جست‌وجوی استاد و پژوهشگر

Maximilian Starkمشاهده پروفایل

مدرس

• Communication Systems
• Machine Learning
• Information Theory
• +۳ مورد دیگر

Dr. Maximilian Stark is a Lecturer at the Institute of Communications, TU Hamburg. His research focuses on machine learning-driven advancements in communication systems, particularly applying the information bottleneck method to decoding algorithms, signal processing, and quantization techniques. His work bridges theoretical information theory with practical implementation challenges in coding and channel design. Key research areas include LDPC and polar codes optimization, low-bitwidth decoding architectures, and distributed signal processing frameworks. Recent publications emphasize adaptive learning systems for error resilience under quantization constraints and hardware-efficient receiver design. Dr. Stark's academic contributions span over 20 peer-reviewed articles since 2016, with a strong emphasis on integrating machine learning principles into traditional communication engineering problems. Notable themes include neural decoding paradigms, resource-constrained quantization strategies, and distributed information compression methods. No scientific awards explicitly listed. Academic advising details and lab affiliations are not provided in source materials.

Davud Rostam-Afscharمشاهده پروفایل

استاد

• Public Economics
• Macroeconomics
• Microeconomics
• +۷ مورد دیگر

Prof. Dr. Davud Rostam-Afschar is an economist at the University of Mannheim , focusing on firm and household decision-making in public and macroeconomics. His work bridges empirical research with policy implications, particularly on taxation, labor markets, and business dynamics. Academic Appointments : Universitätsprofessor (W2) since 2022, Visiting Professor at Mannheim, and Akademischer Rat (Assistant Professor) at Hohenheim. Research Collaborations : Affiliated with Harvard University, UC Berkeley, Queen Mary University London, and Johns Hopkins University. Research Themes include: Public Economics: Taxation, welfare reforms, and labor market institutions Macroeconomics: Inflation expectations and business dynamics Econometrics: Adaptive survey experiments and panel data analysis Teaching Contributions span advanced econometrics, empirical public economics, and data science courses at Mannheim and Hohenheim. Recognized for interactive and conceptually rich instruction. Key Publications examine taxation's impact on entrepreneurship, occupational licensing effects, and real-time business behavior through the German Business Panel. His work appears in journals like Labour Economics , European Accounting Review , and Public Finance Analysis . Awards & Recognition 2024: Featured in Handelsblatt and Süddeutsche Zeitung media 2019: Best Teaching Awards at University of Hohenheim 2017: TRR 266 Collaborative Research Center contributions Leadership includes roles in the German Business Panel, an influential semi-annual survey of firm decision-makers, and developing Stata modules for econometric analysis.

Anil Yildizمشاهده پروفایل

پژوهشگر

• Geohazards
• Computational Engineering
• Landslide Modeling
• +۷ مورد دیگر

Dr. Anil Yildiz is a senior researcher and Deputy Director at the Chair of Methods for Model-based Development in Computational Engineering (MBD), RWTH Aachen University, Germany. He leads the research group Engineering Climate Change Response and is actively involved in computational modeling of geohazards and climate-responsive engineering systems. PhD in Civil Engineering, ETH Zurich (2018) M.Sc. in Civil Engineering, Bogazici University (2013) B.Sc. in Civil Engineering, Bogazici University (2010) Dr. Yildiz's research centers on geohazards , particularly shallow landslides , soil-plant-atmosphere interactions , and computational modeling . He is deeply committed to reproducible workflows , data-integrated modeling , executable publications , and FAIR data principles . His work integrates machine learning , uncertainty quantification , and surrogate modeling to improve the reliability and efficiency of geohazard simulations. His recent publications (2023–2025) emphasize FAIR data practices , reproducible workflows , and uncertainty-aware modeling in landslide and sea ice research. Key themes include modular frameworks , data reuse , and executable scientific publications , reflecting a strong trend toward open, transparent, and computationally rigorous geoscience. Scientific awards and recognitions include: IDEA League Fellowship (2024) Culmann Prize, ETH Zurich (2019) Outstanding Doctoral Thesis in Civil Engineering (2018) Roland Schlich Early Career Scientist's Travel Support, EGU Dr. Yildiz has been involved in significant research projects, including work on green infrastructure and ground heat exchange at the National Green Infrastructure Facility (UK), and has contributed to interdisciplinary collaborations involving geotechnical engineering , climate modeling , and infrastructure resilience . He has not formally listed advisees, but his collaborative publications suggest active mentorship and team leadership. His research group focuses on developing reliable, data-driven models for climate-related geohazards. He has been affiliated with leading institutions including ETH Zurich, Newcastle University, University of Göttingen, and RWTH Aachen University, demonstrating a strong international research trajectory.

