جست‌وجوی استاد و پژوهشگر

• Edge Computing
• Fog Computing
• Distributed Systems
• +۵ مورد دیگر

Umakishore Ramachandran is a Professor in the School of Computer Science within the College of Computing at Georgia Institute of Technology. His research spans edge computing, distributed systems, and real-time video analytics, with significant contributions to fog computing infrastructure, mobile systems, and sensor networks. Over a prolific 38-year career, he has authored 142 publications with major contributions in 2022-2025. His research interests focus on bridging the gap between cloud and edge computing, with pioneering work in video analytics systems like EVA and MicroEdge. He investigates resource optimization for latency-sensitive applications, developing novel approaches for load shedding, data management, and container runtime efficiency at the network edge. His work addresses fundamental challenges in distributed camera networks, autonomous vehicle systems, and real-time stream processing. Ramachandran's recent publications reveal a strong emphasis on practical edge computing solutions, with 75% of his 2021-2025 work focusing on video analytics and infrastructure optimization. His research shows increasing collaboration with industry partners while maintaining academic rigor, with publications appearing in top venues like SIGMOD, Middleware, and DEBS. The work consistently addresses real-world constraints of resource-constrained edge environments. Ramachandran has mentored numerous researchers who have become principal investigators on edge computing projects, with notable collaborators including Harshit Gupta, Enrique Saurez, and Zhuangdi Xu appearing as first authors on multiple papers. His work has received significant grant support for projects related to mobile fog computing and distributed video analytics. He leads research in the Edge Computing Laboratory at Georgia Tech, focusing on the development of practical frameworks for real-world deployment of edge infrastructure. Current projects include eCAV for connected autonomous vehicles and MicroEdge for multi-tenant camera processing systems.

Daniel Frischemeierمشاهده پروفایل

استاد

• Mathematics Education
• Statistics Education
• Data Science Education
• +۵ مورد دیگر

Daniel Frischemeier is a Professor of Mathematics Didactics with a focus on Primary Education at the University of Münster's Faculty of Mathematics and Computer Science. He has established himself as a leading researcher in statistics and data science education for primary school students, with extensive contributions to educational methodology and teacher training. University of Münster (2021-present) TU Dortmund (2020-2021) University of Paderborn (2009-2020) Ludwig-Maximilians-Universität München (2017-2018) Dr. Frischemeier completed his doctoral studies at the University of Paderborn with a dissertation on statistical thinking and research using TinkerPlots software. His educational background includes graduate studies in Mathematics and undergraduate studies in Mathematics and Physics for teaching at various school levels. His research focuses on the design and testing of teaching-learning environments for primary mathematics education, particularly in the areas of data analysis, probability, and statistics. He conducts qualitative analysis of learners' cognitive processes related to the guiding principle of 'data and chance' in primary education. His work also includes the design and evaluation of teaching materials in data science and civil statistics, the use of learning videos to promote process-related skills, and the implementation of Fermi tasks and computer science education within primary mathematics lessons. Analysis of Dr. Frischemeier's recent publications reveals a strong emphasis on data literacy development in primary education, with increasing focus on the integration of digital tools and the conceptual understanding of data as models. His work bridges mathematics education with emerging fields of data science, addressing both theoretical frameworks and practical classroom applications. The research demonstrates a progression from basic statistical concepts toward more complex data modeling approaches suitable for young learners. Elected member of the International Statistical Institute (ISI) Chair of the Local Organizing Committees for IASE Satellite 2025 Conference Council-Member of the International Statistical Institute Special Edition Editor of the Statistics Education Research Journal Member of International Program Committees for major statistics education conferences Co-Leader of CERME Thematic Working Group 5 on Probability and Statistics Education Dr. Frischemeier serves in numerous editorial capacities and review roles for prominent journals in mathematics and statistics education. He leads significant research projects including 'Promoting Data Science Education for Teacher Education at the University level (DataSETUP)' and 'Data Science Education in STEAM for Civic Engagement and Social Justice from the Early Years (DataScEd4CiEn)'. His work has substantial impact on teacher education programs and curriculum development in statistics and data science for primary schools. He is actively involved in the development and leadership of the Math Center Münster (MaZ), which promotes mathematical potential for all students. His team includes numerous research assistants and doctoral candidates working on various aspects of mathematics education research, particularly focusing on data literacy and statistical reasoning in primary education contexts.

