جست‌وجوی استاد و پژوهشگر

• Natural Language Processing
• Machine Learning
• Computational Linguistics
• +۳ مورد دیگر

Dr. Mor Geva is a prominent researcher in natural language processing and machine learning, focusing on transformer-based language models, interpretability, and knowledge representation. Their work addresses fundamental questions about model reasoning, uncertainty expression, and factual recall mechanisms. Key research themes: language model interpretability, multi-hop reasoning limitations, numerical encoding, cross-modal knowledge gaps Recent publications analyze gradient projection, attention head functionality, and fallback behaviors under uncertainty 2023-2025 studies reveal insights into factual association recall, knowledge editing ripple effects, and vision-language model interactions. Collaborations span top institutions in ACL, EMNLP, and ICLR venues. Current research explores parametric knowledge tracing, encoder-decoder dynamics, and attention hijacking phenomena. Tools like LM-Debugger enable interactive model inspection, while theoretical work examines base-10 numerical encoding and task vector creation.

Albin Zeheمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Text Mining
• Sentiment Analysis
• +۳ مورد دیگر

Albin Zehe is a Researcher at the Chair of Data Science (Informatik X) within the Institute of Computer Science at the University of Würzburg's Faculty of Mathematics and Computer Science. He holds an M.Sc. degree and has maintained an active research and teaching profile since at least 2016, with his office located in Room 03.021 of the CAIDAS Building on the Hubland Nord campus. Zehe's research primarily focuses on applying Natural Language Processing techniques to literary analysis, with a special emphasis on sentiment analysis in German literature. His work bridges computer science and humanities, developing computational methods to analyze novels and other literary works. He has expressed particular interest in Tolkien's writings, reflected in his 2022 publication analyzing Tolkien's Legendarium using NLP and Graph Neural Networks. His publication record shows a progression from literary analysis (2016-2018) to broader NLP applications including social media analysis (Twitch.tv in 2020) and time series forecasting (2020). Recent work has expanded to graph-based analysis of literary universes and scene segmentation in fiction. Zehe has served as PC member for major conferences including ACL (2021 & 2023), EMNLP (2021 & 2022), ECMLPKDD (2020 & 2021), ECAI (2020), and AAAI (2020-2022), demonstrating his active role in the academic community. He has taught Machine Learning for Natural Language Processing every summer semester from 2018 to 2023, along with the Natural Language Processing Practicum , and previously taught courses in algorithms, information retrieval, and Web 2.0 technologies. He has co-organized academic challenges including the ChAT challenge at ECML-PKDD 2020 and the Shared Task on Scene Segmentation at KONVENS 2021.

Matthias Springsteinمشاهده پروفایل

پژوهشگر

• Computer Vision
• Deep Learning
• Multimedia Information Retrieval
• +۵ مورد دیگر

Matthias Springstein is a researcher at the Technical Information Library (TIB), which is affiliated with Leibniz University Hannover. He is part of the Visual Analytics research group within TIB's Research & Development department, focusing on advanced computer vision and multimedia information retrieval techniques. His research interests span multiple areas of artificial intelligence with emphasis on computer vision applications. Springstein specializes in web-supervised learning for visual concepts, incremental learning approaches, and methods to minimize manual labeling efforts for training data. His work bridges theoretical machine learning with practical applications in digital humanities, particularly in art-historical image analysis and film/video studies. His publication record shows a consistent focus on multimodal analysis, with recent work exploring knowledge graphs for image classification, large-scale hierarchical classification of art-historical images, and computational tools for film analysis. The research demonstrates progression from foundational work in image-text relations and depth estimation toward increasingly sophisticated applications in cultural heritage and scholarly media analysis. Notable achievements include receiving the Best Paper Award at the International Conference on Multimedia Retrieval (ICMR) in 2019 for his work on semantic image-text relations. Springstein has developed TIB AV-Analytics, a computational platform for scholarly video analysis that has been presented at multiple major conferences (SIGIR 2023, SCSMI 2024), indicating significant impact in both the information retrieval and film studies communities.

