جستجوی استاد راهنمای دانشگاهی

Nils Ole Tippenhauerمشاهده پروفایل

استاد

• Cyber-Physical System Security
• Industrial Control Systems Security
• Physical-Layer Security
• +۷ مورد دیگر

Nils Ole Tippenhauer is a faculty member at the CISPA Helmholtz Center for Information Security , leading the SCy-Phy research group . His academic journey includes an Assistant Professorship at the Singapore University of Technology and Design (SUTD) (2014–2018), a PhD in Computer Science from ETH Zurich (2012), and a Diploma in Computer Engineering from Hamburg University of Technology (2007). He also holds an exchange study year at the University of Waterloo (2004–2005) supported by a DAAD scholarship. Research Interests focus on practical systems security , particularly Security of Cyber-Physical Systems Industrial Control Systems (ICS) and Industrial IoT (IIoT) Physical-Layer Security and Wireless Security Privacy-Preserving Technologies (e.g., DP3T project) Embedded Systems Security Threat Detection & Defenses His work integrates both theoretical and applied approaches, addressing vulnerabilities in critical infrastructures like water systems and power grids. Recent Publications highlight trends in Anomaly detection evasion in industrial networks Microarchitectural side-channel defenses Localization techniques in cellular networks Security analysis of ICS protocols Data-driven vulnerability assessments for robotics Cyber-physical simulation for infrastructure security Scientific Awards include Best paper at DIMVA'23 Distinguished Paper at ACSAC 2022 Best paper at CPSIOTSEC 2022 Best paper at CPSS 2017 K-H Ditze Award for diploma thesis (2007) SG Mark Institution award (2016) Runner-Up at smart energy hackathon (2013) Advising and Grants feature PhD defenses of Daniele Antonioli and Hamid Reza Ghaeini (2022), co-organizing conferences like WiSec and CPSS , and leading projects such as the National Science Experiment (NSE) with 50,000 mobile sensors in Singapore. He also contributed to the DP3T project for privacy-preserving contact tracing. Labs and Teams include the SCy-Phy research group at CISPA and the development of testbeds like SWaT (Secure Water Treatment), WADI (Water Distribution), and EPIC (Electric Power and Industrial Control) at SUTD. These platforms enable hands-on security research in realistic industrial environments.

• Computer Networks
• Cybersecurity
• Distributed Systems
• +۴ مورد دیگر

Saurabh Bagchi is a Professor at Purdue University, West Lafayette, USA. He holds a PhD in Computer Science from the University of Illinois Urbana-Champaign (2001). His research focuses on distributed systems security, networking, and embedded systems. Key areas include IoT security, cyber-physical systems resilience, and machine learning applications in edge computing. Bagchi's work spans theoretical and applied domains, addressing challenges in distributed algorithms, fault tolerance, and secure communication protocols. His contributions to firmware analysis, serverless computing optimization, and anomaly detection in industrial IoT systems have been widely recognized. He has published over 300 papers in top-tier conferences and journals such as IEEE Transactions on Dependable and Secure Computing, ACM Transactions on Sensor Networks, and CVPR. He collaborates with researchers in academia and industry to advance resilient networked systems, including projects funded by NSF and industrial partnerships. His lab explores cutting-edge topics like federated learning security, edge computing architectures, and game-theoretic approaches to cyber defense.

Wolfgang Nejdlمشاهده پروفایل

استاد

• Web Science
• Information Retrieval
• Artificial Intelligence
• +۴ مورد دیگر

Prof. Dr. Wolfgang Nejdl is a Professor at the Institute for Data Science within the Faculty of Electrical Engineering and Computer Science at Leibniz University Hannover. He serves as Executive Director of the L3S Research Centre and Leibniz Forschungszentrum Inclusive Citizenship. Web Science Information Retrieval Artificial Intelligence Deep Learning His recent research focuses on AI applications in medicine , multimodal data fusion , and ethical AI systems . Projects include CAIMed (AI in Causal Medicine) and DAISEC (AI & Cybersecurity). His publications span conferences like AAMAS, WWW, and SIGIR. Notable awards include membership in the National Academy of Science and Engineering (acatech) . Former students hold positions at institutions like Stanford, TU Dresden, and ETH Zürich. Current projects involve climate resilience AI , federated learning for healthcare , and quantum-inspired data science .

