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Lei ChenView profile

Professor

• Data-Driven Machine Learning
• Crowdsourcing-based Data Processing
• Web Data Management
• +6 more

Lei Chen is a Chair Professor at the Department of Computer Science and Engineering , Hong Kong University of Science and Technology (HKUST) , where he serves as Director of the Big Data Institute . He received his PhD from the University of Waterloo after completing BS and MS degrees at Tianjin University and Asian Institute of Technology , respectively. As an IEEE Fellow and ACM Distinguished Scientist , his research spans Data-Driven Machine Learning , Crowdsourcing-based Data Processing , Uncertain Databases , and Knowledge Graphs . His work on graph neural networks , spatial crowdsourcing , and privacy-preserving systems has earned multiple best paper awards at top venues like VLDB , SIGMOD , and KDD . Current research focuses on agentic neural graph databases and large language model optimization . His 15 most recent publications (2022-2025) address: Secure spatial query systems (Hu-Fu) Decentralized graph neural network training (Sancus) Explainable AI frameworks Temporal graph learning Polynomial activation functions for weather forecasting Knowledge graph applications in recommender systems Scientific Awards: VLDB Best Research Paper (2022) SIGMOD Test-of-Time Award Google Faculty Award (2013) Multiple conference best paper awards He has supervised 40+ PhD/MPhil students , many of whom now hold academic/research positions at institutions like BeiHang University , Sun Yat-sen University , and Huawei Noah's Ark Lab . His group ( Database Research Group ) leads projects in graph neural networks , knowledge graphs , and blockchain systems .

Sergio GrecoView profile

Professor

• Database Theory
• Data Integration
• Inconsistent Data Processing
• +3 more

Sergio Greco is Full Professor at University of Calabria's DIMES Department, Coordinator of the Computer Engineering Degree, and Vice-president of the Computer Engineering national Group. His research spans database theory, data integration, inconsistent data processing, and computational logic. Research Focus: Development of theoretical frameworks for data management including argumentation frameworks, knowledge base querying under inconsistency, and decentralized learning systems. Recent work integrates machine learning with formal reasoning methods. Honors: Best Paper Award at International Conference on Logic Programming (2020) Best Paper Award at RuleML Symposium (2014) Leads projects on cybersecurity and data management funded by EU and Italian Ministry of Research, coordinating national and international research groups.

Bin YangView profile

Professor

• Computer Science
• Data Management
• Machine Learning
• +9 more

Bin Yang is a Professor at the Department of Computer Science, Aalborg University, affiliated with The Technical Faculty of IT and Design. His research focuses on data engineering, artificial intelligence, and machine learning, with emphasis on spatiotemporal data analysis, representation learning, and time series forecasting. He leads projects like aSTEP (spatio-temporal data analytics) and Light-AI for Cognitive Power Electronics. Key contributions include advancements in trajectory data processing, anomaly detection, and lightweight neural architectures. Research interests span machine learning, data mining, intelligent transport systems, and material science applications. Notable awards include the IJCAI 2019 Distinguished PC Member and Sapere Aude Research Leader (2018). He has supervised six PhD students and contributed to over 119 publications. His work is supported by grants from Villum Foundation and EU initiatives. Yang is involved in interdisciplinary collaborations, including data-driven decision-making frameworks and environmental monitoring systems. Labs and teams include the Daisy Center for Data-intensive Systems and AI for the People initiatives. Projects emphasize real-world applications in smart cities, traffic forecasting, and sustainable technologies. His research bridges theoretical foundations with practical implementations in domains like oceanography and crowdsourcing systems.

