جست‌وجوی استاد و پژوهشگر

• Digital Controlling
• Management Accounting
• Business Analytics
• +۵ مورد دیگر

Prof. Dr. Imke Keimer is a Professor at the Lucerne University of Applied Sciences and Arts (Hochschule Luzern) in the Department of Economics within the Business School. She is based at the Campus Zug-Rotkreuz and works at the Institute for Financial Services Zug (IFZ) in the CC Controlling (IFZ CO) division. Her work focuses on the intersection of digital transformation and financial management, particularly in the field of controlling. Her educational background includes: 2016: Appointed Professor at Lucerne University of Applied Sciences and Arts 2013: Certificate in University Teaching, Lucerne University of Applied Sciences and Arts 2011: Financial Risk Manager (FRM), Global Association of Risk Professionals (GARP) 2005-2009: Dissertation (Dr. rer. oec.), University of Bern, Topic: "Accounting Policy, IT Investments and the Strategic Orientation of Cost Management - Empirical Work in the Field of Accounting" (summa cum laude) 1999-2005: Studied Business Mathematics (M.Sc.), Technical University of Dortmund (overall grade: very good) Prof. Keimer's research primarily centers on digital transformation in controlling, business analytics, and agile finance. She has developed frameworks for assessing digital maturity in controlling functions and has pioneered approaches to integrate agile methodologies into financial management. Her work bridges theoretical research with practical applications, often through collaborations with industry partners. She has established herself as a thought leader in how digital technologies are reshaping the controller role from traditional accounting functions toward strategic business partnership. Her recent publications reveal a clear trajectory from foundational research on digital controlling frameworks toward more specific applications in sustainability, agile methodologies, and educational technology. The articles show increasing focus on practical implementation challenges, with notable attention to sustainable business practices and the evolving role of controllers in complex organizational environments. Her work consistently emphasizes the human dimension of digital transformation - how skills, mindsets, and organizational culture must evolve alongside technological changes. Scientific awards and recognitions include: Publication Award 2021 in the "Teaching" category for "PLAYMOBIL pro im Controlling: Mit Plastikfiguren das zukünftige Controlling-Rollenprofil gestalten" Financial Risk Manager (FRM) certification from GARP Prof. Keimer has been actively involved in advising and research projects. She served as Program Director for the MSc International Financial Management program and has led several significant research initiatives including the Innosuisse Project "Digital Transformation in Controlling" (2017) and the "Business Analytics in Controlling" project (2019). Her grant portfolio demonstrates strong industry-academia collaboration, with projects often co-developed with financial institutions and corporate partners seeking to modernize their controlling functions. She has also been instrumental in developing continuing education programs that bridge academic research with professional practice. She is closely associated with the Institute for Financial Services Zug (IFZ), where she contributes to research and teaching in the CC Controlling division. Her work often involves cross-disciplinary collaboration, particularly with colleagues in information systems and organizational behavior. She has developed a distinctive approach to teaching and research that combines academic rigor with practical applicability, frequently using case studies and industry partnerships to ground theoretical concepts in real-world challenges.

