جست‌وجوی استاد و پژوهشگر

• Computational Geometry
• Algorithms
• Trajectory Analysis
• +۳ مورد دیگر

Maike Buchin is a Professor of Theoretical Computer Science / Algorithmics at the Faculty of Computer Science, Ruhr-University Bochum, where she has been serving since 2019. She also holds the position of Studiendekanin Informatik (Dean of Studies for Computer Science). Prior to her current position, she was a Visiting Professor at Technical University Dortmund (2017-2019) and a Juniorprofessor at Ruhr University Bochum (2013-2017). Dr. Buchin's research focuses on computational geometry, algorithms, and trajectory analysis. Her work particularly emphasizes Frechet distance computations, curve matching, and geometric algorithms. She has made significant contributions to understanding the computational complexity of geometric problems and developing efficient algorithms for trajectory data analysis. Her research has applications in geographic information systems, movement pattern analysis, and shape comparison. Analysis of her recent publications reveals a strong focus on clustering algorithms for polygonal curves, Frechet distance computations, and trajectory analysis. Her work bridges theoretical computer science with practical applications in GIS and movement data analysis. She has developed approximation algorithms, coreset constructions, and efficient query processing techniques for geometric problems. Her research has been published in top-tier venues including ACM Transactions on Algorithms, Computational Geometry: Theory and Applications, and proceedings of major conferences like Symposium on Computational Geometry (SoCG) and European Symposium on Algorithms (ESA). Dr. Buchin has supervised numerous students and taught courses including Algorithm Paradigms, Computer Science 2 - Algorithms and Data Structures, Computer Science 3 - Theoretical Computer Science, Geometric Algorithms, Data Structures, and seminars on Cryptology and Theoretical Computer Science. She leads research in the Theoretical Computer Science / Algorithmics group at Ruhr-University Bochum, collaborating with researchers worldwide on computational geometry problems and their applications.

• Temporal Databases
• Time Series Analysis
• Database Systems
• +۴ مورد دیگر

Anton Dignös is a professor at the Free University of Bozen-Bolzano , specializing in temporal databases , time series analysis , and database systems . His research focuses on efficient query processing for interval data, temporal joins, and schema design, with significant contributions to in-memory and time series databases. Key research areas include: Temporal Data Management : Advanced techniques for interval and duration queries. Time Series Analytics : Machine learning integration and pattern detection. Schema Optimization : Automated design and tuning of database schemas. Visual Analytics : Tools for period data comparison and correlation analysis. His work spans collaborations with researchers like Johann Gamper and Michael H. Böhlen , addressing challenges in healthcare systems, industrial applications, and financial data analytics. Notable contributions include algorithms for temporal anti-joins , range-duration queries , and machine learning-based anomaly detection .

• Artificial Intelligence
• Building Information Modeling (BIM)
• Machine Learning
• +۳ مورد دیگر

Changyu Du is a researcher at the Chair of Computing in Civil and Building Engineering at the Technical University of Munich. His work focuses on AI-driven automation in BIM workflows, including command recommendation systems and conflict resolution. Research Interests: Application of artificial intelligence and large language models to BIM authoring tools Transformer-based architectures for design pattern recognition Reinforcement learning for automated conflict resolution Multi-modal fusion techniques in 3D point cloud processing Human-machine interaction in engineering software Recent Publication Trends: His work spans AI integration in BIM (2025), neural network architectures for semantic segmentation (2021-2022), and collaborative human-AI systems (2024). Key technologies include transformers, graph neural networks, and reinforcement learning. Teaching: Active participant in the SoftwareLab course, contributing to hands-on computational engineering education.

• 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)

Paolo G. Giarrussoمشاهده پروفایل

پژوهشگر

• Programming Language Theory
• Domain-Specific Languages
• Distributed Systems
• +۵ مورد دیگر

Paolo G. Giarrusso is a researcher at the Institute for Programming Languages and Software Engineering within the Faculty of Informatics at the University of Tübingen . Previously, he was a Ph.D. student at the University of Marburg , where he defended his thesis Optimizing and Incrementalizing Collection Queries by AST Transformation in January 2018.