Janick Edingerمشاهده پروفایل

استاد

• Distributed Operating Systems
• Edge Computing
• Computation Offloading
• +۷ مورد دیگر

Prof. Dr. Janick Edinger is a Professor of Distributed Operating Systems at the Department of Informatics, Faculty of Mathematics, Informatics and Natural Sciences, University of Hamburg, Germany. He leads a research group focused on distributed, context-aware, and adaptive computing systems, with a strong emphasis on edge computing, computation offloading, and assistive technologies. Education: PhD in Computer Science, University of Mannheim Studies at National Taiwan University Studies at University of Alberta, Canada Research stays at University of British Columbia, Hong Kong Polytechnic University, and Georgia State University, USA His research explores how edge computing and computation offloading can enable efficient, privacy-preserving processing of sensor and video data close to their sources, particularly in dynamic environments. He investigates the integration of autonomous and heterogeneous systems—such as drone fleets and mobile devices—into scalable middleware platforms for real-time monitoring and decision-making in logistics and industrial operations. His work also emphasizes societal impact, contributing to accessible routing, adaptive interfaces, and crowd-sourced mapping. The recent publications reflect a strong trend in edge computing, federated learning, privacy-preserving analytics, and assistive technologies. Topics include WebAssembly-based offloading, emotion prediction via eye tracking, real-time traffic detection, and predictive maintenance in Industry 4.0, showcasing a blend of foundational systems research and applied human-centered computing. Scientific Awards: PerCom 2021 Mark Weiser Best Paper Award Best Paper Award at IEEE PerCom 2021 for 'Voltaire: Precise Energy-Aware Code Offloading Decisions with Machine Learning' Prof. Edinger actively advises students and leads research projects involving grants and collaborations. His team includes PhD candidates and researchers working on middleware, edge systems, and context-aware applications. He has served on conference program committees, such as shadow PC member for EuroSys 2021, and publishes in top venues including IPDPS, PerCom, CHIIR, and COMPSAC. Labs and Teams: He leads the Distributed Operating Systems research group at the University of Hamburg, where he mentors students and collaborates on projects involving edge computing, IoT, and adaptive systems.

Diogo Carbonera Luvizonمشاهده پروفایل

پژوهشگر

• Computer Vision
• Computer Graphics
• Deep Learning
• +۴ مورد دیگر

Diogo Carbonera Luvizon is a Researcher at the Max-Planck-Institut für Informatik (MPI-INF) in Saarbrücken, Germany, and a member of the Visual Computing and Artificial Intelligence (VIA) Research Center. He holds a PhD in Computer Vision and Machine Learning from CY Cergy Paris University (2019), and Bachelor's and Master's degrees in Engineering and Applied Computing from UTFPR, Brazil. His research focuses on solving complex problems in Computer Vision, Computer Graphics, and Deep Learning, particularly in human modeling and real-time systems. Education: PhD (2019) - CY Cergy Paris University; M.Sc. (2015) - UTFPR; B.Sc. (2011) - UTFPR. Research interests include 3D human pose estimation, action recognition, multitask learning, and novel view synthesis. He has contributed to patents on multiplane image generation (Samsung) and vehicle speed measurement systems. His work has been recognized with awards like the Best Paper Honorable Mention at GCPR-VMV 2022 and Best Presentation Award at ETIS Lab (2018). He has developed open-source tools, including the deephar repository for human action recognition and pose estimation. His current affiliations include MPI-INF and the VIA Research Center, a partnership between MPI-INF and Google.

• Artificial Intelligence
• Human-Computer Interaction
• Computer Vision
• +۴ مورد دیگر

Prof. Dr. Julius Schöning is a Professor at the Faculty of Engineering and Computer Science , Osnabrück University of Applied Sciences. His research focuses on Artificial Intelligence , Human-Computer Interaction , and Computer Vision within agricultural contexts. 2019–Present: Professor, Hochschule Osnabrück 2018–2019: System Architect, ZF Friedrichshafen AG 2014–2018: Researcher, University of Osnabrück 2009–2013: Project Lead/System Engineer, CLAAS Harsewinkel Education: M.Sc. in Intelligent Embedded Microsystems (Freiburg), B.Eng. in Mechatronics (DHBW Stuttgart) His research spans smart agriculture , quantum NLP , and explainable AI systems , with recent work on vibrotactile warning systems, AI compliance frameworks, and hybrid dataset applications in farming. Publications emphasize interdisciplinary approaches bridging technology and agricultural practice . Scientific honors include: DAAD Stipendium for conference participation Best Paper Award (2018) IEEE GHTC Student Paper Contest Winner (2016) Sonderpreis für gute Lehre (2017) Finalist/Falling Walls Lab (2015)

Bo Xiongمشاهده پروفایل

پژوهشگر

• Machine Learning
• Knowledge Graphs
• Graph Neural Networks
• +۴ مورد دیگر

Bo Xiong is a researcher at the University of Stuttgart in the Analytic Computing group. His research focuses on machine learning and knowledge graphs , with a particular emphasis on geometric embeddings and hyperbolic neural networks. His research interests include: Knowledge graph embeddings Hyperbolic and pseudo-Riemannian geometry in AI Temporal knowledge graph reasoning Structured multi-label prediction Recent publications highlight his work on geometric relational embeddings, complex query answering, and temporal fact reasoning using advanced manifold-based techniques.

