده پروفایل

استاد

• Energy
• Renewable Energy
• Energy Modeling
• +۷ مورد دیگر

Yuyu Zhou is a Professor in the Department of Geography at The University of Hong Kong. With an extensive publication record of 301 papers and over 18,000 citations, Dr. Zhou is a leading researcher in urban environmental studies, climate change, and sustainability science. Dr. Zhou received their PhD in Environmental Science from the University of Rhode Island (2004-2008) and previously worked as a Research Scientist at Pacific Northwest National Laboratory's Joint Global Change Research Institute (2010-2015). They currently serve as Chief Editor for Earth System Science Data (Copernicus Publications), Associate Editor for Ecological Processes, and Section Editor for All Earth. Dr. Zhou's research focuses on the intersection of urbanization, climate change, and environmental sustainability. Their work spans several key areas including urban heat island effects, vegetation phenology in urban environments, energy modeling, and sustainable urban development. Through innovative remote sensing approaches and spatial analysis, Dr. Zhou investigates how urban environments respond to and influence global environmental change. Analysis of Dr. Zhou's recent publications reveals a strong emphasis on urban environmental challenges, with particular attention to urban heat islands, vegetation dynamics, and climate change impacts in cities. Their work combines remote sensing data with ground observations to develop high-resolution models of urban environmental processes. Recent research has focused on urban greening effects, building energy use under climate change, and environmental justice issues related to urban heat exposure. Dr. Zhou has received significant recognition for their work, as evidenced by the high citation count of their publications. Their research has important implications for urban planning, climate adaptation strategies, and sustainable development policies worldwide. As an educator and mentor, Dr. Zhou advises numerous graduate students and collaborates with researchers globally. Their work with international teams has resulted in significant contributions to understanding urban environmental systems across different geographical contexts.

Zhen Liuمشاهده پروفایل

استادیار

• Generative Models
• 3D Representation Learning
• Computer Vision
• +۲ مورد دیگر

Zhen Liu is an Assistant Professor at the School of Data Science, CUHK-Shenzhen. His research focuses on generative models, 3D representations, and the synergy of spatial and semantic understanding in AI systems. With a PhD from Mila and Université de Montréal, he develops foundational methods for physics simulation, 3D assembly, and semantic reasoning in neural networks. His work bridges machine learning with applications in computer vision and graphics, emphasizing: Generative architectures for 3D content creation Diffusion model alignment techniques Efficient parameter finetuning strategies Dr. Liu mentors students in AI research and contributes to advancing 3D generative modeling paradigms.

• Cartography
• Geospatial Analysis
• Urban Planning
• +۴ مورد دیگر

Prof. Liqiu Meng serves as Chair of Cartography and Visual Analytics at the Technical University of Munich (TUM). He specializes in advanced geospatial research, digital cartography, and human-technology collaboration frameworks. Current Faculty at TUM Chair of Cartography and Visual Analytics Research Focus: His work bridges cartographic theory with cutting-edge technology, covering topics like 3D urban modeling, AI ethics visualization, geovisual analytics, and spatiotemporal data interpretation. Urban Morphology Analysis AI Ethics Cartography Geovisual Analytics 3D City Data Integration Location-Based Service Design Publications: Recent works (2025-2024) demonstrate expertise in explainable AI for urban analysis, multi-agent systems for geospatial interaction, and advanced spatial modeling techniques. Contact: liqiu.meng@tum.de | contact.lfk@ed.tum.de

Gaël Kermarrecمشاهده پروفایل

پژوهشگر

• Boundary Layer Meteorology
• Atmospheric Turbulence
• GNSS Applications
• +۳ مورد دیگر