• Database Systems
• In-Database Machine Learning
• GPU Acceleration for Databases
• +۳ مورد دیگر

Maximilian Schüle serves as Assistant Professor in the Department of Data Engineering at the University of Bamberg's Faculty of Information Systems and Applied Computer Sciences since October 2022. Previously, he held research positions at Technical University of Munich (2017-2022). His research bridges database systems and machine learning through compiler-based approaches. His research focuses on in-database machine learning , GPU-accelerated query processing , and recursive SQL extensions . Key contributions include: Developing MLIR-based compilers for automatic differentiation in SQL (DuoLingo-AutoDiff) Creating GPU code generators for database kernels using NVRTC Designing higher-order lambda functions for expressive query languages Implementing end-to-end neural network training within database engines His recent publications (2023-2025) demonstrate consistent output in top venues including ICDE, VLDB workshops, and BTW conferences, with growing emphasis on hardware-aware optimization and compiler techniques for analytical workloads. He currently leads a DFG-funded project on elastic memory hierarchies for memory-intensive applications (2025-2028), supporting multiple PhD researchers. His supervision emphasizes open-source contributions to database systems like Umbra and practical implementation skills alongside theoretical foundations. As an active member of the database community, he serves as workshop chair for BTW 2025 and regularly reviews for ACM TODS, VLDB Journal, and Information Systems. His work on public transport analytics demonstrates real-world impact through collaborations with urban mobility initiatives in Bamberg.

Rita Hartelمشاهده پروفایل

مدرس ارشد

• Data Compression
• Bioinformatics
• Graph Databases
• +۴ مورد دیگر

Dr. Rita Hartel serves as a Senior Lecturer and Research Associate in the Department of Databases and Electronic Commerce at the University of Paderborn's Institute of Computer Science, while also fulfilling the critical role of Academic Advisor for the Computer Science Student Office where she guides undergraduate and graduate students. Her research forms two distinct yet complementary pillars: cutting-edge data compression algorithms for bioinformatics (specializing in Burrows-Wheeler Transform optimizations for DNA sequence data) and graph databases (developing grammar-based compression for knowledge graphs), alongside innovative digital humanities work applying OCR and semantic analysis to comics historiography with a focus on German traditions. This interdisciplinary approach bridges computational rigor with cultural scholarship. Dr. Hartel demonstrates consistent research productivity with five publications from 2021-2025 in premier venues including the Data Compression Conference and specialized workshops. Her work reflects strong international collaboration patterns and likely benefits from active grant funding given the technical resources required for bioinformatics compression research. As an Academic Advisor, she provides essential student support while her dual research focus creates unique opportunities for students interested in either computational methods or digital humanities applications.

Peter Florian Stadlerمشاهده پروفایل

استاد

• Bioinformatics
• Computational Biology
• RNA Biology
• +۴ مورد دیگر

Peter Florian Stadler is a Professor of Bioinformatics at the University of Leipzig since 2002 and Director of the Interdisciplinary Center for Bioinformatics (IZBI) since 2023. He holds affiliations with the Max Planck Institute for Mathematics in the Sciences (scientific member since 2009), the Austrian Academy of Sciences (corresponding member since 2010), and the Santa Fe Institute (external member since 1994). Doctorate in Chemistry (University of Vienna, 1990) Habilitation in Theoretical Chemistry (University of Vienna, 1994) His research focuses on Bioinformatics , Computational Biology , and Evolutionary Dynamics , with specific interests in RNA structure prediction , non-coding RNA , and genome-wide data analysis . Recent work involves developing ViennaRNA Package tools and studying translational control mechanisms in melanoma. Professional milestones include: Co-development of Vienna RNA Package , FRANz (phylogenetic tree reconstruction), code2aln (alignment tools) Key contributions to RNA gene finding algorithms and ncRNA family characterization Honors: Corresponding member, Austrian Academy of Sciences (2010) Honorary professor, Faculty of Science, Bogota (2018)