Simone Winkoمشاهده پروفایل

استاد

• Modern German Literature
• Literary Theory
• Emotion in Poetry
• +۴ مورد دیگر

Prof. Dr. Simone Winko holds the Chair for Modern German Literature and Literary Theory at the University of Göttingen since 2003. Her research spans literary theory, canonization, praxeology of literary studies, and the intersection of emotions with German poetry around 1900 and digital humanities. Academic Roles: Chair at Göttingen (2003–), DFG Priority Programme 2207 leadership (2017–), Courant Center collaboration (2009–) Key Projects: DFG projects on literary change (2023–), computational literary history (2020–2023), and argumentation practices in interpretations (2018–2020) Her recent work focuses on emotions in German-language poetry , using computational methods to model text similarity and analyze historical shifts between Realism and Modernism. She explores how emotional codes and narrative strategies are embedded in lyrical structures, particularly through projects like Anthologien zeitgenössischer deutschsprachiger Lyrik (2022). Scientific Awards : 2010: 1st prize for best doctoral supervision (KissWin, BMBF-funded) Teaching & Collaboration : Supervises B.A., M.A., and Ph.D. theses; co-edits the Journal of Literary Theory and Revisionen book series. Collaborates with Fotis Jannidis, Gerhard Lauer, and Matías Martínez on computational approaches to literary studies.

Stefania Degaetano-Ortliebمشاهده پروفایل

دانشیار

• Computational Linguistics
• Corpus Linguistics
• Sociolinguistics
• +۶ مورد دیگر

Stefania Degaetano-Ortlieb is an Associate Professor of English Linguistics and Corpus Linguistics at Saarland University's Department of Language Science and Technology. She serves as Principal Investigator for the Collaborative Research Center (SFB 1102) 'Information Density and Linguistic Encoding,' leading Project B1 on diachronic information density in English scientific writing (17th century-present). Her interdisciplinary work bridges computational methods with sociolinguistics, focusing on register variation, language change, and digital humanities. Research interests center on text mining, data analytics, and probabilistic modeling of language variation. Key areas include: Diachronic evolution of scientific registers and linguistic densification Information-theoretic approaches to language efficiency Computational sociolinguistics and register diversification AI applications in humanities education (e.g., ChatGPT integration) Her publications show a strong trend toward quantitative diachronic analysis, with recent work emphasizing: interpretable AI models for linguistic change detection; propagandistic narrative analysis in conflict zones; and multi-word expression dynamics in scientific discourse. Cross-disciplinary collaborations frequently intersect with history, psychology, and media studies. Awards include the Fellowship Excellence Program for Young Female Scientists (2015-2018). Current grants: EU Horizon MSCA Doctoral Network 'CASCADE' (€521K to UdS, 2024-2027) Data-Pin Project for AI in education (€50K, 2023-2024) SFB 1102 Project B1 (€595K, 2022-2026) Advises PhD candidates in the EU CASCADE project on computational semantic change. Leads a research team exploring Russian media narratives, personality modeling in LLMs, and multi-word expressions. Directs teaching modules integrating AI tools for humanities students.