Dirk Schweimمشاهده پروفایل

استاد

• Machine Learning
• Artificial Neural Networks
• Estimation of Distribution Algorithms (EDAs)
• +۳ مورد دیگر

Prof. Dr. Dirk Schweim is a Professor at the Department of Business Information Systems and Business Administration at Johannes Gutenberg University Mainz. His research focuses on machine learning, particularly neural networks and Estimation of Distribution Algorithms (EDAs) for combinatorial optimization. He is currently investigating EDAs using neural network models for variable-length encoding problems. Schweim has held roles including research assistant under Prof. Franz Rothlauf since 2016 and part-time project management at R+V Versicherung (2013–2016). Teaching responsibilities include lecturing 'Business Mathematics/Statistics I' at RheinMain University of Applied Sciences and coordinating tutorials for 'Internet Technologies and E-Business'. He has supervised numerous Bachelor’s and Master’s theses since 2016/17. His academic background includes a Master of Science in Management from Mainz University (2013–2016) and a Bachelor of Science in Business Informatics from HWR Berlin (2008–2011). Research interests span genetic programming, ERP systems, and decision support systems. Key publications include work on denoising autoencoder LSTMs in genetic programming and the influence of grammars in evolutionary algorithms.

• Computational Geometry
• Algorithms
• Trajectory Analysis
• +۴ مورد دیگر

Prof. Maike Buchin is a Professor of Computer Science at Ruhr-University Bochum, leading the Theoretical Computer Science/Algorithmics department. She serves as Studiendekanin (Dean of Studies) for Computer Science. Her academic journey includes roles as Visiting Professor at TU Dortmund (2017-2019), Junior Professor at Ruhr-University Bochum (2013-2017), and Assistant Professor at TU Eindhoven (2011-2013). She holds a Doctorate in Computer Science from Freie Universität Berlin (2007) and a Mathematics Diploma from the University of Münster (2003). Her research focuses on computational geometry, algorithms, and trajectory analysis. Key interests include Fréchet distance computations, geometric clustering, and applications in geographic information systems. She has contributed to trajectory grouping structures, map construction from subtrajectories, and efficient algorithms for curve analysis. Publications highlight work on Fréchet distance variants, clustering algorithms, and geometric optimization. Recent trends emphasize practical applications of theoretical algorithms in movement data analysis and network extensions. Her research often bridges algorithmic theory with real-world spatial data challenges. No scientific awards or grants are explicitly listed in the provided materials. She advises graduate students through her academic roles and collaborates internationally in computational geometry and related fields.