Radu Calinescuمشاهده پروفایل

استاد

• formal methods
• software engineering
• self-adaptive systems
• +۷ مورد دیگر

Radu Calinescu is Professor of Computer Science at the University of York, UK, where he serves as Principal Investigator for the UKRI Trustworthy Autonomous Systems Node in Resilience and leads the Trustworthy Adaptive and Autonomous Systems and Processes (TASP) Research Team. His academic career includes previous positions as Lecturer in Computer Science at Aston University (2009-2012), Senior Researcher at the University of Oxford (2008-2009), and part-time Lecturer at Oxford (2005-2009). Professor Calinescu's research focuses on formal modelling, analysis, verification and controller synthesis for autonomous and self-adaptive systems, with particular emphasis on parametric and probabilistic model checking, automated and model-driven software engineering. His work applies these approaches to robotic, cyber-physical, embedded and service-based systems, with a strong commitment to using formal methods at runtime to enhance the resilience and safety of critical autonomous systems. His extensive publication record spans top-tier journals including IEEE Transactions on Software Engineering, Journal of Systems and Software, and Automated Software Engineering. His research demonstrates consistent focus on verification techniques for adaptive systems, with increasing attention to safety-critical applications in recent years. His work bridges theoretical formal methods with practical applications in robotics and autonomous systems. British Computer Society Distinguished Dissertation Award for his DPhil thesis on Autonomic-Independent Loop Parallelisation Principal Investigator for multiple major projects including Continual Verification and Assurance of Robotic Systems under Uncertainty (ORCA Hub/EPSRC), Safety of AI Techniques (AAIP/Lloyd's Register Foundation), and CSI:Cobot Program Committee Co-Chair for major conferences including SEFM 2021, SEAMS 2020, and SERENE 2019 Professor Calinescu actively supervises numerous PhD students and postdoctoral researchers, with current team members including Faisal Alhwikem, Xinwei Fang, Mario Gleirscher, James Harbin, and Colin Paterson. His research group is based at the Ron Cooke Hub in York, a purpose-built facility housing world-class research groups and startups. His former students have gone on to academic positions at institutions worldwide and industry roles at major technology companies.

Ding Liمشاهده پروفایل

استادیار

• Program Analysis
• Energy Optimization
• Mobile Applications
• +۲ مورد دیگر

Ding Li is an Assistant Professor in the School of Computer Science at Peking University. He holds a Ph.D. in Computer Science from the University of Southern California (USC) and a B.S. from Peking University. His research focuses on program analysis, energy optimization for mobile applications, and security, with publications in top conferences including ICSE, FSE, and ASE. His research interests span: Program Analysis : Techniques to optimize mobile application energy consumption. System Security : Identifying vulnerabilities in Android apps and WebAssembly binaries. Cloud/Edge Computing : Enhancing serverless computing efficiency and federated learning security. Dr. Li's recent work explores the integration of large language models into pointer analysis and automated optimization of resource inefficiencies. His publications demonstrate a consistent focus on practical system optimizations and security enhancements across mobile, cloud, and machine learning domains. Awards: Viterbi Undergraduate Research Mentoring Award (2014)

Jingyue Liمشاهده پروفایل

استاد

• Software Engineering
• Software Security
• Software Engineering and AI
• +۲ مورد دیگر