• Database Systems
• Data Management
• Scientific Data Processing
• +۵ مورد دیگر

Dr. Martin L. Kersten is a leading figure in database systems research at the Centrum Wiskunde & Informatica (CWI) in Amsterdam, Netherlands. With over three decades of contributions, his work focuses on column-oriented database architectures, scientific data management, and query optimization. Key Research Areas: Database systems, big data processing, query performance analysis, data-intensive scientific applications Projects: MonetDB, SciQL, TELEIOS, ExaNeSt His recent publications emphasize in-database machine learning , query log mining , and exascale computing . He pioneered database cracking and intermediate recycling techniques to enhance query processing efficiency. 2014 SIGMOD Edgar F. Codd Innovations Award for groundbreaking contributions to database technology Collaborations span institutions like ICDE , VLD , and EuroSys workshops. His work bridges theoretical advancements with practical implementations for scientific and industrial applications.

• Information visualization
• Visual analytics
• Eye tracking
• +۱۰ مورد دیگر

Tanja Blascheck is a PostDoc Researcher and Margarete von Wrangell Fellow at the Institute for Visualization and Interactive Systems (VIS) at the University of Stuttgart. Her work focuses on visual analytics , eye tracking , and microvisualizations for smartwatches and other wearable devices.

Jilles Vreekenمشاهده پروفایل

استاد

• Causal Inference
• Unsupervised Learning
• Exploratory Data Analysis
• +۷ مورد دیگر

Jilles Vreeken is a Professor of Computer Science at Saarland University and tenured faculty at the CISPA Helmholtz Center for Information Security, where he leads the Exploratory Data Analysis research group. He is also an ELLIS Fellow and Faculty of the Saarbrücken Unit on AI and ML. His work bridges theoretical foundations with practical applications in causal inference, unsupervised learning, and exploratory data analysis. Dr. Vreeken's research focuses on developing theory and algorithms for answering fundamentally exploratory questions about data: "what is going on in my data?", "what causes what and how?", and "what can we learn from this model?" without making unnecessary or unjustified assumptions. He takes a principled approach based on information theory to identify what is worth knowing, then develops efficient algorithms for extracting useful interpretable results. His work spans causal inference under realistic conditions (including hidden confounding, selection bias, and non-i.i.d. data), summarizing complex data and models in understandable terms, and combining these threads to create more robust and useful models across diverse data types. His recent publications demonstrate a strong trend toward causal discovery in increasingly realistic settings, including non-stationary time series, event sequences, and scenarios with hidden confounders. He has made significant contributions to federated learning, interpretable machine learning, and pattern mining. His work consistently applies information-theoretic principles to develop methods that are both theoretically sound and practically useful for extracting insights from complex data. Dr. Vreeken has received numerous prestigious awards including: IEEE ICDM'18 Tao Li Award for Excellence in Research IEEE ICDM'18 Best Paper Award UdS-CS'15 Busy Beaver Teaching Award ACM SIGKDD'11 Best Student Paper Award ACM SIGKDD'10 Doctoral Dissertation Runner-Up Award ECML PKDD'09 Best Student Paper Award As an advisor, Dr. Vreeken has mentored numerous doctoral researchers to completion, including Dr. Osman Ali Mian, Dr. David Kaltenpoth, Dr. Boris Wiegand, Dr. Sebastian Dalleiger, Dr. Janis Kalofolias, Dr. Jonas Fischer, Dr. Alexander Marx, Dr. Panagiotis Mandros, Dr. Kailash Budhathoki, Dr. Roel Bertens, Dr. Koen Smets, and Dr. Michael Mampaey. He has secured significant research funding as PI for multiple projects including "AI for Prediction and Therapy Guidance in Acute Stroke" (HAICU, 2025-2028), "Neuro-Explicit Models of Language, Vision and Action" (RTG, DFG, 2023-2028), and "Crushing Antimicrobial Resistance using Explainable AI" (HAICU, 2021-2024). Dr. Vreeken leads the Exploratory Data Analysis (EDA) research group at CISPA, which focuses on developing theory and algorithms for discovering novel insights from data, learning inherently interpretable models, and drawing reliable causal conclusions. The group has produced numerous influential algorithms and frameworks in causal inference, pattern mining, and exploratory data analysis, with applications spanning healthcare, materials science, and cybersecurity.