Rainer Gemullaمشاهده پروفایل

استاد

• Machine Learning
• Data Processing
• Knowledge Graphs
• +۳ مورد دیگر

Rainer Gemulla is a Professor of Practical Computer Science I: Data Analytics at the University of Mannheim, heading the Data and Web Science Group within the School of Business Informatics and Mathematics. He has been a W3-Professor at the University since 2014, following positions as a senior researcher at Max-Planck-Institut für Informatik (2010-2014) and postdoctoral researcher at IBM Almaden Research Center (2008-2010). His research focuses on machine learning with structured and semi-structured data, particularly knowledge graphs, and developing efficient systems for data-intensive processing. Professor Gemulla's research spans multiple areas including machine learning with structured data (relational data), machine learning with semi-structured data (multi-relational graphs), combining these approaches with unstructured knowledge (text), and developing efficient, scalable methods for data-intensive processing. His work bridges theoretical foundations with practical implementations, as evidenced by numerous open-source software projects including LibKGE, DistKGE, and AdaPM. His recent publications show a strong trend toward knowledge graph embeddings, parameter server architectures, and efficient training methods. The research demonstrates increasing focus on scalability challenges in graph learning, with particular attention to hyperparameter optimization, dynamic resource allocation, and benchmarking methodologies. His work consistently addresses the practical challenges of implementing machine learning systems at scale. Distinguished Reviewer Award at SIGMOD, 2025 Distinguished PC Member Award at EDBT, 2023 Outstanding Reviewer Award at NeurIPS, 2021 Junior-Fellow of the Gesellschaft für Informatik (GI), 2013 IBM's 2011 Pat Goldberg Memorial best paper award Best paper of NIPS 2011 Biglearn workshop Professor Gemulla actively mentors PhD students and has supervised numerous successful doctoral candidates. His leadership extends to administrative roles including Head of examination board for MSc Business Informatics since 2017, and previously serving as Study dean of the WIM faculty (2016-2019) and CIO of University of Mannheim (2022-2024). His research is supported by grants including AWS in Education Research Grant Award (2013) and Google Focused Research Award (2011). The Data and Web Science Group develops multiple open-source software projects including LibKGE (knowledge graph embedding library), DistKGE (multi-GPU training), AdaPM (adaptive parameter manager), Lapse (parameter server), and various tools for information extraction and sequence mining. The group maintains active collaborations with industry partners and academic institutions worldwide, particularly in the areas of knowledge graph research and scalable machine learning systems.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +۱۲ مورد دیگر

Hao Liu is a researcher affiliated with institutions like Chinese Academy of Sciences , Beihang University , and Stanford University . His work spans Computer Science , Artificial Intelligence , and Robotics . Key affiliations: National Space Science Center (Beijing), School of Astronautics (Beihang), Key Laboratory of Pervasive Computing (Tsinghua) Research interests include Machine Learning , Image Processing , Graph Neural Networks , and Wireless Communication Optimization His recent publications focus on: Advanced control systems for fuzzy models Medical imaging via hyperspectral analysis Transformer-based approaches in NLP and vision Quantum-safe and edge computing protocols

• Machine Learning
• Computer Vision
• Signal Processing
• +۶ مورد دیگر

Dongwook Kim is affiliated with the Korea Advanced Institute of Science & Technology (KAIST) as a faculty member in the Department of Business and Technology Management under the College of Business. His research spans multiple domains including machine learning, robotics, signal processing, and biomedical engineering. Key contributions in Computer Vision (CNN-based semantic segmentation, 3D point cloud analysis) Significant work in Hardware Design (energy-efficient processors, neuromorphic computing) Interdisciplinary expertise in Medical Imaging (bone age assessment, retinal biomarkers) and Cybersecurity (attack detection, network analytics) Publications since 2015 demonstrate sustained innovation in AI applications , Signal Processing , and Smart City Governance . His work often integrates theoretical advances with practical implementations in real-world systems. No scientific awards or student mentorship details are explicitly documented in the provided records.

Jens Krauseمشاهده پروفایل

استاد

• Behavioural Ecology
• Collective Intelligence
• Social Networks
• +۷ مورد دیگر

Jens Krause is a Professor and Head of Department at the Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB) in Berlin, leading the Research Group on Mechanisms and Functions of Group-Living. He holds a full professorship in Fish Ecology at Humboldt-Universität zu Berlin, Faculty of Life Sciences, Thaer-Institute, and since 2018 has been an Adjunct Professor at Technical University Berlin within the Excellence Cluster 'Science of Intelligence'. His research is centered on collective intelligence, social networks, decision-making, and behavioural ecology in fish and other animals. Full Professor in Fish Ecology, Humboldt-Universität zu Berlin Adjunct Professor at Technical University Berlin (since 2018) Head of Department, IGB Berlin PhD, University of Cambridge Diploma, Free University Berlin His work integrates experimental biology, network analysis, and biomimetic robotics to understand how animals make collective decisions. His expertise spans animal behaviour, evolution, and ecological physiology, with a strong focus on group-living dynamics. Recent research explores group hunting, predator evasion, social foraging, and the impact of environmental stressors on collective behaviour. The analysis of his recent publications reveals a strong trend in understanding collective behaviour in fish, including escape waves, social foraging, group hunting in marlins and sailfish, and the use of robotic agents to study social integration. His interdisciplinary approach combines marine biology, physics, robotics, and data science to uncover the mechanisms behind collective intelligence in both animal and human systems. Editorial Board, Behavioral Ecology Editorial Board, Fish and Fisheries Executive Board, Excellence Cluster 'Science of Intelligence' Advisory Board, Bimini Biological Field Station Foundation He advises numerous PhD students and postdoctoral researchers, and leads major research projects, including 'Developing exploration behaviour' funded by the Excellence Cluster. His work has been supported by extensive collaborations across Europe and North America, and he frequently publishes in top-tier journals such as Nature , Science Advances , Proceedings of the Royal Society , and Current Biology . His lab employs cutting-edge methods including automated tracking, social network analysis, and interactive robotics to study animal groups. His research group, 'Mechanisms and Functions of Group-Living', is embedded within the Excellence Cluster 'Science of Intelligence', where they investigate collective cognition, social information use, and the role of individual differences in group performance. The team combines field studies with laboratory experiments and computational modelling to understand the evolution and function of collective behaviour across species.