• Machine Learning Applications in Agriculture
• Sensor Technology for Animal Health Monitoring
• Data Science in Bioeconomy
• +۳ مورد دیگر

Lukas Minogue is a researcher at the Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB) Potsdam , affiliated with their Sensor Technology and Modeling department. He holds a Master's degree in Mathematics (University of Potsdam, 2019-2023) with focus on machine learning and statistical data analysis, and a prior Bachelor's degree in Mathematics (University of Potsdam, 2013-2019) concentrating on statistical modeling. His research specializes in applying machine learning models to agricultural sensor data , particularly for mastitis detection in dairy cows through automated milking systems (AMS). Key projects include: MEDICow (German-Irish collaboration): Developing AI-driven mastitis risk assessment using multisensor data AMRAfrica : Strengthening antibiotic resistance monitoring through data management systems in DR Congo INNOVAFRIKA (DFG-funded): Digital innovations in crop-livestock systems Recent publications demonstrate his work on: Time series analysis of AMS data with ML classifiers (logistic regression, random forest, neural networks) Comparative performance of SMOTE resampling techniques Feature engineering for dairy health metrics (e.g., fat-protein ratio) His technical expertise spans Python (scikit-learn, NumPy), data visualization (Matplotlib), and statistical modeling frameworks.

• Machine Learning
• Natural Language Processing
• Climate Modeling
• +۶ مورد دیگر

Professor Andreas Hotho leads the Data Science Chair at the University of Würzburg's Faculty of Mathematics and Computer Science, while serving as founding spokesman of the Center for Artificial Intelligence and Data Science (CAIDAS). His academic journey includes senior researcher roles at the University of Kassel and research positions at the AIFB Institute (University of Karlsruhe) and L3S Research Center (Hannover). Research Focus: Machine Learning, Large Language Models, Climate Modeling, Semantic Web, and Knowledge Graphs Methodologies: ConvMOS architecture, DenseLoss approach, LanZ-DML, and BibSonomy system Applications: Environmental monitoring, Digital Humanities, Smart Beehive Analysis, and Social Media Analytics With over 15 recent publications, his work spans climate model output statistics, zero-shot metric learning, imbalanced regression techniques, and German language model development. He serves as editor-in-chief for the Transactions on Graph Data and Knowledge and maintains active roles in academic governance through PC memberships at ECML-PKDD, WWW, and other major conferences. Scientific awards include: NAACL 2024 Best Paper Honorable Mention ICDM 2023 Best Paper Award NeurIPS 2020 Best ML Innovation Award ISWC 2018 SWSA Ten-Year Award WWW 2015 Best Paper Award FAIML 2020 Best Student Paper His research group includes doctoral researchers like Jan Pfister, Julia Wunderle, and Albin Zehe. Key projects include LitBERT for literary analysis, BigData@Geo series for climate modeling, and BeeConnected for ecosystem monitoring.

Martin Gadeمشاهده پروفایل

پژوهشگر

• Coastal Remote Sensing
• Air-Sea Interactions
• Ocean Eddy Dynamics
• +۳ مورد دیگر

Dr. Martin Gade is a Research Scientist at the Institute of Oceanography (IfM) of the University of Hamburg. His work focuses on Coastal Remote Sensing and Air-Sea Interactions , with expertise in Synthetic Aperture Radar (SAR) applications for marine environmental monitoring. He leads projects such as FOR 5267: Biogeochemical Processes in Marine Surface Films and Space for Shore , emphasizing interdisciplinary approaches to coastal erosion and pollution detection. His research integrates machine learning with oceanography, developing algorithms for eddy detection and habitat classification using satellite data. Notable contributions include studies on surfactant impacts on wave dynamics and long-term monitoring of oil leakage from shipwrecks. Gade collaborates internationally, presenting at conferences like EGU and IGARSS, and actively contributes to open-access science through platforms like MDPI Oceans . Key projects include ReSCCoME for changing coastal environments and ESA Coastal Erosion , leveraging Copernicus data for policy-relevant insights. His work bridges technical innovation (e.g., GLUENet for image dehazing) with practical applications in environmental governance and marine resource management.