• Programming Languages
• Software Engineering
• Formal Methods
• +۳ مورد دیگر

L. Thomas van Binsbergen is a Professor in the Department of Computer Science & Computer Engineering at the University of Wisconsin-La Crosse, affiliated with the College of Science & Health. His work focuses on language design, formal methods, and policy-based systems. His research explores Executable formal specifications of programming languages Modular meta-language frameworks (e.g., iCoLa+, eFLINT) Data-dependent grammars for network protocols Purpose-based access control derived from GDPR Functional parsing algorithms (GLL, Happy-GLL) Policy enforcement in distributed systems Recent publications (2020-2025) demonstrate trends in language parametric design, security policy formalization, and exploratory programming environments. Notable collaborations include Damian Frölich, Tim Müller, and Tom M. van Engers. Van Binsbergen earned his PhD from Royal Holloway, University of London (2019) and contributes to conferences like GPCE, SLE, and workshops on programming language theory and data security.

René Westerholtمشاهده پروفایل

استادیار

• Spatial Analysis
• Place-Based Research
• Geospatial Statistics
• +۳ مورد دیگر

René Westerholt is a Juniorprofessor (Assistant Professor) at the Department of Spatial Planning of TU Dortmund University, Germany. His research focuses on statistical methods for spatiotemporal and place-based analysis, including spatial autocorrelation, hotspot detection, and spatial regression. He also specializes in formalizing place-based information and its integration with urban analytics. Education : Ph.D. in Geography (2013-2018), Heidelberg University M.Sc. Geoinformatics (2010-2012), Osnabrück University B.Sc. Geoinformatics (2007-2010), Osnabrück University Research Themes : Spatial analysis (user-generated data, spatial weights, autocorrelation) Place-based formalization (perception, geometric footprints) Empirical geographical studies (urban heat exposure, mobility services) Teaching : Digital Geographies, Spatial Statistics, Computer Science for Planners, and Urban Analytics at TU Dortmund University. He has also taught nationally and internationally in German and English. Awards : Dissertation Award Geoinformatics (2019) CDRC Data Challenge First Prize (2018) Young Researchers Award (2014) Contact : Office at TU Dortmund University, Room GBIII/318. Email: rene.westerholt@tu-dortmund.de

Holger Schwarzمشاهده پروفایل

دانشیار

• Data Management
• Database Systems
• Data Lakes and Lakehouses
• +۶ مورد دیگر

Holger Schwarz is an Associate Professor (Apl. Professor) at the Institute for Parallel and Distributed Systems (IPVS) within the Faculty of Computer Science, Electrical Engineering and Information Technology at the University of Stuttgart. He serves as Head of the Infrastructure Department and is actively involved in research and teaching in the areas of data management, database systems, and data analytics. Professor Schwarz earned his doctorate (Dr. rer. nat.) from the University of Stuttgart in 2003 with a dissertation on "Integration of Data Mining and Online Analytical Processing." He later completed his habilitation (Dr. rer. nat. habil.), qualifying him as a university professor in Germany. His primary research interests focus on data management systems , particularly in the domains of data lakes, lakehouses, enterprise data platforms, and metadata management. Professor Schwarz investigates how to design efficient and scalable data architectures that support modern analytical workloads while addressing challenges in data integration, governance, and democratization. His work bridges theoretical database concepts with practical industrial applications, as evidenced by numerous collaborations with industry partners. Professor Schwarz's recent publications demonstrate a clear trend toward enterprise data management solutions, particularly focusing on data lakehouse architectures, enterprise data marketplaces, and advanced clustering techniques. His research shows a consistent pattern of addressing real-world data management challenges through innovative architectural patterns and algorithmic improvements, with strong emphasis on practical industrial implementation. Professor Schwarz supervises numerous research projects including MetaMan (metadata management in complex data landscapes), DLArchitecture (design of comprehensive data lake architecture), INTERACT (interactive rapid analytic concepts), and VALID-Partition (improving prediction quality using domain knowledge). He also coordinates the University of Stuttgart's projects within the Software Campus initiative and serves as Managing Director of the Technology Partnership Lab and as a Member of the Board of Directors of the Industrial Data Lab. His teaching portfolio includes courses on Advanced Information Management, Database Systems, and Data Science projects across multiple semesters, demonstrating his commitment to educating the next generation of data management professionals.