Alon EfratView profile

Associate Professor

• Computational Geometry
• Algorithms
• Sensor Networks
• +8 more

Alon Efrat is an Associate Professor in the Department of Computer Science at the University of Arizona, College of Engineering. He has been an active researcher and educator, contributing significantly to computational geometry, algorithms, sensor networks, and multimedia systems. Research Interests: His work spans computational geometry, geometric algorithms, sensor and wireless networks, UAV motion planning, video browsing systems, and location-based services. He has developed algorithms for network resilience, data mules, and secure communication using jamming. His interdisciplinary research includes applications in healthcare, pandemic modeling, and educational technology. Scientific Contributions: NSF CAREER Award recipient (2004) Editorial board member for International Journal of Computational Geometry & Applications (IJCGA) and Journal of Discrete Algorithms (JDA) Program committee member for major conferences including SoCG, INFOCOM, FOCS, and ACM GIS Active participant in organizing workshops such as ALGOSENSORS and SoCG Advising and Grants: Advises graduate students in algorithms and systems, including Jesus Arango, Swaminathan Sankararaman, and Qiyam Tung Research funded by NSF (CAREER, CCF, CNS), ACIST, and Honda Research Lab Co-PI on multiple interdisciplinary projects involving network security, mobile health, and UAVs Recent Research Trends: His recent publications show a strong focus on uncertainty in data (nearest-neighbor search under uncertainty), geometric optimization for security (jamming, network resilience), and applications of computational geometry to real-world problems such as pandemic modeling, healthcare in remote regions, and terrain sweeping with UAVs. The work blends theoretical depth with practical implementation, often involving interdisciplinary collaboration. Labs and Projects: Lead of the SLIC video browsing system Involved in UAV-based terrain surveillance and data mule networks Collaborates with teams in Brazil (Codpon Project) and on Amazon-region healthcare

Ihab Francis IlyasView profile

Professor

• Data Science
• Data Management
• Machine Learning for Data Quality
• +4 more

Ihab Francis Ilyas is a Professor at the University of Waterloo , affiliated with the Cheriton School of Computer Science . He currently holds the Thomson Reuters Research Chair in Data Quality and is on leave while serving as a Distinguished Engineer, Proactive Intelligence at Apple Inc. . He has co-founded two successful startups— Inductiv (acquired by Apple) and Tamr —and is a Fellow of the Royal Society of Canada , IEEE Fellow , and ACM Fellow . Research Interests : AI for Data Quality and Curation Knowledge Graphs Large-Scale Data Integration Information Extraction Managing Uncertain Data Data Cleaning Error Detection and Repair Probabilistic and Uncertain Data Management Scientific Awards and Recognitions : C.C. Gotlieb Computer Award, 2024 IEEE Fellow, 2021 ACM Fellow, 2020 NSERC-Thomson Reuters Industrial Research Chair, 2018 Google Faculty Award, 2014 Ontario Early Researcher Award, 2008 IBM CAS Faculty Fellow, 2006–2010 Taha Hussein Medal (Egyptian Ministry of Education), 1990 Leadership and Service : Board of Trustees, VLDB Endowment (2016–2021) Vice Chair, ACM SIGMOD (2016–2021) Co-founder, Inductiv (acquired by Apple) and Tamr Co-author of the leading text Data Cleaning (ACM Books) Lead developer of the HoloClean open-source data repair system Contributor to Saga , a next-generation knowledge construction platform at Apple Publications and Trends : His recent research focuses on AI-driven data cleaning, knowledge graph construction, and scalable data integration systems. Collaborative works span probabilistic inference, differentially private data synthesis, error detection, and HTAP workloads. His publications appear in top venues like SIGMOD , VLDB , and ICDE .

• Web Data Management
• Probabilistic Databases
• Provenance in Databases
• +4 more

Pierre Senellart is a Professor in the Computer Science Department at École normale supérieure (ENS, Université PSL) and deputy director of the DI ENS laboratory, a joint CNRS/Inria/ENS unit. He leads the Valda team at Inria Paris and holds a chair in the PRAIRIE Paris School of AI. University: École normale supérieure (Université PSL) School: Department of Computer Science (DI ENS) Academic Rank: Professor Research Interests: His work bridges theoretical and practical aspects of web data management, including web crawling, archiving, information extraction, uncertainty management, and intensional data systems. He contributes to probabilistic XML data models, provenance tracking, and knowledge base construction. Recent Article Trends: His Google Scholar publications (2025–2023) focus on provability in probabilistic databases, theorem extraction from PDFs, and AI applications in uncertain data analysis. Topics span game theory, knowledge compilation, and multi-modal learning for scientific documents. Scientific Awards: Junior member of Institut Universitaire de France (2020–2025) Distinguished SIGMOD 2017 Program Committee Member ACM HyperText 2014 Douglas Engelbart Best Paper Award Adviser of Clément Genzmer (SIGMOD Programming Contest 2009 winner) Grants & Projects: He secures major funding (e.g., PRAIRIE Paris School of AI: €575k, DesCartes CNRS@CREATE: SGD50M), with roles in research projects like Dissemin (open science platforms) and ARCOMEM (social web archiving).