• Computer Vision
• Machine Learning
• Multimedia Retrieval
• +۵ مورد دیگر

Michel Crucianu is a Professor at the Conservatoire national des arts et métiers (CNAM) in Paris, France, affiliated with the CEDRIC laboratory (Centre d'Études et de Recherche en Informatique et Communications). His research spans computer vision, machine learning, and multimedia information retrieval, with a focus on developing advanced techniques for image and video analysis. Crucianu's research interests include computer vision, deep learning, generative models, zero-shot learning, and cross-modal retrieval. His work often addresses fundamental challenges in representation learning, with applications ranging from fashion recognition to disaster monitoring. He has made significant contributions to GAN-based techniques, particularly in semantic editing and attribute control within latent spaces. His research combines theoretical insights with practical applications, demonstrating strong interdisciplinary connections between computer vision and machine learning. Analysis of his recent publications reveals a strong focus on generative models (particularly GANs), zero-shot learning, and compositional visual reasoning. His work shows an evolution from traditional image retrieval techniques toward more sophisticated deep learning approaches, with increasing emphasis on interpretability, multimodal representations, and efficient learning strategies. The breadth of his research spans theoretical advances in representation learning to practical applications in areas like flood detection and fashion recognition. While specific awards are not mentioned in the available information, Crucianu's extensive publication record in top-tier conferences and journals demonstrates significant recognition within the computer vision and machine learning communities. His consistent publication output over two decades reflects sustained research excellence and impact. Crucianu has collaborated extensively with researchers at CEDRIC and other institutions, particularly with colleagues like Hervé Le Borgne, Nicolas Audebert, and Marius Ferecatu. His work often involves interdisciplinary collaborations spanning computer vision, machine learning, and domain-specific applications. His research has been supported by various projects addressing multimedia indexing, content-based retrieval, and advanced learning techniques. As a member of the CEDRIC laboratory, Crucianu contributes to one of France's leading research centers in computer science and communications. The laboratory's research axes include complex data analysis, machine learning representations, data mining and statistics, and information decision systems, all areas where Crucianu has made substantial contributions through his research and collaborations.

Cedric du Mouzaمشاهده پروفایل

استاد

• Database Systems
• Data Mining
• Digital Humanities
• +۶ مورد دیگر

Cédric du Mouza is a full Professor within the CEDRIC Laboratory at the Conservatoire National des Arts et Métiers (CNAM) in Paris, France. His research activities lie at the intersection of database systems, data mining, and digital humanities, with a strong emphasis on social network analysis, knowledge base population, and spatial/temporal data management. Education details are not explicitly provided in the source material. His research interests can be summarized as follows: Database Technologies: scalable indexing, distributed repositories, spatial and temporal data structures. Data Mining & Machine Learning: community detection, influence maximization, recommender systems. Digital Humanities: prosopographic databases, historical social networks, credibility assessment of historical sources. Social Media Analytics: Twitter user profiling, early detection of popular accounts, reducing filter bubbles. Across more than two decades, Prof. du Mouza has produced a rich scholarly record that blends theoretical advances with practical applications. Recent publications (2020-2024) highlight a methodological shift toward end-to-end evaluation frameworks for knowledge base population, uncertainty modeling in historical corpora, and real-time influence maximization in advertising ecosystems. These works are published in premier venues such as SIGIR , VLDB Journal , WISE , CIKM , and leading French conferences (EGC, BDA). No specific scientific awards, funded projects, or doctoral student names are mentioned in the supplied text. Prof. du Mouza collaborates extensively within interdisciplinary teams involving historians, computer scientists, and statisticians, reflecting CEDRIC’s integrative research culture. While no dedicated laboratory or team name is provided, his continuous affiliation with CEDRIC/CNAM confirms an active and ongoing role in both research and graduate-level education.

Ofer Shayevitzمشاهده پروفایل

استاد

• Information Theory
• Statistical Inference
• Data Science
• +۳ مورد دیگر

Prof. Ofer Shayevitz is a faculty member at the School of Electrical Engineering , Tel Aviv University , holding the academic rank of Professor . He is affiliated with the Department of Systems and leads interdisciplinary research at the intersection of information theory , statistical inference , and data science . His research explores theoretical challenges in interactive communication , machine learning , and quantum information , with applications to communication complexity , graph analysis , and non-stationary environments . Notable work includes advances in high-dimensional regression , entropy estimation , and memory-constrained algorithms . The trends in his recent publications highlight information-theoretic bounds , statistical inference under constraints , and interactive protocols . His group has made significant contributions to quantum key distribution , planted graph detection , and guesswork analysis . Scientific awards include the Best Student Paper Award at ISIT 2020 . His research is supported by major grants from the Israel Science Foundation (ISF) , ERC Starting Grant , and Israel Innovation Authority . Prof. Shayevitz advises current PhD students Assaf Ben-Yishai , Uri Hadar , and Shahar Stein Ioushua , as well as M.Sc. students Inbar Pinsly and Oz Ben Hamo . Former advisees include faculty members at institutions like Kyushu University and University of British Columbia .

