جستجوی استاد راهنمای دانشگاهی

• Machine Learning
• Data Mining
• High-Performance Computing
• +۵ مورد دیگر

Prof. Dr. Fabian Gieseke is a Professor and Chair of Machine Learning and Data Engineering at the University of Münster. He holds a PhD in Computer Science from Carl von Ossietzky University of Oldenburg and a dual degree in Mathematics and Computer Science from the University of Münster. His research focuses on Machine Learning, High-Performance Computing, and their applications in Geosciences, Smart Cities, and Astrophysics. Education: PhD in Computer Science (2012), Carl von Ossietzky University of Oldenburg University studies in Mathematics and Computer Science (2006–2011), University of Münster Research Interests: Data Mining and Machine Learning High-Performance Computing & Distributed Systems Deep Learning Applications in Environmental Science and Astrophysics Geospatial Data Analysis using Satellite Imagery Publications Trends: His recent work emphasizes large-scale environmental monitoring via deep learning, including canopy height estimation, forest biomass prediction, and national-scale tree counting. He also explores interactive systems for geospatial data retrieval and optimization of machine learning models for resource-constrained environments. Advising & Grants: Supervised over 30 theses on topics like satellite image analysis, deep learning on microcontrollers, and data marketplaces for smart grids. Active in securing grants for interdisciplinary projects combining AI with Earth observation. Labs/Teams: Leads the Machine Learning and Data Engineering group at the University of Münster, focusing on scalable AI solutions for real-world challenges in science and industry.

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.

Joaquim Pintoمشاهده پروفایل

استاد

• Mid-latitude meteorology
• Climatology
• Extreme weather events
• +۷ مورد دیگر

Professor Joaquim Pinto is a leading climate scientist at the Karlsruhe Institute of Technology (KIT), where he serves as Head of the Working Group "Regional Climate and Weather Hazards" and as Spokesperson of the collegial institute management team. He holds the prestigious AXA Research Fund Chair position at the Institute of Meteorology and Climate Research - Troposphere Research (IMK-TRO). His academic background includes a Licenciate in Geophysical Sciences - Meteorology from the University of Lisbon (1990-1996), PhD studies at the University of Cologne (1998-2002), and academic positions at the University of Cologne (2002-2016) and University of Reading (2013-2016) before joining KIT in 2016. He became a Privatdozent (lecturer) at the University of Cologne in 2011 and earned his habilitation with research on extreme European wind storms. Professor Pinto's research focuses on mid-latitude meteorology and climatology, with special emphasis on extreme weather events , climate variability in Europe across multiple time scales, regional climate modeling and downscaling methods , and the diagnostic modeling and quantification of risks associated with extreme events affecting Europe. His work bridges fundamental climate science with practical applications for risk assessment and management. His extensive publication record demonstrates expertise in analyzing European windstorms, heatwaves, and compound extreme events. Recent work examines the impacts of climate change on wind energy potential, extreme precipitation events, and the complex interactions between atmospheric circulation patterns and regional climate extremes. His research often employs high-resolution climate modeling, statistical-dynamical downscaling approaches, and interdisciplinary collaborations to address pressing climate challenges. AXA Research Fund Chair in Regional Climate and Weather Hazards Professor Pinto teaches graduate and undergraduate courses including "Climate Modelling and Dynamics with ICON," "IPCC Assessment Report," "Climatology," "Energy Meteorology," "Methods of Data Analysis," and "Regional Climate and Weather Hazards." His teaching reflects his research expertise in climate modeling, extreme events, and regional climate change impacts.

• Variable Selection and Regularization
• Generalized Linear Models
• Event Data Analysis
• +۳ مورد دیگر

Andreas Groll is a Professor at the Technical University of Dortmund, affiliated with the Department of Statistical Methods for Big Data under the Faculty of Statistics. His research focuses on variable selection, regularization techniques in generalized linear models, categorical data analysis, and sports statistics, particularly predicting international soccer and tennis tournaments. He leads a working group including researchers like Dr. Daniel Horn and Dr. Rouven Michels. Key research areas include semiparametric regression and event data analysis. Recent work explores machine learning applications in sports analytics and healthcare, such as predicting hospital readmissions and modeling environmental data. Groll has published extensively in journals like Journal of Quantitative Analysis in Sports and Statistical Modelling .