Dr. Gaël Kermarrec is a researcher at the Boundary Layer Meteorology Group , part of the Institute of Meteorology and Climatology within the Faculty of Mathematics and Physics at Leibniz University Hannover . His work focuses on atmospheric turbulence, GNSS applications, and remote sensing for environmental monitoring. Boundary layer meteorology Turbulence theory GNSS signal processing Terrestrial laser scanning Climate change impacts Geodetic time series analysis His research integrates advanced mathematical models like LR B-splines and Matérn covariance with large eddy simulations to study: Atmospheric turbulence effects on optical/GNSS signals Hydrospheric mass loading Deformation analysis of terrain/port infrastructure Climatic sea-level changes Machine learning for remote sensing The 15 most recent articles (2025-2023) demonstrate his focus on: GNSS-based turbulence detection AI-enhanced climate mapping Advanced surface approximation techniques Multi-sensor data fusion Stochastic modeling of geodetic observations Environmental impacts on optical measurements He has developed tools like the Klimascanner QGIS plugin for urban climate resilience and contributes to: Understanding atmospheric scale lengths Improving TLS/GNSS deformation monitoring Analyzing hydrospheric changes Wavefront modeling Ionospheric corrections

• Computational Linguistics
• Historical Linguistics
• Phylogenetic Linguistics
• +۸ مورد دیگر

Prof. Gerhard Jäger holds the Chair of General Linguistics at the Faculty of Humanities, University of Tübingen . He serves as a Principal Investigator (PI) in the Clusters of Excellence Human Origins and Machine Learning for Science , and leads projects like Phylomilia (funded by Volkswagen Foundation) and CrossLingference (ERC Advanced Grant). His career spans multiple institutions, including Bielefeld University (2004-2009) and Stanford University (visiting scholar, 2004). Habilitation (2002) at Humboldt University Berlin with thesis on Anaphora and Type Logical Grammar PhD (1996) at Humboldt University Berlin on Dynamic Semantics His research bridges computational linguistics , phylogenetic analysis , and game theory , focusing on Bayesian models , language evolution , and cross-linguistic typology . Recent work explores phylogenetic inference from acoustic speech data and geographic influences on language trees . Key contributions include 15+ recent publications on topics spanning phylogenetic typology , cognate detection , and Bayesian language modeling . These works employ machine learning , statistical inference , and evolutionary game theory to analyze language change , typological variation , and linguistic stability . Honors include ERC Advanced Grant , Volkswagen Foundation funding , and DFG-Humanities Centre for Advanced Studies participation. He has taught courses in Computational Historical Linguistics , Phylogenetic Methods , and Bayesian Data Analysis across institutions like Tübingen, Bielefeld, and Stanford. He actively contributes to academic communities through workshop organization (e.g., Quantitative Theoretical Linguistics , Game Theory in Pragmatics ) and serves on the faculty council at Tübingen. His team collaborates with institutions like Max Planck Institute for Evolutionary Anthropology , University of Pennsylvania , and LMU Munich .

Krisztina Kis-Katosمشاهده پروفایل

استاد

• Applied Development Economics
• Political Economy
• Effects of (De-)Globalization
• +۱۱ مورد دیگر