Vitalii Konarovskyiمشاهده پروفایل

استاد

• Stochastic Analysis
• Interacting Particle Systems
• Stochastic Partial Differential Equations
• +۳ مورد دیگر

Prof. Dr. Vitalii Konarovskyi is a Professor of Stochastic Processes and their Applications at the University of Hamburg's Department of Mathematics within the Faculty of Mathematics, Informatics and Natural Sciences. He holds a W2 professorship and has held academic roles at institutions such as Universität Leipzig, Bielefeld, and Jena. His research focuses on stochastic analysis, interacting particle systems, stochastic PDEs, measure-valued diffusions, random graphs, and machine learning. He actively teaches advanced courses in stochastic processes, mathematical statistics, and related fields, including recent modules like 'Stochastic Differential Equations' and 'Large Deviations.' Education & Career: PhD in Mathematics (2010-2015), Institute of Mathematics of NAS of Ukraine W2 Professor, University of Hamburg (2023–present) Postdoc roles at Universität Bielefeld, Leipzig, and the Max Planck Institute Research Interests: His work bridges stochastic processes with applications in physics and machine learning, emphasizing systems with singular interactions, measure-valued dynamics, and coalescing/fragmenting particle systems. Recent focus includes the Dean-Kawasaki equation, stochastic gradient descent dynamics, and stochastic block models. Awards & Recognition: No specific awards mentioned, but his contributions to stochastic analysis and coalescing particle systems are internationally recognized through collaborative research centers like SFB 1283 at Bielefeld. Teaching & Supervision: Extensive teaching portfolio includes stochastic calculus, large deviations, and mathematical methods for physicists. Active in supervising PhD students through research groups and seminars on topics like machine learning and stochastic flows. Labs/Teams: Collaborates with groups like Benjamin Gess's team at Bielefeld and Max von Renesse's group, contributing to projects on fluctuating hydrodynamics and mean-field limits.

Philipp Bachمشاهده پروفایل

پژوهشگر

• Causal Inference
• Machine Learning
• High-Dimensional Econometrics
• +۶ مورد دیگر

Dr. Philipp Bach is a Researcher at the University of Hamburg Business School's Department of Statistics with Application in Business Administration. He holds a PhD in Economics from Hamburg University (2021) and a Postdoc in Statistics (since 2021). His research focuses on causal inference using machine learning methods, high-dimensional econometrics, and applications in labor, health, and financial economics. Key areas include hyperparameter tuning for causal estimation, sensitivity analysis, and difference-in-difference models. His work emphasizes practical implementations, such as the DoubleML package for R and Python, which facilitates double machine learning techniques. He has published in top journals like the Journal of the Royal Statistical Society and Journal of Statistical Software. Current projects explore multimodal data causal estimation and pandemic shielding strategies using SEIR models. No formal awards are listed, but his contributions are widely recognized in computational econometrics. Bach advises no listed students but collaborates with institutions like Booking.com on sensitivity analysis applications. His lab focuses on bridging machine learning and traditional econometric methods for real-world policy analysis.

Oliver Schachtمشاهده پروفایل

پژوهشگر

• Causal Inference
• Machine Learning
• Causal Reinforcement Learning
• +۴ مورد دیگر

Oliver Schacht is a Researcher at the University of Hamburg Business School's Department of Statistics, specializing in Statistics with Applications in Business Administration. His research focuses on Causal Inference using Machine Learning, Causal Reinforcement Learning, and Deep Learning, with applications in Economics, Health, and Engineering/Management. He holds a B.Sc. (2018) and M.Sc. (2021) in Industrial Engineering and Management from the University of Hamburg, and was a Visiting Student at San Diego State University (2019). Since October 2021, he has been pursuing a Doctorate in Statistics under the Chair of Statistics with Application in Business Administration. Education: PhD Candidate: Statistics, University of Hamburg (since 2021) M.Sc.: Industrial Engineering and Management, Universität Hamburg (2021) B.Sc.: Industrial Engineering and Management, Universität Hamburg (2018) His research interests bridge statistical methodologies and their practical implementations in diverse fields. He is affiliated with the Statistics department at the Hamburg Business School and contributes to interdisciplinary studies at the intersection of data science and business administration.