• Evolutionary Ecology
• Marine Biology
• Conservation Ecology
• +۴ مورد دیگر

Nils Anthes is a Tenured Lecturer at the Institute of Evolution and Ecology, Department of Biology, within the Faculty of Mathematics and Natural Sciences at the University of Tuebingen, Germany. His academic career spans over two decades, with research focusing on evolutionary ecology and animal behavior across diverse taxonomic groups. Dr. Anthes completed his Diploma in Landscape Ecology at the University of Münster in 2002 with research on butterfly ecology, followed by his dissertation in 2006 on mating conflicts in hermaphroditic sea slugs through a joint program between Münster and Tuebingen universities. Since 2006, he has been a continuous member of the Animal Evolutionary Ecology group at the University of Tuebingen. His research portfolio includes three major thematic areas: Marine visual ecology investigating fluorescence patterns in fish; Applied Conservation Ecology focusing on farmland birds and endangered butterflies; and Sexual selection in hermaphrodites examining mating systems in marine invertebrates. His work on animal personality and filial cannibalism in fish with paternal care represents another significant research stream. Current projects include marine visual ecology (fluorescence in fish) and applied conservation ecology, while previous work focused on traumatic mating mechanisms and polyandry dynamics. Analysis of his publication record reveals a clear evolution in research focus. Early work (2005-2015) centered on marine invertebrates and sexual selection mechanisms, while more recent publications (2018-2025) demonstrate a shift toward conservation biology, particularly bird population dynamics, statistical ecology, and habitat management. This trajectory reflects both changing ecological priorities and methodological advancements in ecological research. Dr. Anthes maintains active collaborations with numerous researchers including Nico Michiels, Pierre-Paul Bitton, Ulrike Harant, Matteo Santon, and others across multiple projects. His work appears across diverse platforms as evidenced by his profiles on Google Scholar, ResearchGate, ResearcherID, OrcID, and Publons. His laboratory work spans both field and experimental approaches, with research conducted in marine environments, forest ecosystems, and agricultural landscapes. The evolution of his research interests demonstrates adaptability to emerging ecological challenges while maintaining core expertise in evolutionary processes.

Kapil Ahujaمشاهده پروفایل

استاد

• Machine Learning
• Network Science
• Computational Optimization
• +۴ مورد دیگر

Prof. Kapil Ahuja is a Full Professor in the Department of Computer Science & Engineering at the Indian Institute of Technology Indore (IIT Indore), where he heads the Mathematics of Data Science and Simulation (MODSS) research lab. After completing dual Master's degrees and a Ph.D. from Virginia Tech (USA) followed by postdoctoral work at the Max Planck Institute in Germany, he has held visiting positions at UT Austin, IMT Atlantique, Sandia National Labs, TU Dresden, and TU Braunschweig. His administrative roles include founding Dean of International Affairs and former Head of Computer Science & Engineering at IIT Indore. Education: Ph.D. in Mathematics, Virginia Tech (2011) M.S. in Mathematics, Virginia Tech (2009) M.S. in Computer Science, Virginia Tech (2007) B.Tech. in Mechanical Engineering, IIT (BHU) Varanasi (2001) Research Focus: Prof. Ahuja's work bridges theoretical advances with real-world applications, emphasizing machine learning algorithms for plant/cancer studies, game-theoretic poverty reduction models, exascale climate modeling solvers, and drone trajectory optimization. His interdisciplinary approach integrates numerical linear algebra with network science to solve complex systems problems across healthcare, agriculture, and climate science, supported by 4.85 Crores INR in external funding. Publication Trends: Recent work demonstrates growing emphasis on AI-driven optimization for physical systems (drones, climate models) and biomedical applications (cancer classification). His publications increasingly feature cross-disciplinary collaborations between computer science, biology, and economics, with notable contributions in explainable AI for healthcare and resource allocation algorithms for social networks. Scientific Recognition: National Teacher's Award (2024) from the President of India Five-time recipient of IIT Indore's Best Teacher Award (2013-2023) Best Poster Award at International Workshop on Game Theory & Networks (2019) Steeneck Graduate Research Fellowship (Virginia Tech, 2011) Multiple SIAM travel awards for international conferences Mentorship & Service: Prof. Ahuja has graduated 5 Ph.D. and 4 M.S. (Research) students while mentoring 75 B.Tech. projects. He serves as Associate Editor for Applied Intelligence Journal (Springer Nature) and Knowledge and Information Systems, organizes international conferences, and reviews for 35+ academic sources. His administrative leadership significantly expanded IIT Indore's global partnerships through the Research Park initiative. Research Infrastructure: The MODSS lab maintains active collaborations with Oak Ridge National Lab, Sandia National Labs, and European institutions. Current projects include AI-optimized drone swarms for agricultural monitoring and game-theoretic models for poverty intervention, utilizing high-performance computing resources for large-scale simulations.