Jingyue Li is a Professor in the Department of Computer Science at the Norwegian University of Science and Technology (NTNU), within the Faculty of Information Technology and Electrical Engineering. She earned her Ph.D. in Software Engineering from NTNU in 2006 and has extensive industrial experience including positions at IBM and DNV Research and Innovation. Her educational background includes: Ph.D. in Software Engineering from NTNU (2006) Professor Li's research spans several key areas in software engineering with a focus on both theoretical and practical applications. Her work bridges traditional software engineering practices with emerging technologies, particularly in the domain of security and blockchain systems. She has conducted extensive empirical research and applied design science methodologies to develop innovative tools and approaches. Her recent publications demonstrate a strong trend toward blockchain technologies, software security, and the intersection of AI with software engineering. The research shows increasing focus on practical applications of blockchain in decentralized autonomous organizations, consensus protocols, and security vulnerabilities in smart contracts. There's also significant work on integrating security practices into DevOps (DevSecOps) and applying machine learning techniques to software engineering problems. Professor Li has received recognition through her leadership roles in major conferences: General Chair for FSE 2025 (The ACM International Conference on Foundations of Software Engineering) Member of the EASE (International Conference on Evaluation and Assessment in Software Engineering) steering committee In terms of research leadership, Professor Li has served as Principal Investigator (PI) or Key Scientist on numerous research projects including TRACE4EU (2023-2025), PaaSforChain (2020-2023), CyberSmart (2017-2020), and several others focused on blockchain, security, and software engineering. She has also conducted research visits to institutions including University College London, University of Washington, Hiroshima University, and Peking University. Her research group appears to be actively engaged in both theoretical and applied research, with strong industry connections through projects like CyberSmart and SecureCyber that address real-world challenges in cybersecurity and smart city infrastructure.

Salam Traboulsiمشاهده پروفایل

پژوهشگر

• Smart City
• 5G Applications
• Indoor Positioning Systems
• +۲ مورد دیگر

Dr. Salam Traboulsi is a Researcher at Stuttgart University of Applied Sciences (HFT Stuttgart), affiliated with the Competence Center for Digitalization in Research, Teaching & Economics since 2019. She holds a PhD in Computer Science from the University of Toulouse, France (2008). Her work bridges technology and urban innovation, with a focus on developing scalable solutions for modern cities. Research Focus: Dr. Traboulsi specializes in: Smart City ecosystems integrating IoT and 5G Precision technologies for urban positioning and navigation Data management frameworks for large-scale sensor networks Open-source IoT platforms for building efficiency and environmental monitoring Cloud and grid computing infrastructures Key Projects: She leads/contributes to: iCity 2: UDigiT4iCity – Developing urban digital twins using IoT building data and 5G sensor networks iCity 1 – Foundational research on intelligent urban infrastructure systems Publication Trends: Her recent work (2020-2024) emphasizes 5G-enabled urban solutions, including fleet management optimization, indoor positioning systems, and IoT analytics for smart buildings. Earlier research (2005-2013) focused on distributed computing, storage virtualization, and information retrieval systems, demonstrating consistent expertise in large-scale data infrastructure. Academic Engagement: She serves as a scientific reviewer for journals and conferences and teaches in the surveying study area at HFT Stuttgart.

Sabine Arndt-Lappeمشاهده پروفایل

استاد

• Linguistics
• Morphology
• Phonology
• +۵ مورد دیگر

Sabine Arndt-Lappe is a Professor of English Linguistics at the University of Trier , Germany. She previously held research positions at the Universities of Marburg, Siegen, and Düsseldorf. As a structural linguist, her work bridges empirical and theoretical approaches to morphology and phonology. Research Focus : Variation and productivity in English lexicon, formal and semantic aspects of word-formation, prosodic morphology, analogy-based and usage-based grammatical models, computational analogical modeling, and Optimality Theory. Projects : Leads the DFG-funded AdvAnTheMorph project (2025–2028) and co-leads the DFG-ANR ERSaF project (2022–2025). Spokesperson for the Trier Center for Language and Communication research group on Patterns/Musterhaftigkeit (2020–2023). Publications span topics like name blending, stress assignment in English verbs, clipping mechanisms, affix rivalry, and cross-linguistic morphological analysis. Her work employs computational models (e.g., Naive Discriminative Learning) and corpus-based methodologies. Editorial Contributions : Editor-in-Chief of Morphology (2022–present), former Editor of Journal of Linguistics (2017–2021), and editorial board member for multiple journals including English Language and Linguistics and Journal of Linguistics . Teaching includes courses like LING 101: Introduction to Linguistic Studies , LING 801: Words in the Mind , and LING 804: Statistics for Linguists . She also mentors doctoral candidates and supervises software development for morphological research (e.g., M2E - Morph2Excel ).