Krisztina Kis-Katos serves as Professor for International Economic Policy at the University of Göttingen's Department of Economics, a position she assumed in 2016. She holds prominent leadership roles including Chairwoman of the Standing Field Committee of Development Economics of the German Economic Association (Verein für Socialpolitik) and Chairwoman of the Scientific Advisory Board of the RWI Leibniz Institute for Economic Research. Her institutional affiliations extend to research fellowships at IZA and RWI, along with editorial positions at the Journal of Labour Market Research, European Journal of Political Economy, Bulletin of Indonesian Economic Studies, and Journal of Development Studies. Professor Kis-Katos earned her Economics education in Szeged and Konstanz, attended the Swiss Doctoral Program at the Study Center Gerzensee, and received her doctoral degree from the University of Freiburg in 2010. Her scholarly work spans applied development economics and political economy with particular focus on how (de-)globalization and macroeconomic processes affect social and economic outcomes including labor markets, firm performance, land use change, deforestation, and conflict. Her research portfolio reveals consistent thematic threads across recent publications: the intersection of environmental concerns with economic development (particularly deforestation and palm oil in Indonesia), the gendered impacts of trade liberalization, the socioeconomic effects of pandemics like COVID-19, and the complex relationship between governance, corruption, and economic outcomes. Methodologically, her work combines rigorous econometric approaches with innovative data sources including satellite imagery and high-frequency power usage data. Teaching prize for the best doctoral course in RTG 1723, University of Göttingen (2019) Teaching prize of the Student Union of Economics of the University of Freiburg (2014) BMZ/GIZ Public Policy Award (2013) Friedrich-August-von-Hayek-award (2011) Excellence award of the KfW Development Bank (2011) Professor Kis-Katos leads multiple significant research initiatives including the BMZ-DEval funded evaluation of Madagascar's forest restoration program, the DFG-funded Thailand-Vietnam Socioeconomic Panel, and the DFG Research Training Group on Sustainable Food Systems. Her advisory role extends to supervising doctoral candidates through these projects and previously serving as spokesperson for the Research Training Group 1723 on Globalization and Development. Her substantial grant portfolio demonstrates strong research leadership across international collaborations involving institutions in Germany, Indonesia, Thailand, Vietnam, and the United States. Her work connects closely with the Collaborative Research Centre 990 on Ecological and Socioeconomic Functions of Tropical Lowland Rainforest Transformation Systems in Sumatra, Indonesia, reflecting her deep engagement with environmental-economic research questions in Southeast Asia. She also contributes to interdisciplinary teams through projects like PlanetHealth examining global land-use impacts of the COVID-19 pandemic.

Oliver Staadtمشاهده پروفایل

استاد

• Virtual Reality
• Augmented Reality
• Computer Graphics
• +۷ مورد دیگر

Prof. Dr. sc. techn. ETH Oliver Staadt is Full Professor of Computer Science and Chair of Visual Computing at the University of Rostock , Germany. Since 2023 he also serves as Director of the Institute for Visual and Analytic Computing within the Faculty of Computer Science and Electrical Engineering . Previously he was Dean (2016–2018) and Vice Dean (2010–2016) of the same faculty. Education Ph.D. in Computer Science, ETH Zürich (2001) M.Sc. in Computer Science, TU Darmstadt (1994) Research Interests Prof. Staadt’s research spans virtual and augmented reality , computer graphics , visualization , telepresence , immersive analytics , and human–computer interaction . A particular focus lies on real-time rendering and display technologies for large high-resolution display systems, depth-image enhancement for RGB-D sensors, and interaction techniques that leverage spatial cognition and eye-tracking. His work is frequently applied to collaborative settings and microgravity environments, including experiments aboard parabolic flights and the International Space Station. Recent Publication Trends Between 2019 and 2021 his output centers on foveated rendering , AR viewpoint guidance , collaborative analytics on wall-sized displays , and embodied interaction metaphors . Earlier work addressed bandwidth-efficient telepresence, depth-image filtering, and physically-based animation. The corpus reveals a steady evolution from fundamental graphics algorithms toward applied immersive systems. Scientific Awards & Honors Fellow of the Eurographics Association Associate Editor, IEEE Transactions on Visualization and Computer Graphics (past) Associate Editor, Computers & Graphics (past) Associate Editor, Computer Animation and Virtual Worlds (past) Associate Editor, Frontiers in Virtual Reality (current) Chair, Expert Group on Virtual & Augmented Reality, German Informatics Society (2013–2020) Advising & Funding He has successfully supervised more than ten PhD graduates whose dissertations range from collision detection and physically-based animation to 3D interaction in microgravity and predictive user modeling. Current PhD researchers include Bipul Mohanto, Mana Takhsha, and Sven Kluge. His projects are supported by national and EU programs such as EVOCATION, SMOOTH, ARGuide, 3DPick, DIVA, and Telepresence. Labs & Teams Prof. Staadt leads the Visual Computing Group at Rostock, operating state-of-the-art facilities including large tiled display walls, VR/AR laboratories, and motion-capture systems. The institute hosts interdisciplinary collaborations with partners in visualization, computer vision, psychology, and aerospace engineering.