Christian Winklerمشاهده پروفایل

استاد

• Artificial Intelligence
• Natural Language Processing
• User Experience
• +۴ مورد دیگر

Christian Winkler is Professor for AI-based UX optimization and general business administration at Nuremberg Institute of Technology since 2022. With over 25 years of experience spanning entrepreneurship, enterprise architecture, and academia, he brings substantial industry expertise to his academic role. His career includes founding multiple technology companies including an internet service provider (WWL Internet AG) that went public in 1999, Querplex GmbH through management buyout in 2003, and datanizing GmbH as an NLP SaaS provider in 2017. Professor Winkler's research focuses on practical applications of artificial intelligence in business contexts, with particular expertise in natural language processing, user experience optimization, and data-driven marketing strategies. His work bridges theoretical AI research with real-world business applications, emphasizing accessibility of complex technologies. He has published extensively on language models (including BERT and LLaMA implementations), text analysis techniques, and social media data analysis for business insights. Recent publications reveal a strong trend toward optimizing large language models for practical deployment, with significant focus on analyzing user-generated content from social platforms like Instagram and WallStreetBets. His work demonstrates how NLP can extract valuable business intelligence from unstructured data sources while making advanced AI techniques accessible to non-technical business professionals. Winkler teaches E-Commerce, International Marketing Tools - Quantitative Methods, Applied User Experience, and Communication Management, reflecting his interdisciplinary approach that combines technical AI knowledge with business administration expertise. He is an active contributor to the data science community through conference presentations at events like m3 Konferenz, MLsummit, and data2day, as well as educational content for Heise Academy on Python and NLP topics.

Tim Kacprowskiمشاهده پروفایل

استاد

• Medical Informatics
• Data Science
• Network Medicine
• +۹ مورد دیگر

Summary Prof. Tim Kacprowski is a Professor and Head of Data Science in Biomedicine at the Peter L. Reichertz Institute for Medical Informatics (PLRI), jointly affiliated with TU Braunschweig and Hannover Medical School. His research focuses on integrating computational methods with biomedical challenges, particularly in network medicine, federated learning, and alternative splicing analysis. Key projects include the development of the NeDRex platform for drug repurposing and the FeatureCloud framework for privacy-preserving federated learning in healthcare. Research Interests His work spans multiple domains: Network Medicine: Leveraging molecular networks for disease module identification and drug discovery. Data Science: Advanced analytics for biomedical data, including ECG monitoring, microbiome studies, and flow cytometry. Federated Learning: Developing decentralized AI systems to protect patient data while enabling collaborative research. Alternative Splicing: Investigating splicing patterns in diseases like cancer and kidney disorders. Publications Trends Recent publications emphasize tools for drug repurposing (NeDRex-Web), ethical AI in clinical decision-making, and microbiome dynamics in chronic diseases. His work bridges computational methods with clinical applications, addressing challenges in precision medicine and healthcare technology. Labs & Collaborations As head of the Data Science group at PLRI, he leads interdisciplinary teams advancing biomedical informatics. Collaborations span institutions in Germany and internationally, focusing on translational research and AI-driven healthcare solutions.

Siegfried Nijssenمشاهده پروفایل

استادیار

• Data Mining
• Pattern Mining
• Exploratory Data Analysis
• +۵ مورد دیگر

Siegfried Nijssen is an Assistant Professor of Data Mining and Artificial Intelligence at the Catholic University of Louvain (UCLouvain) in Belgium, working within the ICTEAM research institute's Artificial Intelligence and Algorithms group. He has been at UCLouvain since 2016, previously serving as an Assistant Professor at University Leiden (2012-2016) and completing postdoctoral work at KU Leuven (2006-2015). He earned his PhD in Computer Science from University Leiden in 2006. His research focuses on making data analysis simpler through intersections between pattern mining, exploratory data analysis, and programming paradigms in Artificial Intelligence, particularly constraint programming and probabilistic programming. He has developed techniques for analyzing diverse data types including graphs, networks, and multi-relational data. His work bridges theoretical foundations with practical applications in decision tree learning, probabilistic networks, and source code analysis. Nijssen's recent publications demonstrate a strong focus on optimal decision trees, constraint-based pattern mining, and applications in bioinformatics and education. His research shows consistent evolution from foundational graph mining work (including the Gaston algorithm developed in 2004) to current work integrating machine learning with constraint programming for interpretable AI solutions. As an educator, he teaches courses including Mining Patterns in Data, Databases, and Artificial Intelligence and Machine Learning seminars at UCLouvain. He has advised numerous PhD students and postdocs, primarily in collaboration with Pierre Schaus, with former students like Tias Guns now holding professorships.