Wolfgang Petrichمشاهده پروفایل

استاد

• Biomedical Spectroscopy
• Medical Diagnostics
• Infrared Spectroscopy
• +۳ مورد دیگر

Prof. Wolfgang Petrich is a Professor at the Kirchhoff-Institute for Physics at Heidelberg University. His research focuses on biomedical spectroscopy, particularly mid-infrared and Raman techniques applied to medical diagnostics, glucose sensing, and disease detection. He leads a lab developing optical sensors and analytical methods for clinical applications. His work includes advancements in continuous glucose monitoring systems, serum-based disease diagnostics (e.g., BSE, diabetes, rheumatoid arthritis), and machine learning approaches for spectral data analysis. Petrich collaborates internationally on projects like serum-based infectious disease detection and clinical triage systems for acute conditions. Key contributions include pioneering mid-infrared laser spectroscopy for in vitro diagnostics and establishing methodologies for pattern recognition in medical spectra. His research bridges physics, chemistry, and medicine, with applications in both human and veterinary health. Notable collaborations involve institutions like the University of Heidelberg's Biophotonics group and global medical research networks. His labs focus on translating spectroscopic innovations into practical diagnostic tools.

Martin Schmidtمشاهده پروفایل

استاد

• Mathematical Optimization
• Energy Systems
• Mixed-Integer Nonlinear Optimization
• +۳ مورد دیگر

Martin Schmidt is a Full Professor (W3) for Nonlinear Optimization at the Department of Mathematics, Trier University, since 2019. He has held leadership roles in research training groups and international committees, focusing on mathematical modeling and optimization of energy systems, gas networks, and market equilibria. His work bridges mixed-integer nonlinear optimization , bilevel optimization , and robust methods with applications to real-world energy challenges. Education: PhD in Mathematics (2013), Diplom in Mathematics (2008), both from Leibniz University Hannover. Editorial Roles: Editorial Board member of Journal of Optimization Theory and Applications and Optimization Letters , Associate Editor for OR Spectrum and EURO Journal on Computational Optimization . His research integrates complex physical systems (e.g., gas transmission networks) with game-theoretic models to analyze energy markets. Recent publications emphasize robust optimization , decomposition techniques , and machine learning integration in bilevel frameworks. Awards highlight his contributions to gas market feasibility , linear bilevel optimization , and practical applications in energy systems. Collaborations span institutions like Universidad Zaragoza, Sapienza University, and Forschungszentrum Jülich.

Hanna Meyerمشاهده پروفایل

استاد

• Remote Sensing
• Spatial Modeling
• Machine Learning for Spatial Data
• +۷ مورد دیگر

Prof. Dr. Hanna Meyer is a Professor of Remote Sensing and Spatial Modeling at the Institute of Landscape Ecology, University of Münster (WWU). She leads the Remote Sensing and Spatial Modeling Group and is actively involved in teaching and research in geospatial data science, machine learning, and environmental monitoring. Her work is supported by multiple national and international funding bodies including the DFG, EU Horizon Europe, and internal university grants. B.Sc. Geography, Philipps University Marburg (2007–2010) M.Sc. Environmental Geography, Philipps University Marburg (2010–2013) Ph.D., Philipps University Marburg (2014–2018) Her research focuses on machine learning methods for spatial data, optical remote sensing, environmental monitoring, and spatio-temporal modeling. She develops and applies advanced statistical and machine learning techniques to satellite and drone-based data for mapping ecological variables, land cover, and environmental change. Her work emphasizes methodological rigor, model transferability, and uncertainty quantification in spatial predictions. The recent publications reflect a strong trend in developing and validating machine learning models for environmental mapping, with applications in soil science, peatland hydrology, forest ecology, and polar climatology. She contributes both to theoretical advancements in spatial model validation and to practical software tools in R for geospatial analysis. She has secured competitive research funding for projects such as PRISM, Carbon4D, Uebersat, and BEyond, focusing on spatial pattern recognition, carbon modeling, AI model transferability, and biodiversity prediction. She teaches courses on remote sensing, spatial data analysis with R, and environmental modeling, and supervises students and early-career researchers. She collaborates widely with researchers across institutions and leads a dynamic research group including postdoctoral researchers and students. Her open-source contributions, particularly R packages like CAST and uavRst, support reproducible research in geospatial machine learning.