Ilhan Mutluمشاهده پروفایل

پژوهشگر

• Environmental Informatics
• Machine Learning
• Graph Databases
• +۴ مورد دیگر

Dr. İlhan Mutlu is a postdoctoral researcher at the Helmholtz Centre for Environmental Research - UFZ in the Department of Computational Biology and Chemistry since 2023. Previously, he worked at TU Dresden (2021-2023) and IAV GmbH (2017-2021). He holds a PhD in Control and Automation Engineering (Istanbul Technical University, 2017) and focuses on data integration , graph databases , and machine learning models for toxicology prediction . His research bridges environmental data analysis with automated systems , particularly in freshwater toxicity assessment and optimal control techniques . Recent projects include ChEOS (toxicity prediction in European waters) and SafePol (pollinator protection). His technical expertise spans Neo4j , CleanGeoStreamR , and ecoKI platform development. Publications emphasize data curation for environmental monitoring, graph-based toxicity tools , and ML-driven chemical risk assessment . His work also involves automated driving systems , including model-free trajectory planning and robust control algorithms . Though no scientific awards are listed, his contributions span interdisciplinary domains combining environmental science with computational engineering .

Martin Semmannمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Natural Language Processing
• Digital Transformation
• +۷ مورد دیگر

Dr. Martin Semmann serves as the Managing Director of the Hub of Computing and Data Science (HCDS) at the University of Hamburg since 2023. Previously, he worked as a Postdoctoral Researcher & Managing Director at ahoi.digital (2018-2023) and as a Scientific Staff Member in the IT Management and Consulting (ITMC) department at the University of Hamburg (2011-2018). His academic background includes an M.Sc. and B.Sc. in Business Informatics from Georg-August-Universität Göttingen. Dr. Semmann's research spans multiple interdisciplinary domains with a strong focus on Artificial Intelligence applications for social good , including democratic discourse monitoring, hate speech detection, and equitable access to medical knowledge. His work bridges Natural Language Processing with political science, ethics, and low-resource language processing, particularly focusing on Amharic language applications. He has made significant contributions to Digital Transformation research, especially in sustainable mobility and public transportation systems. His publication portfolio demonstrates expertise across multiple domains including Retrieval-Augmented Generation systems, multilingual polarization analysis, interdisciplinary collaboration frameworks, and decolonial AI approaches. These works show a consistent pattern of addressing societal challenges through technological innovation while emphasizing ethical considerations and cultural relevance. Dr. Semmann actively supervises numerous final theses across diverse topics including Digital Transformation, Benefit Management, and Service Systems. His current research projects include WizARd (knowledge networking through augmented reality) and Rescue-Mate (dynamic situation creation for emergency responders). Previously, he contributed to projects such as IGT (Information Governance Technologies), ExTEND, PROMIDIS, and ProduSE. As an active member of the academic community, Dr. Semmann serves on editorial boards and as a reviewer for prominent journals including Business & Information Systems Engineering, Communications of the AIS, and the European Conference on Information Systems. He is also a founding member of the Artificial Intelligence Center e.V. since 2019.

Marco Bohnhoffمشاهده پروفایل

استاد

• Seismology
• Seismomechanics
• Geomechanics
• +۷ مورد دیگر

Prof. Dr. Marco Bohnhoff is Head of Section 4.2 (Geomechanics and Scientific Drilling) at the GFZ Potsdam German Research Center for Geosciences and Professor of Experimental and Borehole Seismology at Freie Universität Berlin. Since 2019, he has served as Executive Director of the International Continental Scientific Drilling Program (ICDP). His research focuses on earthquake physical processes at both reservoir and plate-tectonic scales, with particular emphasis on the North Anatolian Fault in Turkey and induced seismicity from human activities. Bohnhoff's research interests span seismology, seismomechanics, geomechanics, borehole geophysics, and seismic monitoring of induced seismicity. His work investigates earthquake physics, seismotectonics, the seismic cycle at the North Anatolian Fault Zone, shear-wave anisotropy, and wide aperture seismics. He leads the ICDP-driven Geophysical Observatory at the North Anatolian Fault (GONAF) near Istanbul and similar projects in Koyna/India and STAR observatory in Italy. His recent publications reveal trends in fault mechanics, earthquake prediction, and seismic hazard assessment, particularly for the Istanbul region which faces significant risk from a potential M7+ earthquake. His laboratory work combines with field observations to understand the relationship between fault properties, stress states, and earthquake occurrence across different scales. Elected member of the Leibniz-Sozietät der Wissenschaften zu Berlin e.V. (2018) Heisenberg-Fellowship awarded by Deutschen ForschungsGemeinschaft (DFG) (2007) Bohnhoff has supervised more than 10 PhD students to completion and raised approximately 16 million euros in third-party funding. He leads major research projects including the GONAF observatory and participates in international collaborations studying seismic hazards. His work bridges fundamental earthquake physics with practical applications for seismic hazard mitigation, particularly in densely populated regions like Istanbul that face significant seismic risk.

