جست‌وجوی استاد و پژوهشگر

• Deep Learning
• Federated Learning
• Data Analysis on Graphs
• +۹ مورد دیگر

Vinay Chakravarthi Gogineni is an Assistant Professor at The Maersk Mc-Kinney Moller Institute , University of Southern Denmark (SDU), specializing in SDU Applied AI and Data Science . His research integrates advanced AI techniques into healthcare, industrial IoT, and fusion energy systems, with a strong focus on federated learning, privacy-preserving AI, and ethical machine learning practices. Education: Ph.D. in Distributed Machine Learning, Indian Institute of Technology Kharagpur (Aug 2019) Research Interests: Dr. Gogineni's work spans Deep Learning , Federated Learning , and Graph Data Analysis . He develops personalized AI models for healthcare applications, including cervical cancer screening, colorectal cancer detection, and dementia prediction. His innovations in Machine Unlearning ensure compliance with GDPR and the EU AI Act, while his work on Physics-Informed Neural Networks enhances predictive accuracy in fusion energy systems. Key areas include self-supervised learning, continual learning, and fairness-aware AI. Scientific Awards: HC Ørsted Research Talent Award (2024, Denmark) ERCIM Alain Bensoussan Fellowship (2019) Best Paper Award , APSIPA ASC-2021, Tokyo Professional Roles: IEEE Senior Member and editorial board member of IEEE Sensors Journal . He teaches courses on AI for Healthcare Data and Calculus and Linear Algebra , fostering interdisciplinary education in applied AI.

Vijay Narayananمشاهده پروفایل

استاد

• Power Aware Computing
• Computer Architecture
• Embedded Systems
• +۴ مورد دیگر

Vijay Narayanan is the Robert Noll Chair Professor in Computer Science & Engineering and Electrical Engineering at Pennsylvania State University. He co-directs the Microsystems Design Lab and leads research in embedded visual analytics, self-powered processors, and system design using emerging devices. Education: B.E in Computer Science and Engineering (1993) from University of Madras, India Ph.D. in Computer Science and Engineering (1998) from University of South Florida, USA His research spans Power Aware Computing , Computer Architecture , and Embedded Systems , with emphasis on Visual Cortex on Silicon and Self-Powered Processors . Current work includes Non-Volatile Processors and Design Automation under unreliable power conditions via NSF ERC ASSIST. Recent publications focus on GPU architecture (Tensor Cores, ACE), Memory Consistency Verification (QED), and Neural Radiance Fields (Disorf, Distwar) for robotics and rendering. Key collaborations include Tsinghua University and DARPA/SRC LEAST Center . Scientific Awards: IEEE Fellow ACM Fellow He leads the Architecture, Benchmarking and Circuits Thrust in the DARPA/SRC LEAST Center and contributes to NSF ERC ASSIST for self-powered systems. Grants and projects emphasize cross-layer optimizations and hardware-software co-design.

Bruce Draperمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Visual Learning
• +۷ مورد دیگر

Bruce Draper is a Professor and Chair of the Department of Computer Science in the College of Natural Sciences at Colorado State University. His work bridges artificial intelligence, machine learning, and computer vision, with a strong emphasis on real-world applications involving visual data and intelligent systems. Research Interests: Draper's research centers on machine learning with a focus on visual learning, adversarial AI, and visual agents. He investigates how AI systems can perceive, interpret, and interact with visual environments through technologies like facial recognition, object tracking, augmented reality, and automated visual communication. His work addresses both the capabilities and vulnerabilities of modern AI, particularly in defending systems against adversarial attacks. Publication Trends: His recent scholarly output reflects a consistent trajectory in advancing computer vision and AI robustness. The articles span topics from adversarial defense mechanisms and visual agent autonomy to scalable learning frameworks and real-time video analysis. Collectively, they emphasize secure, efficient, and context-aware visual intelligence systems grounded in deep learning and representation learning. Scientific Awards: No specific awards mentioned in the provided text. Advising and Grants: While no students or grants are explicitly listed, his leadership role as department chair and prior experience as a DARPA program manager suggest extensive involvement in research funding, mentorship, and high-impact project direction. His background indicates likely supervision of graduate students and management of federally funded research initiatives in AI and computer vision. Labs and Teams: Although no specific lab or research group is named, his research scope implies leadership or affiliation with interdisciplinary teams working on AI security, computer vision, and augmented reality systems within the Department of Computer Science at CSU.