• Electric Vehicles
• Fuzzy Logic
• Blockchain
• +۵ مورد دیگر

Shahid Hussain is an Associate Professor at King Abdullah University of Science and Technology (KAUST) in the Computer, Electrical and Mathematical Sciences and Engineering Division. His research spans multiple domains including electric vehicle infrastructure, blockchain technology, and intelligent systems for IoT applications, with a focus on practical implementations of computational intelligence techniques. Dr. Hussain's research interests include: Electric Vehicles and Smart Grid Integration Fuzzy Logic and Intelligent Decision Systems Blockchain Applications for Security and Privacy Machine Learning for IoT and Healthcare Applications Energy Management Systems Smart City Infrastructure Development His recent publications demonstrate a strong focus on applying hybrid computational approaches to solve complex engineering problems, particularly in transportation systems and healthcare applications. Dr. Hussain has published extensively in IEEE journals, with particular emphasis on innovative approaches to electric vehicle charging infrastructure, secure IoT systems, and blockchain-enabled solutions for real-world challenges. Dr. Hussain maintains active collaborations with researchers globally, particularly with Reyazur Rashid Irshad, Young-Chon Kim, and Subhasis Thakur. His work bridges theoretical advancements with practical implementations, as evidenced by his research on fuzzy integer linear programming for EV charging stations and blockchain-enabled security frameworks for medical IoT systems.

Richard A. Davisمشاهده پروفایل

استاد

• Applied Probability
• Time Series Analysis
• Stochastic Processes
• +۴ مورد دیگر

Richard A. Davis is a Professor of Statistics at Columbia University and a Hans Fischer Senior Fellow at the Technical University of Munich's Institute for Advanced Study (TUM-IAS). He holds the Howard Levene Professorship in Statistics. His research focuses on applied probability, time series analysis, stochastic processes, and extreme value theory. Davis has contributed to nonlinear time series models, spatial modeling, and financial econometrics. He has held academic positions at Colorado State University and MIT, and has been recognized with awards including the Koopmans Prize, IBM Faculty Award, and fellowships from the Institute of Mathematical Statistics and American Statistical Association. His education includes a B.A. and Ph.D. in Mathematics from the University of California, San Diego. He co-authored influential textbooks like Time Series: Theory and Methods and has advised numerous papers on topics ranging from count time series to spatial extremes. Current research explores machine learning applications in time series and environmental risk modeling through collaborations like the STARMAP program.

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

Ivan Serina is a researcher at Università degli Studi di Brescia, Italy , with a focus on Artificial Intelligence , Machine Learning , and Natural Language Processing . His work spans Biomedical Informatics , Automated Planning , and Data Science , particularly in healthcare and multi-agent systems applications. Recent research includes AI planning with Python , language model bias detection , and COVID-19 prognosis estimation . He has extensively collaborated with Alfonso Emilio Gerevini and Luca Putelli, integrating GPT models with classical planning frameworks and analyzing BERT's attention mechanisms for medical report classification. His publications emphasize privacy-preserving planning , deep learning for land cover segmentation , and multi-task learning in biomedical NER. Ivan's work combines theoretical planning algorithms with practical implementations in healthcare , agriculture , and Italian language processing .