Alexander Eckerمشاهده پروفایل

استاد

• Machine Learning
• Computational Neuroscience
• Visual Perception
• +۳ مورد دیگر

Prof. Dr. Alexander Ecker is Professor of Data Science at the Institute of Computer Science, University of Göttingen, and concurrently holds the prestigious Max Planck Fellow position at the Max Planck Institute for Dynamics and Self-Organization. Since 2020 he also serves on the Executive Board of the Campus Institute Data Science in Göttingen. He leads the Neural Data Science research group, comprising 14 PhD students and 2 postdoctoral researchers, focusing on the interface of machine learning and computational neuroscience. His educational background includes a Dr. rer. nat. in Neuroscience (2014) from the Graduate School of Neural and Behavioral Sciences/IMPRS, University of Tübingen, followed by post-doctoral and group-leader positions at the University of Tübingen and the Max Planck Institute for Biological Cybernetics. Research Interests Machine Learning & Deep Learning: developing novel algorithms for representation learning and generative modeling. Computational Neuroscience: large-scale data-driven modeling of visual cortical circuits. Visual Perception: bridging biological vision and computer vision via biologically inspired architectures. His work has produced a steady stream of influential publications (2019-2025) in leading journals such as Nature Communications , Nature , Nature Methods , PLOS Computational Biology , ICLR , NeurIPS , and CVPR . The publications trend toward integrating high-resolution neural recordings with state-of-the-art machine-learning models to uncover principles of sensory processing, neuron-type classification, and behavior. Scientific Awards & Honors Max Planck Fellow, Max Planck Institute for Dynamics and Self-Organization (ongoing) Executive Board Member, Campus Institute Data Science, Göttingen (since 2020) Teaching, Advising & Grants Regularly teaches advanced courses: “Deep Learning for Image Synthesis”, “Current Topics in Deep Learning”, and “Graph Machine Learning”. Supervises 14 current PhD students and 2 postdocs within the Neural Data Science Group. Offers numerous Bachelor’s and Master’s thesis projects, with topics ranging from neuronal morphology clustering to primate vocalization analysis. Leads or co-leads large collaborative consortia with labs in Göttingen, Tübingen, Baylor College of Medicine, and other institutions across the US and Germany. Labs & Teams The Neural Data Science Group operates at the Institute of Computer Science, University of Göttingen, and is tightly integrated with the Max Planck Institute for Dynamics and Self-Organization. The group maintains active collaborations with over a dozen partner laboratories, including groups led by Fabian Sinz, Andreas Tolias, Thomas Euler, Tim Gollisch, and Viola Priesemann, fostering an interdisciplinary environment that spans computer science, physics, biology, and psychology.