• Cloud Computing
• Cybersecurity
• Software Engineering
• +۶ مورد دیگر

Kyrre Begnum is an Associate Professor at OsloMet – Oslo Metropolitan University, affiliated with the Faculty of Technology, Art and Design through the Department of Computer Science. His research spans cloud computing, cybersecurity, and software engineering, with a focus on virtual machine management, intrusion detection, and autonomic systems. Cloud Computing Cybersecurity Software Engineering Machine Learning Data Center Management His recent publications emphasize adversarial machine learning in security, affinity-aware scheduling for cloud efficiency, and network optimization in virtualized environments. While no formal awards are documented in the provided texts, his work demonstrates technical innovation in distributed systems and educational applications of virtualization technology.

• 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.

Phil Husbandsمشاهده پروفایل

استاد پژوهشی

• Artificial Intelligence
• Evolutionary Robotics
• Artificial Life
• +۱۲ مورد دیگر

Phil Husbands is a Research Professor of Artificial Intelligence in the Department of Informatics at the University of Sussex, School of Engineering and Informatics. He is a co-founder of the Centre for Computational Neuroscience and Robotics (CCNR) and a founding member of the Evolutionary and Adaptive Systems group (now the AI Group), reflecting his deep interdisciplinary engagement across engineering, neuroscience, and computer science. His research interests span artificial life, evolutionary robotics, biologically inspired neural networks, computational neuroscience, machine learning, and the history and philosophy of AI. He has pioneered work on GasNets (neural networks with diffusible modulators), insect-inspired navigation, and bio-inspired optimization algorithms. His work often bridges theory and application, integrating insights from biology into adaptive robotic systems. The recent publications highlight a strong trajectory in bio-inspired computation, including evolutionary swarm algorithms, neuromodulation models, cognitive architectures, and tactile robotics. His work increasingly explores foundational questions in agency, time perception, and autonomy, suggesting a move toward more comprehensive models of artificial cognition. Scientific Awards: No specific awards listed in the provided text. Phil Husbands has been involved in numerous research grants from major funders including the European Union, EPSRC, BBSRC, and Wellcome Trust. He has supervised multiple doctoral students and collaborated widely, notably with Andy Philippides and Michael O’Shea. He is actively engaged in postgraduate research supervision and public dissemination of science through books like Robots: What Everyone Needs to Know . He leads and contributes to interdisciplinary research teams such as the Evolutionary and Adaptive Systems group and the Centre for Computational Neuroscience and Robotics, fostering collaboration between computer scientists, neuroscientists, and roboticists.

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.

Kay Römerمشاهده پروفایل

استاد

• Wireless Sensor Networks
• Internet of Things
• Cyber-Physical Systems
• +۷ مورد دیگر

Kay Römer is Professor and Director of the Institute for Technical Informatics at Graz University of Technology, Austria. He previously held a professorship at the University of Lübeck, Germany, and was a senior researcher at ETH Zurich, where he obtained his doctorate in computer science in 2005. His research focuses on networked embedded systems, particularly in the domains of Wireless Sensor Networks, Internet of Things (IoT), and Cyber-Physical Systems. Key areas include wireless networking, operating systems, programming models, dependability, and deployment methodologies for large-scale embedded networks. The most recent publications reflect a consistent trend in dependable and scalable IoT systems, with emphasis on wireless protocols, fault tolerance, real-time operation, and system robustness. His work bridges theoretical innovation with practical deployment, often involving cross-layer design and energy efficiency. Associate Editor, IEEE Transactions on Mobile Computing Associate Editor, IEEE Transactions on Computers Program Co-Chair, ACM SenSys 2011 Program Co-Chair, ACM/IEEE IPSN 2013 Coordinator, EU FP7 FIRE Project RELYonIT Coordinator, TU Graz Excellence Project "Dependable Internet of Things" Kay Römer has advised multiple research teams and coordinated major international projects funded by the Swiss National Science Foundation, European Commission, German Science Foundation, German Federal Ministry of Education and Research, and the Austrian Federal Ministry of Science, Research, and Economy. He has delivered invited keynote talks and co-directed five international summer schools, demonstrating strong leadership in research dissemination and mentoring. He leads research activities within the Institute for Technical Informatics at TU Graz, focusing on dependable and secure IoT systems, with active collaborations across Europe and industry partners.