Talel AbdessalemView profile

Professor

• Database Systems
• Data Mining
• Machine Learning
• +4 more

Talel Abdessalem is a Professor at Télécom Paris , where he has held leadership roles including Director of LTCI Research Laboratory (since 2017) and Dean of Research (since 2018). He currently serves as Deputy Vice-President for Research at Institut Polytechnique de Paris . PhD in Computer Science from Paris-Dauphine University Habilitation (HDR) from UPMC-Sorbonne University His research spans large-scale data management , recommender systems , social network analysis , and uncertain data modeling . He has participated in numerous national (ANR, FEDER) and European (FP7) research projects, with recent work focusing on stream-based learning , graph analytics , and privacy-preserving systems . His publications include diverse contributions to directed graph centrality algorithms (2021), stream recommender frameworks (River, Scikit-Multiflow), and geospatial recommendation models (ALGeoSPF). He has supervised 14 PhD students and collaborates with researchers in France, Brazil, and Indonesia. Co-leads DIG (Data, Intelligence and Graphs) research team Director of LTCI (Information Processing and Communication) laboratory

• Data Provenance
• Database Systems
• Data Cleaning
• +5 more

Boris Glavic is a Professor at Illinois Institute of Technology, Chicago, specializing in database systems with a strong research focus on data provenance, uncertain data management, and database optimization techniques. His work bridges theoretical database concepts with practical applications in data cleaning, debugging, and ML integration. Glavic's research primarily centers on data provenance, where he has developed innovative techniques for tracking data lineage, optimizing provenance computations, and applying provenance to various database tasks. His work spans theoretical foundations of provenance in database query languages to practical systems like GProM (a 'Swiss Army Knife' for provenance needs) and applications in data debugging, uncertain data management, and ML pipeline robustness. Key contributions include provenance-based data skipping, reenactment techniques for transaction debugging, and frameworks for explaining query answers and non-answers. Analysis of Glavic's recent publications reveals a clear evolution from foundational provenance research toward integration with machine learning systems and addressing data quality challenges. His work increasingly focuses on practical applications where provenance techniques enhance data reliability in ML pipelines, improve debugging of complex data workflows, and enable efficient handling of uncertain and incomplete data. The publications demonstrate strong interdisciplinary connections between database theory, data management systems, and machine learning. While specific awards aren't documented in the provided information, Glavic's extensive publication record in top-tier venues (VLDB, SIGMOD, ICDE) over more than a decade demonstrates significant recognition within the database research community. His work has clearly influenced both theoretical and practical aspects of data management systems. Glavic has mentored numerous researchers who have become frequent collaborators, including Seokki Lee, Xing Niu, Su Feng, and Pengyuan Li. His research has been supported by grants enabling substantial contributions to data provenance, database debugging, and uncertain data management. The collaborative nature of his work is evident through extensive co-authorship networks spanning multiple institutions and research groups. Though specific lab affiliations aren't detailed in the provided information, Glavic's research appears to be conducted within a vibrant database research group at Illinois Institute of Technology, with strong connections to other leading database research centers. His work on systems like GProM suggests an active research laboratory focused on practical database tools and techniques.

Aleksandr PopovView profile

Researcher

• Computational Geometry
• Algorithms
• Trajectory Analysis
• +2 more

Aleksandr Popov is a researcher in computational geometry currently affiliated with the ALGO cluster at Eindhoven University of Technology. He completed his PhD in 2023 focusing on algorithms for uncertain trajectories and continues active research in this domain. Education: PhD in Algorithms for Imprecise Trajectories, Eindhoven University of Technology, 2023 Master's in Similarity of Uncertain Trajectories, Eindhoven University of Technology, 2019 Dr. Popov's research centers on computational geometry with emphasis on trajectory analysis under uncertainty . His work develops algorithms to quantify trajectory similarity using Fréchet distance, simplify complex paths while preserving essential features, and handle imprecise spatial data. His research bridges theoretical computer science with practical applications in movement pattern analysis and spatial databases, addressing fundamental challenges in how uncertainty affects geometric computations. Analysis of his publication record reveals a systematic progression from theoretical foundations of uncertain curve analysis to more applied problems like map-matching and trajectory clustering. His work consistently explores how measurement uncertainty impacts geometric algorithms, developing specialized methods that maintain computational efficiency while accounting for data imprecision. A distinctive feature of his research is the adaptation of classical geometric measures like Fréchet distance to probabilistic and uncertain settings. Dr. Popov has substantial teaching experience, having assisted with bachelor courses on data structures and algorithms (2021-2023), supported master seminars on algorithms (2019-2021), and served as a student assistant for various courses during his bachelor and master studies (2014-2019). He also contributed as an editorial assistant for Dagstuhl Seminar reports on computational geometry, demonstrating engagement with the broader academic community. As an active member of the ALGO cluster at Eindhoven University of Technology, Dr. Popov collaborates with leading researchers including Kevin Buchin, Maarten Löffler, and Marcel Roeloffzen, contributing to a vibrant research environment focused on theoretical and applied aspects of algorithm design for geometric problems.