Kurt Rothermelمشاهده پروفایل

استاد

• Distributed Systems
• Time-Sensitive Networking
• Networked Control Systems
• +۷ مورد دیگر

Prof. Kurt Rothermel is a faculty member at the University of Stuttgart, specifically affiliated with the Institute for Parallel and Distributed Systems (IPVS) within the Faculty of Computer Science, Electrical Engineering, and Information Technology. His research focuses on distributed systems with particular expertise in time-sensitive networking, networked control systems, and complex event processing. Prof. Rothermel's research interests span multiple areas in distributed computing and networking: Distributed Systems and Networked Control Systems Time-Sensitive Networking and Deterministic Networking Quality of Service (QoS) Management and Optimization Complex Event Processing and Stream Analytics Edge Computing and Real-Time Video Analytics Digital Twin Systems and Proximity-based Services His recent publications (2022-2025) demonstrate a strong focus on time-sensitive networking, with numerous papers addressing scheduling algorithms, conflict graph creation, and latency management for time-triggered communication. There's also significant work on load shedding techniques for resource-constrained environments, particularly for real-time video analytics at the edge. His research increasingly incorporates digital twin systems and explores novel approaches for distributed mobile simulation, as evidenced by his "Persival" framework for handling 3D meshes on AR devices. Prof. Rothermel has made substantial contributions to the field of complex event processing, developing multiple shedding strategies (gspice, hSPICE, pspice) to manage resource constraints while maintaining utility. His work bridges theoretical networking concepts with practical applications in industrial settings, particularly evident in his research on networked control systems and time-sensitive software-defined networks.

Andreas Harthمشاهده پروفایل

استاد

• Decentralized information systems
• World Wide Web technologies
• Blockchain environments
• +۷ مورد دیگر

Prof. Dr. Andreas Harth holds the Chair of Business Information Systems, especially Technical Information Systems, at Friedrich-Alexander University Erlangen-Nuremberg (FAU), where he has been a faculty member since 2018. He also serves as a department head at the Fraunhofer IIS-SCS in Nuremberg. His academic work spans both theoretical research and practical applications in decentralized information systems, with strong connections to industry through numerous collaborative projects. Harth completed an apprenticeship as a banker before studying computer science. He earned his doctorate from the Digital Enterprise Research Institute at the National University of Ireland, Galway, and completed his habilitation at the Karlsruhe Institute of Technology. His academic journey included teaching and research stays at the universities of Heidelberg, Innsbruck, Stanford, and Southern California, providing him with a global perspective on information systems research. His research focuses on developing methods and technologies for decentralized information systems found in the World Wide Web and blockchain environments, with applications in companies. He investigates data integration using Semantic Web and Linked Data technologies, process modeling languages, and their applications in the Internet of Things, Web of Things, and Industry 4.0 contexts. His work bridges theoretical computer science with practical business applications, particularly in data sovereignty and decentralized architectures. Analysis of his recent publications reveals a strong trend toward Solid protocol applications, knowledge graph technologies, and the integration of large language models with semantic web technologies. His research increasingly focuses on practical implementations in enterprise settings, healthcare data management, and manufacturing systems, demonstrating the real-world applicability of his theoretical work. As a member of FAU's research focus on Digitalization and Innovation, Harth collaborates with strategic partners including the Fraunhofer Institute for Information Systems (IIS) and major German industrial companies. His work contributes significantly to FAU's position as one of Germany's most research-intensive universities, particularly in the fields of business informatics and decentralized systems.