• Recommender Systems
• User Modeling
• Personalization
• +۱۱ مورد دیگر

Alan Said is an Associate Professor in the Department of Applied IT at the University of Gothenburg, where he also serves as Head of Education. His research focuses on recommender systems, user modeling, personalization, and the human-centered evaluation of AI technologies. He is actively engaged in promoting responsible, fair, and sustainable AI through interdisciplinary research and community leadership. Research Interests: User Modeling and Personalization Recommender Systems (RecSys) Human-Computer Interaction (HCI) and Human-Centered AI (HCAI) Explainable and Responsible AI Green AI and Environmental Impact of Recommender Systems Reproducibility and Evaluation Methodologies His recent publications reflect a strong trend toward ethical, sustainable, and socially conscious AI, with a focus on fairness in healthcare, environmental cost measurement, and interdisciplinary approaches to recommendation. He contributes extensively to top-tier venues such as ACM RecSys and UMAP. Scientific Awards: ACM Distinguished Speaker Advising and Grants: Alan Said advises students on topics ranging from fairness in AI to human-centered explanations and sustainable recommender systems. He has been involved in organizing workshops and special issues that promote critical reflection and interdisciplinary collaboration in the RecSys community. He has participated in funded research projects, including international collaborations supported by agencies like Vinnova. Labs and Teams: He is a key organizer of the Human-centered AI (HCAI) podcast and has co-organized influential workshops such as BEYOND and INTROSPECTIVES at RecSys, fostering dialogue on the societal and ethical dimensions of AI. His work bridges computer science, psychology, design, and ethics, emphasizing collaboration across disciplines.

Daniel Posslerمشاهده پروفایل

پژوهشگر

• Media Psychology
• Entertainment Research
• Digital Media Effects (Video Games, Social Media)
• +۲ مورد دیگر

Dr. Daniel Possler is a research assistant at the Department of Journalism and Communication Research at Leibniz University Hannover . He previously held a research assistant position at the Department of Media and Business Communication at Julius-Maximilians-Universität Würzburg (2022-2024). His work focuses on media psychology , entertainment research , and the use and effects of digital media , particularly video games and social media. He also explores environmental communication and computational methods in social research. PhD (2020): "Fascinating Entertainment: The Elicitation and Entertaining Quality of the Emotion Awe in Media Reception Using Video Games as a Case Example" Research Interests include player-avatar relationships, emotional responses to media (especially awe), pro-environmental messaging through documentaries, and computational approaches to analyzing communication data. His recent publications examine dual-process entertainment models, eudaimonic gaming motives, and automated content analysis tools. Professional Activities involve: Young Members’ Representative of the Media Psychology Division (DGPs, since 2023) Co-organiser of the DGPuK 2022 conference Member of the editorial board of Media Psychology (since 2022) Lecturer for the Media Entrepreneurship program (since 2013) Membership in ICA, ECREA, DGPuK, and DGPs

Veselin Rakocevicمشاهده پروفایل

مدرس ارشد

• Multihop Wireless Networks
• Vehicular Networks
• Ad-hoc Networking
• +۵ مورد دیگر