• Data Science
• Artificial Intelligence
• Algorithm Design
• +7 more

Bojie Shen is a Research Fellow in the Department of Data Science & AI at Monash University. His research lies at the intersection of artificial intelligence, data science, and algorithmic transportation systems, with a focus on efficient and eco-friendly route planning for electric vehicles, public transport, and multi-agent systems. His primary research interests include: Artificial Intelligence and Multi-Agent Path Finding Graph Algorithms and Route Planning Energy-Efficient Navigation for Electric Vehicles Public and Multi-Modal Transport Routing Combinatorial Search and Optimization Sustainable Mobility and Green Logistics The analysis of his recent publications reveals a consistent trajectory in developing scalable, exact, and energy-aware routing algorithms. His work frequently appears in high-impact venues such as IEEE Transactions on Intelligent Transportation Systems and AAAI conferences, demonstrating strong technical rigor and relevance to real-world transportation challenges. A significant portion of his research contributes to UN Sustainable Development Goals related to sustainable cities and climate action. Notable scientific contributions include: Taxonomy and survey of eco-friendly route planning algorithms Efficient public transport routing using transfer connection databases Advancements in multi-agent path finding beyond pairwise constraints Exact and dynamic shortest path computation for electric vehicles Dr. Shen actively collaborates with leading researchers including Muhammad Aamir Cheema, Daniel Harabor, and Peter Stuckey. While no formal advising or grant information is available in the text, his publication volume and co-authorship network suggest active involvement in funded research projects and potential mentorship roles within the department. He is affiliated with a vibrant research group in data science and AI at Monash University, contributing to advancements in intelligent transportation systems and combinatorial search algorithms.

Sunil PrabhakarView profile

Professor

• Data Management
• Database Security
• Probabilistic Databases
• +2 more

Sunil Prabhakar serves as Vice Provost for Faculty Affairs and Professor of Computer Science at Purdue University, having joined the institution in 1998. He held leadership roles including Head of the Computer Science Department from 2010 and inaugural Director of the Integrative Data Science Initiative in 2019. His research centers on data management and security , with specialized focus on developing database systems for probabilistic and uncertain data while ensuring authenticity and integrity of outsourced databases . This work addresses critical vulnerabilities in cloud-based data storage where trust assumptions are compromised. As Department Head, he spearheaded Purdue Moves expansion initiatives that drove record growth in enrollments, faculty hires, and revenue. This strategic leadership directly enabled creation of new undergraduate majors in Artificial Intelligence and Data Science , alongside a professional master's program in Cybersecurity .

Sanjay KrishnanView profile

Assistant Professor

• Data Science
• Databases
• Machine Learning
• +5 more

Sanjay Krishnan is an Assistant Professor of Computer Science at the University of Chicago. His research focuses on managing uncertain data in databases and information systems, including accuracy guarantees in incomplete databases, query evaluation under corruption, and data leakage detection. He leads the ChiDATA research group within the University of Chicago's Department of Computer Science, collaborating with researchers like Michael Franklin and Raul Castro Fernandez on large-scale data analysis and systems. His academic work spans foundational areas such as AI/ML integration with data systems, scalable data processing, and resource-efficient database design. He is affiliated with the CERES Center for Unstoppable Computing and the Systems Group, exploring resilient computing systems and software engineering challenges. His lab's research has been featured in UChicago CS news for innovations like geometric database query processing and interdisciplinary collaborations with the Museum of Science & Industry. Key contributions include frameworks like EdgeServe for decentralized model serving, CrocodileDB for resource-efficient query execution, and ActiveClean for interactive data cleaning in ML pipelines. His work bridges theoretical computer science with practical system implementations, emphasizing real-world data challenges in IoT, healthcare, and environmental monitoring.