• Bioinformatics
• Computational Biology
• Phylogenetics
• +۳ مورد دیگر

Daniel Huson is a Professor of Algorithms in Bioinformatics at the University of Tübingen , affiliated with the Faculty of Science and actively contributing to the Computer Science Department . He has held this position since 2002 and previously served as Senior Staff Scientist at Celera Genomics (1999-2002) and Post-Doc at Princeton University and the University of Pennsylvania (1997-99). Education: PhD in Mathematics, Bielefeld University (1990, summa cum laude) Habilitation in Mathematics, Bielefeld University (1997) Studied Mathematics and Physics, University of Washington (1980-86) Research Interests: Designing algorithms for bioinformatics and computational biology Metagenomic data analysis using tools like MEGAN and SplitsTree Phylogenetic networks and evolutionary modeling Microbiome dynamics and industrial bioproduction optimization Development of interactive software for biological data visualization Exploring autocatalytic reaction networks in early biochemistry Scientific Contributions: Developed MEGAN, a widely used metagenome analysis tool Created SplitsTree for phylogenetic network analysis Published extensively on microbiome analysis, metagenomic binning, and evolutionary relationships Contributed to power-to-gas technology and bioelectrochemical systems Awards & Grants: Royal Society of New Zealand International Leader Fellowship (2020-22) PLOS Computational Biology Research Prize (2017) Technology Transfer Prize of the IHK Reutlingen (2016) Co-organizer of major conferences (GCB, RECOMB, ISMB, etc.) Leadership Roles: Head of Computer Science Department (2011-14) Founding member of Computomics (since 2012) Faculty member of IMPRS 'From Molecules to Organisms' (since 2011)

• Computer Graphics
• Interactive Environments
• Visualization
• +۴ مورد دیگر

Prof. Dr. André Hinkenjann is the Founding Director of the Institute for Visual Computing and holds a Research Professorship in Computer Graphics and Interactive Systems at Bonn-Rhein-Sieg University of Applied Sciences. His research spans computer graphics, interactive environments, and visualization, with applications in VR/AR, digital twins, and scientific data analysis. He leads multidisciplinary projects funded by institutions like BMBF and Zukunftsfonds NRW. His research integrates: Computer Graphics : Real-time global illumination, foveated rendering, and GPU optimization Interactive Systems : Haptic interfaces, large-display collaboration, and spatial interaction techniques Applied VR/AR : From trauma therapy to industrial training and cultural heritage preservation Recent publications emphasize mixed-reality interaction, neural rendering, and perceptual optimization, reflecting a consistent focus on bridging theoretical graphics with human-centered applications. His lab frequently contributes to high-impact venues like ACM SIGGRAPH, IEEE VR, and Eurographics. Notable projects under his direction include: PInBiM: Gamified citizen science for museum-based insect research DT4MP: Digital twins for urban/industrial multiphysics simulations GTN: State-wide network advancing game technology in NRW Witality: VR for sensory wine analysis

• Data Science
• Internet of Things (IoT)
• Visual Analytics
• +۱۰ مورد دیگر

Professor Dirk J. Lehmann is a Professor of Data Science in IoT at Ostfalia University of Applied Sciences, Faculty of Computer Science, where he has been employed since May 2022. He holds significant leadership roles including Deputy Head of the Institute for Information Engineering (since 2024), Research Officer of the Faculty of Computer Science (since 2023), and membership in multiple committees including the Admissions Committee for Digital Technologies and the Digital Technologies Examination Board. Professor Lehmann's extensive academic journey includes: Part-time professorship in Data Science in IoT at Ostfalia University (2020-2022) Senior Specialist for Digitalization, AI, and Visual Analysis at IAV GmbH (2018-2023) Assistant Professor of Visual Data Analysis at Nazarbayev University, Kazakhstan (2017) Visiting professorships at TU Graz, Austria and Universidad Rey Juan Carlos, Spain (2016-2017) Researcher at Otto-von-Guericke University Magdeburg (2009-2017) His research expertise centers on Visual Analytics and Data Science, with particular emphasis on high-dimensional data visualization, categorical data analysis, and IoT applications. Professor Lehmann leads the Data Science in IoT working group, conducting research across three main areas: visual data analysis, distributed data analysis using AI methods, and applied data analysis in geology, climate data, medicine, and industrial processes. His methodological contributions include innovative visualization techniques for complex datasets across multiple domains. Analysis of Professor Lehmann's 15 most recent publications (2017-2025) reveals a consistent focus on advancing visualization techniques for complex data analysis. His work spans categorical data visualization (CatNetVis), biological data analysis (D. Melanogaster research), optimization of star coordinate systems, and interactive exploration methods for large datasets. These publications appear in top venues including IEEE Transactions on Visualization and Computer Graphics and EuroVis, demonstrating both theoretical rigor and practical application across diverse domains from healthcare to environmental science. As an educator, Professor Lehmann teaches a comprehensive range of courses from foundational mathematics to advanced machine learning and visualization techniques. He actively supervises student projects and theses, emphasizing clear project definitions with measurable acceptance criteria. His international collaborations span institutions in Israel, Saudi Arabia, China, Austria, and Spain, reflecting a global research perspective that bridges academic theory with industry applications, particularly through his previous role at IAV GmbH, a Volkswagen subsidiary.