Ingolf Kühnمشاهده پروفایل

استاد

• Macroecology
• Plant Invasion Ecology
• Functional Traits
• +۱۲ مورد دیگر

Ingolf Kühn is a Professor of Macroecology at Martin-Luther University Halle-Wittenberg and Head of the Department of Community Ecology at the Helmholtz Centre for Environmental Research (UFZ) in Halle, Germany. He is also a member of the German Centre for Integrative Biodiversity Research (iDiv) and holds a fellowship at the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL). His research is centered on plant invasions, functional traits, and biodiversity responses to global change, with a strong methodological focus on spatial and phylogenetic modeling. His research interests span macroecology , plant invasion dynamics , urban and alpine flora , and data-driven ecological modeling . He has led major projects such as Biodiversity Meets Data (BMD) , eLTER PLUS , and AlienScenarios , and contributed to EU frameworks like EuropaBON and DAISIE . He is deeply involved in developing and managing large databases, including BiolFlor and the TRY Plant Trait Database . His recent publications focus on functional traits of invasive plants, biodiversity digital twins, and climate-driven shifts in species distributions. They reflect a strong trend toward integrating big data, machine learning, and macroecological theory to predict ecological change. Scientific Awards: Highly Cited Researcher (2014–2022) Fellow of the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) He serves as Editor-in-Chief of NeoBiota and Associate Editor of Journal of Vegetation Science . His advising spans PhD students and postdocs in the GLIMPSE cohort and iDiv projects. He leads multiple long-term monitoring initiatives, including alpine glacier forefield studies in the Dachstein and Berchtesgaden regions. He is affiliated with key research teams and platforms such as: Macroecology & Vegetation Science Working Group iDiv Science Strategy Board Task Forces: sTWIST, sUMMITDiv, sCoMuCra, sREGPOOL eLTER Research Infrastructure Biodiversity Digital Twin initiative

Fang Hanمشاهده پروفایل

دانشیار

• Rank-based methods
• Graph-based methods
• Statistical optimal transport
• +۱۰ مورد دیگر

Fang Han is an Associate Professor in the Department of Statistics at the University of Washington and an Adjunct Professor in Economics. He is also an Affiliated Investigator at Fred Hutchinson Cancer Research Center. His research focuses on rank-based and graph-based methods , statistical optimal transport , and nonparametric/semiparametric regression . Education: Ph.D. in Biostatistics, Johns Hopkins University (2015) - supported by Google Ph.D. Fellowship M.S. in Biostatistics, University of Minnesota B.S. in Mathematics, Peking University Research interests span rank correlation , causal inference , nonparametric regression , high-dimensional statistics , and random matrix theory . His recent work includes adaptations of Chatterjee's correlation to manifold data and rigorous analysis of matching estimators' variance. Scientific awards include Bernoulli Society New Researcher Award (2021) Google Ph.D. Fellowship (2013-2015) Margaret Merrell Award (2015) National Science Review 2015 Best Paper (2016) Teaching includes courses like Advanced Theory of Statistical Inference , Time Series Analysis , and Foundations of Machine Learning . He serves as Associate Editor for Bernoulli and editorial board member for Dependence Modeling .