Marcus Müllerمشاهده پروفایل

استاد

• Polymer Physics
• Soft Matter Physics
• Biophysics
• +۳ مورد دیگر

Prof. Marcus Müller is a Professor of Theoretical Physics at the University of Göttingen's Faculty of Physics, Department of Theoretical Physics. His research employs advanced computational methods to investigate fundamental phenomena in polymer systems, soft matter, and biological membranes, with significant contributions to understanding non-equilibrium dynamics and self-assembly processes. His research portfolio spans: Polymer Physics : Dynamics of polymer melts, block copolymer self-assembly, and phase separation mechanisms Soft Matter Physics : Active matter systems, membrane biophysics, and dissipative structures Computational Physics : Development of specialized simulation techniques including peridynamic-enhanced Fourier spectral methods and slip-spring models Analysis of his 15 most recent publications (2023-2025) reveals a dominant focus on the interplay between processing conditions and nanostructure formation in block copolymers, with expanding applications to battery electrolytes and biological systems. Key trends include the investigation of non-equilibrium pathways in spinodal decomposition, membrane fission/fusion mechanisms, and reaction-driven organization in active liquids, demonstrating strong integration of theoretical modeling with experimental validation. No scientific awards were documented in the available source material. While specific advising relationships and grant details were not provided, his extensive publication record suggests active mentorship of graduate students in computational soft matter physics. The research direction indicates ongoing investigation of fundamental transport phenomena in polymeric systems with applications to energy storage and cellular biophysics. His work appears conducted within computational research groups at the Department of Theoretical Physics, focusing on molecular simulations and theoretical modeling of complex soft matter systems, with particular emphasis on membrane dynamics and polymer self-assembly under non-equilibrium conditions.

Dan Arielyمشاهده پروفایل

استاد

• Behavioral Economics
• Social Psychology
• Decision-Making
• +۳ مورد دیگر

Dr. Dan Ariely is a James B. Duke Professor of Psychology and Behavioral Economics at Duke University. He is a renowned social scientist, bestselling author of Predictably Irrational , and a leading expert on human behavior and decision-making processes. His recent work focuses on the psychology of misinformation and conspiracy theories , as detailed in his book Misbelief: What Makes Rational People Believe Irrational Things . As a behavioral economist and psychologist, Ariely explores cognitive biases , emotional decision-making , and social trust mechanisms . His research combines Experimental psychology Behavioral economics principles Human-computer interaction Trust in institutions to understand how people form irrational beliefs. Ariely's work has been featured in major media outlets, and he maintains active engagement with public audiences through social media platforms. He has experienced being targeted by disinformation campaigns himself, which informed his personal and professional exploration of belief formation and truth perception in the digital era.

Jun Huangمشاهده پروفایل

استادیار

• Electrochemistry
• Electrocatalysis
• Computational Materials Science
• +۲ مورد دیگر

Dr. Jun Huang is an Assistant Professor at Forschungszentrum Jülich, leading the Helmholtz Young Investigator Group focused on the 'Theory of Electrocatalytic Interfaces.' He is affiliated with the Institute of Energy Technologies (IET), specifically in the department of Theory and Computer-Based Modelling of Materials in Energy Technology. His research centers on theoretical electrocatalysis and electrochemical interfaces, with expertise in: Electrical double layer phenomena and capacitance behavior Density-potential functional theory for metal-solution interfaces Multiscale modeling of electrochemical reaction environments Electrocatalyst design through computational methods Ion transport and interfacial structuring in energy systems His recent publications demonstrate a strong focus on developing fundamental theoretical frameworks for understanding electrocatalytic interfaces, with recurring themes in double-layer effects, reaction kinetics, and computational method development. The work bridges theoretical electrochemistry with applications in energy conversion and storage. Major recognitions include: Helmholtz Young Investigator Group Grant European Research Council Starting Grant Dr. Huang leads a computational research group developing advanced theoretical models to decipher electrocatalytic processes. His team focuses on creating predictive frameworks for interfacial reactions relevant to energy technologies.