Johannes Pfauمشاهده پروفایل

پژوهشگر

• FPGA Architecture
• Reconfigurable Computing
• Digital Beamforming
• +۳ مورد دیگر

Dr. Johannes Pfau serves as a Scientific Assistant at the Institute for Information Processing Technology (ITIV) within Karlsruhe Institute of Technology (KIT), working in Prof. Becker's research group. His position combines postdoctoral research in advanced FPGA architectures with teaching responsibilities including System-on-Chip internships and academic advising for specialized engineering tracks. Education: Doctorate (Dissertation) in Electrical Engineering and Information Technology, Karlsruhe Institute of Technology, 2024 Research Interests: Pfau's work centers on reconfigurable computing with three interconnected pillars: (1) Next-generation FPGA architectures using emerging technologies like RFETs that require fundamental toolchain redesigns; (2) Digital beamforming systems for satellite Earth observation that replace analog processing with FPGA-based solutions to enable on-orbit data compression; and (3) High-throughput data acquisition systems for 6G prototyping handling multi-100Gbps streams through RFSoC platforms. His research bridges semiconductor physics, hardware architecture, and practical applications in communications and remote sensing. Publication Trends: Pfau's 15 most recent publications (2021-2024) reveal a strong focus on practical FPGA implementations addressing real-world constraints. His work increasingly integrates power management (7 papers), 6G infrastructure (5 papers), and novel semiconductor technologies (4 papers), with a clear trajectory toward hardware solutions for satellite communications and next-generation wireless systems. The research demonstrates consistent progression from architectural innovations (RFET, V-FPGAs) to applied systems (beamforming, 6G testbeds). Advising and Mentorship: Pfau maintains an active student supervision portfolio with documented guidance of 7+ Bachelor's and Master's theses since 2021. His projects emphasize hands-on hardware development, spanning power management techniques, beamforming filter design, and prosthetic control systems. The academic advising role for specializations 13 and 21 positions him at the intersection of computer science and electrical engineering education. Research Context: As a core member of Prof. Becker's group at ITIV, Pfau contributes to KIT's leadership in reconfigurable systems research. The group maintains strong industry connections through 6G initiatives and satellite technology development, with Pfau's work directly supporting German and European efforts in secure communications infrastructure and Earth observation systems.

• Climate Modeling
• Hydrological Cycle
• Land-Atmosphere Interactions
• +۶ مورد دیگر