Dr. Veselin Rakocevic is a Senior Lecturer in Electronic Engineering at City, University of London (post-merger with St George's). He holds a PhD in Telecommunications from Queen Mary, University of London (2002) and a Dipl-Ing in Electrical & Electronic Engineering from the University of Belgrade (1998). At City, he has served as Head of Department (2015-2018) and Associate Dean for Portfolio Development (since 2023). PhD: Telecommunications, Queen Mary, University of London (2002) Dipl-Ing: Electrical & Electronic Engineering, University of Belgrade (1998) His research focuses on multihop wireless networks , including optimal scheduling , dynamic clustering , cooperation mechanisms , and network self-organisation . Applications span vehicular networks , heterogeneous cellular networks , and energy network optimisation . Over 15 years, his publications demonstrate expertise in 5G/LTE-A protocols , VANET security , and transport-layer multipath tunneling . Recent article trends highlight 5G congestion control , machine learning for traffic identification , and game theory applications in rural wireless networks . He has supervised 12 PhD students with topics covering network optimization , QoS management , and mobile security . Major projects include REACH (EU-India rural broadband) and Wireless Networks for Moving Objects (EU COST Action).

Daniel Sonntagمشاهده پروفایل

استاد

• Intelligent User Interfaces
• Natural Language Processing
• Information Retrieval
• +۷ مورد دیگر

Prof. Dr.-Ing. Daniel Sonntag serves as Scientific Director of Interactive Machine Learning at the German Research Center for Artificial Intelligence (DFKI) and holds an Endowed Chair of Applied Artificial Intelligence at University of Oldenburg. He leads multiple significant research initiatives including No-IDLE (Interactive Deep Learning Enterprise), MASTER (Mixed reality ecosystem for teaching robotics), Ophthalmo-AI (intelligent eye care diagnostics), pAItient (AI applications in medicine), and XAINES (explainable AI with narratives). His research spans intelligent user interfaces, natural language processing, information retrieval, dialogue systems, common-sense modeling, and explainable machine learning methods for cognitive computing. His work particularly focuses on multimodal-multisensor interfaces for medical and health systems and Industry 4.0 applications. His research has had industrial impact, with SmartWeb's multimodal interaction design forming the technical infrastructure for Apple's Siri. His recent publications demonstrate strong trends in medical AI applications, particularly in eye tracking, radiology report generation with enhanced interpretability, and interactive federated learning frameworks. His work consistently bridges theoretical AI advances with practical medical and industrial applications. German High Tech Champion Award (2011) As a principal investigator, Sonntag leads national and European projects funded by the Federal Ministry of Education and Research, Federal Ministry for Economic Affairs and Energy, Federal Ministry of Health, and Horizon Europe. He has supervised numerous researchers including Michael Barz, Jan Zacharias, and Alexander Prange across multiple projects. His research group has successfully transitioned technologies to industry, with former researchers joining companies like Apple, Wacom, and Semvox. Sonntag's laboratory work centers on the Interactive Machine Learning department at DFKI, with strong connections to medical institutions and industry partners. His team develops cognitive assistance systems, mixed reality applications for smart factories, and AI-based diagnostic support tools, particularly in ophthalmology and radiology.

Axel Küpperمشاهده پروفایل

استاد

• Decentralized Systems
• Blockchain Technology
• Distributed Ledgers
• +۷ مورد دیگر

Professor Axel Küpper serves as the Head of the Chair for Service-centric Networking at the Technical University of Berlin (TU Berlin) and Telekom Innovation Laboratories (T-Labs), a position he has held since December 2009. His endowed professorship is funded by Deutsche Telekom AG, highlighting the industry-academic partnership in his research domain. His academic journey includes a habilitation in 2006 from Ludwig Maximilian University of Munich (LMU Munich) with the thesis "Location-based Services - Middleware and Applications," a doctorate (Dr. rer. nat.) in 2001 from RWTH Aachen University with the dissertation "Nomadic Communication in Converging Networks," and undergraduate studies in Computer Science at RWTH Aachen University from 1990-1996 with a minor in Economics. Prof. Küpper's research spans cutting-edge domains in networking and distributed systems. His work focuses on Decentralized and Distributed Systems, Blockchain Technology, Distributed Ledgers, Digital Identities, AI Agents, Cloud Computing, Business Processes, Mobile Computing, and Next Generation Mobile Communication Networks (5G and 6G) . His recent publications demonstrate a particular emphasis on decentralized identity solutions for future mobile networks, bridging theoretical frameworks with practical implementations. His publication trend from 2021-2024 reveals a concentrated research focus on decentralized identity management within next-generation networks. Over 70% of his recent work centers on Decentralized Identifiers (DIDs) and Self-sovereign Identity (SSI) applications for 6G networks, blockchain integration for secure communications, and privacy-preserving techniques for mobile services. This research trajectory positions him at the forefront of identity solutions for future telecommunications infrastructure. Prof. Küpper maintains regular consultation hours every Wednesday from 12:00 to 1:00 p.m. at TU Berlin, demonstrating his commitment to academic engagement. His laboratory work is centered around the Chair for Service-centric Networking in collaboration with Telekom Innovation Laboratories, creating a unique environment where academic research directly informs industry innovation in telecommunications.