Xi Wangمشاهده پروفایل

پژوهشگر

• Computer Vision
• Computer Graphics
• Vision Science
• +۷ مورد دیگر

Dr. Xi Wang is a postdoctoral researcher at the Technical University of Munich (TUM) within the Computer Vision Group (Informatics 9) under Prof. Daniel Cremers, and a researcher at the Computer Vision and Geometry Lab at ETH Zurich led by Prof. Marc Pollefeys. He collaborates with Prof. Luc Van Gool at INSAIT and has affiliations with MIT and Adobe Research. His research spans computer vision, computer graphics, vision science, and vision-language multimodal learning , focusing on modeling human common sense, intent-driven behavior, and egocentric scene understanding. Research Highlights: Developed multimodal world models (GEM) for fine-grained ego-motion and object dynamics Created vision-language interior design systems (I-Design) and driver trajectory prediction models Advanced 3D human-object interaction reconstruction (ROMEO) and eye-tracking-based action anticipation Explored Gaussian splatting for dynamic scene understanding (EgoGaussian) Scientific Recognition: Recipient of the ECCV 2024 Workshop Best Paper Award for ROMEO $1.6 million grant from BMBF for establishing his independent TUM research group Accepted papers at top venues: CVPR, ECCV, ICLR, ETRA, ISMAR

Christoph M. Flathمشاهده پروفایل

استاد

• Business Analytics
• Operations Research
• Information Systems
• +۳ مورد دیگر

Prof. Dr. Christoph M. Flath is the Chair of Business Information Systems and Business Analytics at Julius-Maximilians-Universität Würzburg's Faculty of Management and Economics (Department of Business Management). He has held this position since April 2018, previously serving as Junior Professor for Operations Management at the same institution (2014-2018). His academic journey includes leadership roles at the Karlsruhe Institute of Technology where he headed the Smart Grids and Energy Markets division. Education: Doctorate (with distinction) in Economics, Karlsruhe Institute of Technology (2013) Diploma in Industrial Engineering (with distinction), University of Karlsruhe (2008) Study abroad program at University of Toronto, Canada (2006-2007) Research Focus: Prof. Flath's work bridges business analytics, operational optimization, and information systems, with applications in energy markets, supply chains, and industrial data science. His research employs advanced quantitative methods to solve complex problems in smart grids, sharing economies, and digital innovation ecosystems. Awards and Honors: Doctoral dissertation awarded with distinction Diploma in Industrial Engineering awarded with distinction Team Leadership: Directs research group including six team members (Bianca Hartmann, Robin Derzbach, Aabhash Dhakal, Nico Elbert, Tim Lachner, Nikolai Stein), focusing on business analytics applications across industrial domains.

• Numerical Mathematics
• Data Assimilation
• Hamiltonian Dynamics
• +۵ مورد دیگر

Sebastian Reich is a Professor of Numerical Analysis at the University of Potsdam and holds an honorary Visiting Professorship at Imperial College London . He leads the Chair of Numerical Mathematics and serves as Editor-in-Chief of the SIAM/ASA Journal on Uncertainty Quantification since 2021. Research Interests Numerical methods for Hamiltonian systems Data assimilation in geoscience Stochastic particle filters Bayesian inference algorithms Molecular dynamics simulation Multi-scale modeling Collaborative Projects : Principal Investigator and former Speaker (2017-2024) of SFB 1294 Data Assimilation , a DFG-funded Collaborative Research Center Active participant in SFB 1114 Scaling Cascades in Complex Systems at Freie Universität Berlin Books Authored : Probabilistic Forecasting and Bayesian Data Assimilation (Cambridge UP, 2015) Simulating Hamiltonian Mechanics (Cambridge UP, 2005) Technical Contributions : Development of symplectic integration methods Innovations in ensemble Kalman filtering Regularization approaches for geophysical models Stochastic algorithms for molecular simulations