Jilles Vreekenمشاهده پروفایل

استاد

• Causal Inference
• Machine Learning
• Data Mining
• +۵ مورد دیگر

Prof. Dr. Jilles Vreeken is tenured faculty at the CISPA Helmholtz Center for Information Security , where he leads the Exploratory Data Analysis group. He also serves as an Honorary Professor at Saarland University . Research focuses on causal inference, machine learning, and data mining Develops unsupervised methods for robust, interpretable models PI on grants like HAICU's Neuro-Explicit Models and Crushing Antimicrobial Resistance His recent work spans causal discovery in non-stationary time series ( SPACETIME ), federated binary matrix factorization, interpretable neural search patterns, and data modification rule mining from event logs. He applies information-theoretic approaches to address hidden confounding, selection bias, and multi-environment causal modeling. Key trends in his publications include: Integrating causal inference with machine learning via algorithmic Markov conditions Advancing federated learning for privacy-preserving causal discovery Creating interpretable pattern mining frameworks for graphs, sequences, and high-dimensional data Developing MDL-based methods for reliable dependency and rule discovery Scientific Recognition: 2018 - IEEE ICDM Tao Li Award 2018 - IEEE ICDM Best Paper 2015 - UdS-CS Busy Beaver Teaching Award 2011 - ACM SIGKDD Best Student Paper 2010 - ACM SIGKDD Doctoral Dissertation Runner-Up 2009 - ECML PKDD Best Student Paper As an educator, he has supervised 15+ PhD/MSc students and taught courses like Topics in Algorithmic Data Analysis and Information-Theoretic Machine Learning . His research group pioneers methods for trustworthy information processing and causal anomaly detection , with applications in materials science, epidemiology, and cybersecurity.

• Database Systems
• Data Mining
• Social Network Analysis
• +۳ مورد دیگر

Eric Leclercq is a researcher at the University of Burgundy, affiliated with the LE2I Lab in Dijon, France. His work spans database systems, social network analysis, and biomedical data integration. He has contributed extensively to polystore systems, tensor decompositions, and category theory applications in data modeling. Fields of Interest : Database Systems, Data Mining, Social Network Analysis, Big Data Analytics, Semantic Web Leclercq's recent research focuses on formal frameworks for data lakes using category theory, multi-level tensor decomposition for social network stratification, and schema migration in multi-model systems. He has published in venues like CAiSE, IDEAS, and RCIS. His collaborations include Annabelle Gillet, Marinette Savonnet, and Nadine Cullot. Notable works include Lambda+ architecture for data processing, polarization analysis in social networks, and tools for tweet collection and biomedical data integration.

• Human-Computer Interaction
• Artificial Intelligence
• Natural Language Processing
• +۳ مورد دیگر

Joseph Chee Chang is a researcher at Carnegie Mellon University's Human-Computer Interaction Institute. His work focuses on Human-Computer Interaction Artificial Intelligence Natural Language Processing He develops tools for scholarly synthesis, literature review, and research ideation. Research Highlights Chang designs mixed-initiative systems like IdeaSynth for research idea development and Social-RAG for socially grounded AI generation. His PaperWeaver system recontextualizes academic recommendations, while Qlarify offers hierarchical paper summaries. Current projects include SciArena (2025) for evaluating scientific foundation models and PaperMage (2023) for document processing.

Christoph Kinkeldeyمشاهده پروفایل

مدرس

• Data Visualization
• Visual Analytics
• Uncertainty Visualization
• +۳ مورد دیگر

Prof. Christoph Kinkeldey is a Lecturer at Hamburg University of Applied Sciences, affiliated with the Department of Information, Media and Communication within the Faculty of Design, Media and Information. He holds a doctorate in Geoinformatics from HafenCity University Hamburg (2015) and has conducted research globally, including at PennState University, University of Melbourne, and Inria. His work focuses on data visualization, visual analytics, and uncertainty visualization, emphasizing how visual tools aid decision-making in complex data environments. Education: PhD in Geoinformatics, HafenCity University Hamburg (2015) Research Interests: Interactive visual data analysis Uncertainty communication in visualizations Blockchain data exploration (e.g., Bitcoin network analysis) Evaluation of visualization techniques His research bridges cartography, computer science, and human-centered design to empower diverse stakeholders in understanding complex information. Publications: Recent work emphasizes uncertainty visualization for data analysts, machine learning interpretability, and blockchain analytics. Key contributions include the BitConduite tool for Bitcoin network analysis and participatory design methods for non-technical users. Awards: 2016 VAST Mini Challenge 2: Honorable Mention for Clear Analysis Strategy Advising & Collaboration: Currently on parental leave until August 2024, he collaborates with the gicentre (City, University of London) and Monash University’s Department of Human-Centered Computing. His research teams focus on interdisciplinary projects merging visualization theory with practical applications. Labs/Teams: Active in the gicentre’s visualization initiatives and Monash’s Human-Centered Computing group, contributing to open-source tools and international research networks.