Lai-yung Ruby Leung is a Battelle Fellow at Pacific Northwest National Laboratory (PNNL) working in Earth Systems Analysis & Modeling. She serves as Chief Scientist of the Energy Exascale Earth System Model (E3SM) supported by the U.S. Department of Energy, leading major efforts to develop state-of-the-art capabilities for modeling human-Earth system processes on high-performance computers. Dr. Leung's research broadly spans climate and hydrological cycle modeling with expertise in land-atmosphere interactions, orographic processes, monsoon climate, and climate extremes. Dr. Leung earned her educational credentials from prestigious institutions: Ph.D., Atmospheric Science, Texas A&M University M.S., Atmospheric Science, Texas A&M University B.S. (Honors), Physics & Statistics, Chinese University of Hong Kong Her research interests focus on regional and global climate modeling , land-atmosphere interactions , and the regional hydrologic cycle . She investigates orographic precipitation mechanisms, climate extremes, climate variability and change, and aerosol-cloud interactions. Her work integrates advanced modeling techniques with observational data to understand complex Earth system processes, with research featured in Science , Popular Science , Wall Street Journal , and National Public Radio . Dr. Leung has published over 500 peer-reviewed papers and serves as an editor for the American Meteorological Society's Journal of Hydrometeorology . Analysis of Dr. Leung's recent publications reveals her leadership in developing and applying the Energy Exascale Earth System Model (E3SM), with significant contributions to understanding mesoscale convective systems, soil moisture dynamics, urban hydrology, and climate extremes. Her work demonstrates increasing integration of machine learning techniques with traditional climate modeling approaches, particularly in model evaluation frameworks and high-resolution simulations. She maintains strong focus on practical applications of climate science for understanding water resources, extreme weather events, and climate change impacts. Dr. Leung's scientific recognition includes: Election to the National Academy of Engineering (NAE) Election to the Washington State Academy of Sciences (WSAS) Fellow of the American Geophysical Union (AGU) Fellow of the American Meteorological Society (AMS) Fellow of the American Association for the Advancement of Science (AAAS) AMS Hydrologic Sciences Medal (2022) U.S. Department of Energy Office of Science Distinguished Scientist Fellow (2021) Reuter's Hot List of top 1,000 most influential climate scientists (2021) AGU Jacob Bjerknes Lecture (2020) AGU Bert Bolin Global Environmental Change Award (2019) As Chief Scientist of E3SM, Dr. Leung leads major research initiatives funded by the Department of Energy and has organized key workshops sponsored by DOE, NSF, NOAA, and NASA. She has served on numerous advisory panels and National Academies committees that define future priorities in Digital Twin, AI/ML, climate modeling, hydroclimate, and water cycle research. Her professional service includes membership on the Board on Atmospheric Sciences and Climate of the National Academies, council membership with the American Meteorological Society, and editorial roles for prominent journals. Dr. Leung directs research within PNNL's Earth Systems Analysis & Modeling group, collaborating with national and international climate research teams. She leads efforts to advance the Energy Exascale Earth System Model (E3SM), which represents cutting-edge capabilities in modeling human-Earth system processes. Her work connects with multiple PNNL research areas including atmospheric science, global change, and coastal science, contributing to the laboratory's mission of addressing complex environmental challenges through scientific innovation.

• Cognitive Neuroscience
• Computational Modeling
• Predictive Coding
• +۴ مورد دیگر

Ryszard Auksztulewicz serves as Visiting Professor and Group Leader of the Prediction and Memory unit within the Department of Education and Psychology at Freie Universität Berlin. Based at Habelschwerdter Allee 45, Room JK 25/220 in Berlin, he leads research at the intersection of cognitive and systems neuroscience. His research focuses on neural mechanisms of prediction, attention, and memory , employing multimodal approaches including EEG, fMRI, MEG, ECoG, TMS, electrophysiology, and calcium imaging. He integrates these with computational modeling (Bayesian models of neural/behavioral data) and multivariate decoding techniques. Key research themes include: Predictive coding frameworks for understanding sensory processing Neural gain modulation in temporal expectation Auditory cortex representations of memory and prediction Task-dependent modulation of sensory predictions Recurrent processing in perceptual awareness Analysis of his 34 publications (h-index: 18) reveals consistent focus on computational neuroscience approaches to memory systems , with recent work emphasizing cross-species auditory processing and neural representation dynamics. His 2022-2018 publications particularly highlight mechanistic distinctions between "what" and "when" predictions in sensory cortex. Teaching responsibilities include: Probabilistic and Statistical Modeling (Lecture) Introduction to Programming (Seminar) Research Colloquium for Qualification Theses