Doru C. Lupascuمشاهده پروفایل

استاد

• Ferroic Materials
• Perovskite Solar Cells
• Cement Recycling
• +۵ مورد دیگر

Prof. Doru C. Lupascu is a leading academic in Materials Science at the University of Duisburg-Essen . His research spans ferroic materials , perovskite solar cells , cement recycling , and electrocaloric effects . He supervises PhD students like Astita Dubey and Andrei Karabanov , whose work on photocatalysis and mechanical ice properties has earned recognition. Key Research Themes: Ferroic and multiferroic materials Lead-free perovskites for solar and photocatalytic applications Recycling of cement and concrete via thermal reactivation Electrocaloric and dielectric properties for cooling technologies Recent Article Trends highlight his group’s focus on machine learning in materials discovery , high-throughput screening of perovskites, and novel composite structures for energy storage. These works often intersect with environmental sustainability (e.g., UpCement project) and fundamental physics of ferroic systems. Notable Collaborations: The institute works with institutions like University of Cape Town (Antarctic sea ice expeditions), CentraleSupélec (relaxor ferroelectrics), and Oak Ridge National Lab (postdoctoral research). Projects such as UpCement and RelaxSolaire are funded by German and European agencies , including the Ministry for Economic Affairs and Climate Action (NRW) and DFG .

• Wireless Sensor Networks
• Robotics
• Cognitronics
• +۷ مورد دیگر

Dr. Marc Hesse serves as Team Leader of the Cognitronics & Sensor Technology Group at Bielefeld University's Faculty of Engineering and is also a Board member of the Center for Cognitive Interaction Technology (CITEC). His work bridges engineering, robotics, and sensor technology with practical applications across multiple domains. Dr. Hesse's research spans wireless sensor networks, robotics, machine learning applications, and Industry 4.0 technologies. His work focuses on developing practical solutions for real-world problems, including physiological monitoring systems, UWB localization in challenging environments, and edge computing applications for smart grids and manufacturing. He has made significant contributions to educational robotics through the AMiRo platform, which integrates research and teaching in robotics education. His publication record shows consistent output across multiple domains, with recent work emphasizing machine learning applications in sensor networks, edge computing implementations, and digital twin technologies. The research demonstrates a trajectory from fundamental sensor and system design to applied implementations in agriculture, healthcare, and industrial settings. Dr. Hesse collaborates extensively across disciplines and institutions, with publications spanning biomedical engineering, robotics, electrical engineering, and sports science. His work often addresses the practical challenges of implementing theoretical concepts in real-world environments with resource constraints.

• Signal Processing
• Machine Learning
• Acoustic Communication Systems
• +۴ مورد دیگر

Rainer Martin is a Professor of Information Technology and Communication Acoustics at Ruhr-Universität Bochum, where he has been affiliated since 2003. He served as Dean of the Faculty of Electrical Engineering and Information Sciences from 2007 to 2009. He holds a Dipl.-Ing. and Dr.-Ing. from RWTH Aachen University and an M.S.E.E. from Georgia Institute of Technology. His research focuses on signal processing, machine learning, and acoustic systems for voice communication, hearing instruments, and human-machine interfaces. Key areas include speech enhancement, noise reduction, binaural processing, cochlear implant optimization, and acoustic sensor networks. His work integrates deep learning with traditional signal processing for real-world applications. Recent publications emphasize neural networks for noise control, hearing aid algorithms, radar interference mitigation, and auditory perception modeling. Trends show strong convergence of machine learning with acoustic engineering, particularly in low-power embedded systems and healthcare applications. Awards: Fellow of the IEEE He leads projects on acoustic sensor networks, automotive radar, and hearing technology, collaborating with industry partners. His lab develops algorithms for hearing aids, cochlear implants, and robust communication systems, with patents spanning noise reduction, echo control, and speech enhancement.