Janina Kneippمشاهده پروفایل

استاد

• Surface-Enhanced Raman Scattering
• Plasmonic Catalysis
• Nanoscale Spectroscopy
• +۴ مورد دیگر

Prof. Dr. Janina Kneipp is a Professor (W3) of Physical Chemistry at Humboldt-Universität zu Berlin, where she has led an active research group since 2012. She previously held positions as Assistant Professor at HU Berlin/BAM (2008-2012), Junior Researcher at BAM (2005-2008), and research appointments at Harvard Medical School, Princeton University, and Erasmus Universiteit Rotterdam. Education: Dr. rer. nat. (summa cum laude), Freie Universität Berlin (2002) Undergraduate/Graduate Studies in Biology & Physics, Freie Universität Berlin (1992-1998) Research Focus: Her interdisciplinary work bridges physical chemistry and biospectroscopy, with particular emphasis on: Surface-enhanced Raman scattering (SERS) for complex sample analysis Plasmonic catalysis and hot electron chemistry Multiphoton-excited vibrational spectroscopy Nanoscale biochemical mapping in plant and animal systems Development of advanced plasmonic substrates Publication Trends: Recent work demonstrates strong focus on multimodal spectroscopy applications, with studies combining SERS, hyper-Raman, IR, and synchrotron techniques to address questions in catalysis, nanoparticle-cell interactions, plant biochemistry, and biosensing. Publications frequently incorporate advanced nanomaterials, electrochemical methods, and machine learning-assisted spectral analysis. Scientific Awards: Fellow, European Academy of Sciences (2020) Caroline von Humboldt Professorship (2019) Wilhelm Ostwald Fellow, BAM (2012) Bunsen-Kirchhoff Award, GDCh (2010) ERC Starting Grant (2010) Academic Leadership: Currently advises 5 PhD students and leads multiple collaborative initiatives. Serves as Board Member of Einstein Center Catalysis (since 2019), Head of Chemistry Department (2014-2016), and Speaker of Graduate School SALSA (since 2012). Secured funding through DFG, EU networks, and ERC grants supporting spectroscopy infrastructure development. Lab & Team: Leads the KneippLab research group with 2 postdoctoral researchers, 5 graduate students, and technical staff. Research focuses on developing spectroscopic methods for interrogating biological and chemical processes at nanoscale resolution using plasmonic enhancement strategies.

Andreas Wübbekeمشاهده پروفایل

استاد

• Software Engineering
• Agile Development Models
• Digitalization in Agriculture
• +۲ مورد دیگر

Prof. Dr. Andreas Wübbeke is a Professor of Software Engineering at the Faculty of Electrical Engineering, University of Applied Science Southern Westphalia (since 2022). With extensive industry experience in tech leadership roles at CLAAS E-Systems and Wincor Nixdorf, his research bridges academic rigor and practical applications. Research Focus: His work spans Agile methodologies, VR application testing, digital agriculture, and cloud systems. Recent projects include AI-driven classification of agricultural operations and sustainable energy solutions. He leads the South Westphalia Software Engineering Lab, mentoring thesis students through structured programs. Professional Engagement: Active in academic communities, he serves on committees for conferences (PROFES, GIL) and journals. His invited keynotes address topics like smart farming connectivity and agile transformations. Education & Background: PhD in Computer Science (Databases/Information Systems), University of Paderborn (2010) Business Computer Science degree, University of Paderborn (2007)