Cristina G. Fernandesمشاهده پروفایل

دانشیار

• Algorithms
• Combinatorial Optimization
• Graph Theory
• +۷ مورد دیگر

Cristina G. Fernandes is an Associate Professor in the Department of Computer Science at the Institute of Mathematics and Statistics, University of São Paulo (IME-USP), where she conducts research in theoretical computer science, combinatorial optimization, and graph theory. She teaches advanced courses such as Advanced Data Structures and Topics in Algorithm Analysis. Her research interests include algorithms, combinatorial optimization, graph theory, approximation algorithms, data structures (persistent, retroactive, kinetic, succinct), and computational geometry. She applies theoretical methods to solve complex problems in network design, clustering, and discrete structures. The recent publications reflect a strong focus on structural and extremal graph theory, approximation algorithms, and combinatorial optimization. Key themes include tree and path packing, dominating sets, Steiner-type problems, and combinatorial properties of graphs. Her work often involves deep structural analysis and algorithmic design for NP-hard problems. Cristina G. Fernandes has not been mentioned with any specific scientific awards in the provided texts. She has supervised numerous postdoctoral researchers, PhD, MSc, and undergraduate students, many funded by FAPESP, CAPES, and CNPq. She leads significant research projects, including CAPES/MATH/STIC/CLIMAT-AMSU on energy efficiency in distributed computing and CNPq Universal projects on partitioning and connectivity. She is actively involved in academic advising and grant-funded research. She leads or participates in the Research Group in Theoretical Computer Science, Combinatorics and Combinatorial Optimization at IME-USP, fostering collaborative research in algorithms and discrete mathematics.

Stephan Seifertمشاهده پروفایل

استاد

• Chemometrics
• Bioinformatics
• Food Profiling
• +۳ مورد دیگر

Professor Stephan Seifert leads research at the University of Hamburg's Department of Chemistry, focusing on chemometrics and bioinformatic methods for food profiling and manuscript analysis. His group develops analytical approaches using vibrational spectroscopy, machine learning, and data fusion techniques. Research spans: Chemometric method development (random forests, variable selection) Spectroscopic analysis (FTIR, Raman, NMR) Food authentication and quality control Manuscript material characterization Recent publications demonstrate increasing use of machine learning for metabolomics data interpretation and multi-technique data fusion. Research integrates analytical chemistry with computational approaches to solve problems in food science and cultural heritage. Current projects include the Cluster of Excellence 'Understanding Written Artefacts' and Palm-Leaf Manuscript Profiling Initiative. Leads the Seifert AG research group at the Institute of Food Chemistry.

Melanie Schmidtمشاهده پروفایل

استاد

• Geometric Data Analysis
• k-means clustering
• Combinatorial Optimization
• +۳ مورد دیگر

Professor Melanie Schmidt is a faculty member in the Department of Computer Science at Heinrich-Heine-Universität Düsseldorf, where she leads the Algorithms and Data Structures research group. Previously, she was affiliated with the University of Bonn's Institute of Computer Science, where she completed her PhD under Prof. Dr. Heiko Röglin and headed a subgroup on "clustering for big data" within his research group. Current Position: Professor at Heinrich-Heine-Universität Düsseldorf Previous Position: Researcher and lecturer at University of Bonn PhD Advisor: Prof. Dr. Heiko Röglin Her research focuses on geometric data analysis, particularly k-means clustering in data streams, combinatorial optimization, and approximation algorithms. Her work bridges theoretical computer science with practical applications in big data processing. She has made significant contributions to understanding the theoretical foundations of clustering algorithms while developing efficient implementations for real-world applications. Professor Schmidt's publication record shows a consistent evolution from theoretical analysis of k-means to practical implementations for big data environments. Her recent work explores fairness in clustering, privacy-preserving techniques, and efficient algorithms for high-dimensional data. She has published in top-tier conferences including SODA, ICALP, ESA, and ITCS, demonstrating both theoretical rigor and practical relevance. Best Student Paper Award at ESA 2012 (joint work with Martin Groß, Jan-Philipp W. Kappmeier, and Daniel Schmidt) She actively supervises numerous Master's and Bachelor's students, with current advisees including Lena Carta, Lukas Drexler, and Anna Arutyunova. Her research group includes members such as Anja Rey, Julian Wargalla, and Annika Hennes. She teaches advanced courses in algorithms and data structures, with a focus on randomized algorithms and efficient algorithm design for big data problems. Professor Schmidt leads the Algorithms and Data Structures research group at Heinrich-Heine-Universität Düsseldorf, which focuses on developing and analyzing efficient algorithms for fundamental computational problems, with particular emphasis on clustering, geometric data analysis, and big data applications.