Roland Zengerleمشاهده پروفایل

استاد

• Microfluidics
• Lab-on-a-Chip
• Bio-MEMS
• +۶ مورد دیگر

Prof. Dr. Roland Zengerle serves as Full Professor for Application Development at the Institute of Microsystems Technology within the Faculty of Engineering at Albert Ludwigs University of Freiburg, concurrently holding the position of Director at Hahn-Schickard Institute for Microanalysis Systems in Freiburg. His academic leadership spans microsystems engineering with a focus on translational research bridging fundamental science and clinical applications. Zengerle's research expertise centers on Microfluidics, Lab-on-a-Chip systems, Bio-MEMS, Electrochemical Energy Systems, and Tomographic Reconstruction of Mesoporous Materials. He pioneers hybrid manufacturing techniques integrating molten metal printing with polymer processing to develop point-of-care diagnostic platforms and advanced energy systems. Current projects include UTI-Diag for urinary tract infection diagnosis and PhotonMed, a 32-million-euro medical technology initiative where his MEMS Applications Laboratory develops centrifugal microfluidic solutions. Analysis of his recent publications reveals a dominant trend toward multi-technology integration: centrifugal microfluidics combined with 3D bioprinting for organoid-based drug testing, molten metal printing for flexible electronics, and bead-based immunoassays for infectious disease detection. The work demonstrates strong clinical translation focus, particularly in cancer diagnostics (circulating tumor cell isolation), infectious disease testing (TB diagnostics), and regenerative medicine (spheroid/organoid handling). His laboratory has secured significant funding for high-impact projects including: UTI-Diag: Molecular diagnostics for urinary tract infections PhotonMed: Medical technology innovation consortium livMatS: Living, Adaptive and Energy-autonomous Materials Systems Zengerle actively mentors researchers through Freiburg's Master Lab program and Writer's Studio initiative while promoting young talent via Bootcamp training. His group maintains strategic alliances with Hahn-Schickard spin-offs and industry partners, leveraging university cleanroom facilities and specialized service centers for microfabrication. The MEMS Applications Laboratory operates as a hub for interdisciplinary innovation, combining microfabrication expertise with clinical insights to develop commercializable diagnostic solutions. Current infrastructure supports centrifugal microfluidic cartridge development, 3D-bioprinting of tissue models, and electrochemical sensor integration, with ongoing work focused on automating complex biological workflows for point-of-care applications.

• Computer Architecture
• Hardware Acceleration
• Approximate Computing
• +۳ مورد دیگر

Akash Kumar is a Professor at Ruhr University Bochum, Germany, with affiliations to multiple institutions including TU Dresden and National University of Singapore. His research focuses on computer architecture, hardware acceleration, and approximate computing, with a strong emphasis on FPGA-based systems and neural network optimization. He has contributed extensively to embedded systems, mixed-criticality systems, and security in emerging technologies like reconfigurable nanodevices. His work spans cross-layer approximation methodologies, graph neural networks, and energy-efficient processing for edge AI. Collaborations with industry and academia highlight his leadership in VLSI design and secure hardware implementations. Notable contributions include frameworks for bounding time in mixed-criticality systems and resilient logic locking techniques. Research interests include hardware-software co-design, real-time systems, and efficient neural network architectures. His publications in top-tier conferences (DAC, DATE, FCCM) and journals (IEEE Trans. CAD, ACM TECS) underscore his impact in the field.