• Mathematical Optimization
• Conic Optimization
• Interior Point Methods
• +۳ مورد دیگر

Prof. Dr. Florian Jarre is affiliated with the Mathematical Institute at Heinrich-Heine-Universität Düsseldorf, specializing in mathematical optimization. His research spans conic optimization, interior point methods, semidefinite programming, and nonlinear optimization with applications in computational mathematics. Research interests focus on theoretical and applied aspects of optimization algorithms, including convergence analysis of iterative methods, complexity theory for convex problems, and development of efficient computational techniques for large-scale optimization challenges. Work extends to applications in machine learning and systems biology. Publications demonstrate consistent focus on optimization theory advancements, particularly in interior-point methods and convex programming. Recent work explores connections between optimization and machine learning, including SVM training methods and stochastic gradient descent variants.

• Algorithms
• Artificial Intelligence
• Optimization
• +۳ مورد دیگر

Laura Codazzi is a researcher at the Institute for Algorithms and Complexity (E-11) at Technische Universität Hamburg. Her work bridges algorithmic research with practical applications in energy systems, optimization, and data analysis. She focuses on developing fair dynamic pricing mechanisms, optimizing microgrid operations, and advancing statistical methods for complex datasets. Her research interests span operations research, algorithmic game theory, and machine learning applications. Key contributions include envy-free pricing schemes and Gaussian graphical models for spectrometric data analysis. She actively collaborates on interdisciplinary projects involving energy systems and computational economics. Though no specific awards are listed, her publications reflect engagement with top-tier conferences (e.g., IJCAI) and journals. Laura contributes to the institute's research portfolio without explicitly listed grants or lab affiliations in the provided data.

• Semiconductor Reliability
• Machine Learning for CAD
• Hardware Security
• +۶ مورد دیگر

Hussam Amrouch is a Professor (W3) at the Technical University of Munich (TUM) , leading the Chair of AI Processor Design (AI-Pro) and affiliated with the Munich Institute of Robotics and Machine Intelligence (MIRMI) . He also serves as the head of Semiconductor Test and Reliability (STAR) at the University of Stuttgart. His academic journey includes a Ph.D. (Dr.-Ing.) summa cum laude from Karlsruhe Institute of Technology (KIT) and leadership of the Dependable Hardware Research Group at KIT. His research spans design for reliability and testing , machine learning for CAD , hardware security , approximate computing , and emerging technologies with a focus on ferroelectric devices . Recent publications highlight applications in hyperdimensional computing , cryogenic circuits , and neuromorphic systems . Amrouch has received 8 HiPEAC Paper Awards and best paper nominations at top EDA conferences. His work is funded by organizations including the German Research Foundation (DFG) , Advantest Corporation , and the U.S. Office of Naval Research (ONR) . He has over 200 publications across multidisciplinary domains from semiconductor physics to computer architecture. As an editor at Nature Scientific Reports , he contributes to journal oversight. His technical leadership extends to program committees of major EDA conferences like DAC, ASP-DAC, and ICCAD.

Peyman Saemianمشاهده پروفایل

پژوهشگر

• Remote Sensing
• Hydrology
• Geodesy
• +۴ مورد دیگر

Peyman Saemian is a Scientific Employee at the Institute of Geodesy , part of the University of Stuttgart . His research focuses on applying geodetic and remote sensing techniques to study hydrological systems, water resource management, and climate change impacts. He specializes in satellite gravimetry (GRACE/GRACE-FO), altimetry, and data assimilation for environmental monitoring. Key research areas include: Global water storage anomalies and runoff dynamics Integration of remote sensing data with hydrological models Drought assessment using satellite-based indices Environmental disaster analysis (e.g., Lake Urmia restoration) Development of tools like HydroSat for water cycle monitoring His work bridges geodesy, hydrology, and environmental science, contributing to climate resilience strategies and precision agriculture.