Ye Hongمشاهده پروفایل

پژوهشگر

• Geoinformatics
• Urban Planning
• Transportation Engineering
• +۳ مورد دیگر

Dr. Ye Hong is a Researcher affiliated with the Institute of Cartography and Geoinformatics at ETH Zürich, specifically within the Department of Geoinformation Engineering. Their work focuses on geospatial data analysis, urban mobility modeling, machine learning applications in transportation systems, and sustainable urban planning. They contribute to open-source tools like Trackintel for mobility analysis and have published extensively on topics such as mobility data synthesis, traffic prediction, and privacy-preserving techniques. Research interests emphasize integrating multi-source geospatial data with deep learning to address challenges in urban sustainability, poverty reduction, and transportation efficiency. Their articles highlight innovations in trajectory generation, uncertainty quantification, and contextual-aware neural networks for spatial-temporal prediction. Dr. Hong’s publications explore the interplay between urban infrastructure, human behavior, and environmental impact. Key themes include accessibility measurement in cities, carbon footprint analysis, and the ethical implications of tracking mobility data. Their work bridges theoretical advancements in geoinformatics with practical applications in urban policy-making and infrastructure design. Notable contributions include frameworks for causal intervention in mobility data, methods to estimate poverty reduction efficiency using remote sensing, and analyses of tracking duration effects on location privacy. These efforts demonstrate a commitment to leveraging geospatial technologies for socially impactful research.

Anna Lackingerمشاهده پروفایل

پژوهشگر

• Edge Intelligence
• Distributed Inference
• Federated Learning
• +۵ مورد دیگر

Anna Lackinger is a PreDoc Researcher and PhD student in the Distributed Systems Group at TU Wien's Faculty of Informatics. Holding a Diplom-Ingenieur (Master's) and BSc from TU Wien, she specializes in edge intelligence and distributed machine learning systems with applications in cloud infrastructure optimization. Education: Diplom-Ingenieur (Master of Science in Engineering), TU Wien, 2023 Bachelor of Science, TU Wien Her research centers on Edge Intelligence and Federated Learning architectures, developing communication-efficient orchestration frameworks for hierarchical learning systems. She pioneers time series prediction techniques for cloud workload forecasting, enabling proactive resource scaling while addressing challenges in heterogeneous IoT environments and communication-constrained distributed systems. Analysis of her publication record reveals consistent focus on adaptive federated learning systems that optimize communication costs while maintaining model accuracy. Her work bridges theoretical AI advancements with practical distributed systems implementations, particularly in edge-cloud continuum scenarios where resource constraints demand innovative orchestration solutions. Professional Service: Reviewer for IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS) Reviewer for International Web Information Systems Engineering Conference (WISE) Reviewer for ACM Transactions on Internet Technology (TOIT) Reviewer for IEEE Transactions on Services Computing (TSC) Reviewer for IEEE Internet Computing (IC) Reviewer for IEEE Open Journal of the Computer Society (OJCS) Anna co-advises bachelor's and master's theses in distributed systems and machine learning. She actively contributes to major research initiatives including AloTwin (2023–2025) on reactive federated learning orchestration, INTEND (2024–2026) developing adaptive inference agents, and TEADAL (2022–2025) focused on load-aware federated learning systems. As a core member of TU Wien's Distributed Systems Group, she collaborates on cutting-edge research in edge intelligence and IoT swarm coordination, advancing the RainCloud framework for decentralized heterogeneous IoT systems while contributing to the group's leadership in distributed AI infrastructure.