• Mathematical Statistics
• Applied Statistics
• Probability Theory
• +۶ مورد دیگر

Holger Dette is a Professor and Chair Holder of Stochastics (specializing in Statistics) at the Faculty of Mathematics, Ruhr University Bochum. He leads the prominent Group Dette within the Institute of Statistics, overseeing a team of researchers, doctoral students, and administrative staff including Birgit Tormöhlen as team assistant. His research group is deeply integrated within the university's mathematical ecosystem, collaborating with other research groups across algebra, analysis, numerics, and topology. Dette's research spans mathematical statistics with strong applications in real-world problems. His primary interests include optimal experimental design, time series analysis, functional data, change point problems, nonparametric regression, biostatistics, special functions, goodness-of-fit tests, and random matrices . His work bridges theoretical statistics with practical applications, particularly evident in his collaborations with pharmaceutical giants Novartis and Bayer AG in biostatistics, as well as Quasol, a spin-off company from his statistics institute. His recent publications (2024-2025) reveal a research program increasingly focused on high-dimensional and functional data analysis, privacy-preserving statistics, and novel methodological approaches to longstanding statistical problems. Dette's work shows strong interdisciplinary connections, particularly with biomechanics (analyzing joint angles during fatigue phases) and data science (addressing challenges in the era of big data). His research group is actively involved in multiple DFG-funded projects including the newly established 'Small Data' collaborative research center (Sonderforschungsbereich 1597) and the Spatio-temporal Statistics for the Transition of Energy and Transport (Transregio 391). Dette has received significant recognition including the prestigious Humboldt Research Award . His paper 'With Great Power Come Great Side Channels: Statistical Timing Side-Channel Analyses with Bounded Type-1 Errors' achieved second place at the CSAW'24 Applied Research Competition MENA. His research group has also secured multiple significant funding awards from the German Research Foundation (DFG). As an advisor, Dette supervises numerous doctoral and master's students including Pascal Quanz, Marius Kroll, and Carina Graw. His group offers statistical consulting services for scientists and students across bachelor's, master's, and doctoral phases. The group maintains strong industrial partnerships, particularly in biostatistics applications, demonstrating Dette's commitment to translating theoretical statistics into practical solutions for real-world challenges.

• Ecological Epidemiology
• Wildlife Disease Dynamics
• Veterinary Epidemiology
• +۴ مورد دیگر

Dr. Martin Lange is part of the Project Group Ecological Epidemiology within the Department of Ecological Modelling at the Helmholtz Centre for Environmental Research (UFZ). His research focuses on computational epidemiology, particularly in wildlife and livestock systems. Key areas include disease transmission dynamics, surveillance strategies, and the development of predictive models for infectious diseases like African Swine Fever (ASF) and Bovine Viral Diarrhea Virus (BVDV). Affiliations: UFZ, Department of Ecological Modelling; EcoEpi research group. Education: PhD in Veterinary Epidemiology (2013, TU Bergakademie Freiberg). Research interests span ecological epidemiology, wildlife disease control, and the application of agent-based and individual-based models to understand pathogen spread. Notable contributions include modeling ASF in wild boar populations and BVDV eradication strategies in Ireland. Awards: Received the 2018 UFZ-Wissenstransferpreis (shared with H.H. Thulke) and the Konrad-Bögel-Nachwuchsförderpreis for his doctoral work on eco-epidemiology of wild boar diseases. Also recognized at the DACh Epidemiologietagung 2011. Publications: Over 50 peer-reviewed articles since 2006, emphasizing interdisciplinary approaches to disease modeling and environmental systems. Recent work includes digital twin frameworks for biodiversity monitoring and Python-based model coupling tools like FINAM. Labs/Teams: Active in the EcoEpi group and contributes to projects like BioDT (Biodiversity Digital Twin) and CauSES (Causation in Social-Ecological Systems).