Ruoming JinView profile

Associate Professor

• Data Mining
• Database
• Biomedical Informatics
• +5 more

Ruoming Jin is an Associate Professor in the Computer Science Department at Kent State University. He earned his Ph.D. from Ohio State University in 2005 and is based at the Kent campus. His office is located in MCS 264, and he can be reached at rjin1@kent.edu or (330)672-9063. Ph.D., Ohio State University, 2005 Dr. Jin's research focuses on Data Mining, Database Systems, Biomedical Informatics, and Cloud Computing, with a particular emphasis on Big Data and Graph Data analysis. His work develops novel algorithmic and system solutions for managing and analyzing massive graph and network data. His research has significant applications in semantic web, social networks, biomedical informatics, and transportation systems, bridging theoretical foundations with practical implementations. His publication record demonstrates a strong trajectory in graph data management, with recent work spanning network science, uncertain graphs, and graph databases. Key themes include reachability computation, distance queries, subgraph discovery, and privacy-preserving techniques, showing both theoretical depth and practical applicability across multiple domains. While specific details about advising and grants aren't provided in the available information, Dr. Jin maintains an active research program with consistent high-impact publications in top venues including KDD, SIGMOD, VLDB, and ICDM, indicating ongoing research projects and likely supervision of graduate students. Dr. Jin leads a research group dedicated to developing solutions for managing and analyzing massive graph and network data. His team works on both theoretical algorithms and practical system implementations, focusing on scalability challenges presented by modern big graph data applications across various domains.

• Big Data
• Probabilistic Databases
• Information Extraction
• +7 more

Prof. Martin Theobald is a Full Professor in Computer Science with a focus on Big Data at the University of Luxembourg's Faculty of Science, Technology and Medicine (FSTM), Department of Computer Science. He leads the Big Data research group and serves as the Study Program Director for the Bachelor in Computer Science (BiCS) program since 2022. Previously, he held professorial roles at the University of Ulm (Co-Director of the Institute for Databases and Information Systems) and the University of Antwerp (Advanced Database Research and Modeling group). His academic journey includes a joint doctoral degree from Saarland University and the Max Planck Institute for Informatics in 2006, followed by a postdoctoral research stint at Stanford University's Infolab from 2006 to 2008. Education: PhD in Computer Science (2006) from Saarland University and Max Planck Institute for Informatics, Saarbrücken Postdoctoral Researcher at Stanford University's Infolab (2006–2008) Research Interests: Martin's work centers on Big Data systems, probabilistic databases, distributed stream processing, and machine learning. He has extensively explored techniques such as asynchronous iterative routing (AIR), real-time neural network training from data streams (TensAIR), and large-scale information extraction leveraging statistical methods. His contributions span scalable architectures, optimization frameworks for deep learning, and knowledge graph construction with high precision and recall. Publications: His recent work reflects a focus on real-time systems, convergence analysis of distributed algorithms, and robust embedding techniques. Key themes include the development of efficient dataflow engines, probabilistic database models, and frameworks for handling uncertain and temporal data. Scientific Awards: None explicitly mentioned in the provided texts. Advising & Grants: Martin has advised numerous students (though names are not listed) and contributed to over 100 international conferences/workshops as a PC member, reviewer, and organizer. He has held editorial roles, including Area Editor for Elsevier’s 'Information Systems' (2013–2018) and PVLDB (2020–2021). His research has driven advancements in Jeopardy! clue-solving SPARQL engines, automotive navigation systems under uncertainty, and the design of open-domain information extraction architectures. Labs/Teams: Current leadership of the Big Data research group at the University of Luxembourg. Previously led the 'Ranking and Uncertain Data Management' group at the Max Planck Institute for Informatics (2008–2012). Key collaborations include the development of the AIR dataflow engine and the YAGO-QA knowledge base system.

• Mathematical Economics
• Stochastic Analysis
• Model Uncertainty
• +5 more

Fabian Fuchs is a Researcher and doctoral candidate at Universität Bielefeld, affiliated with the Faculty of Mathematics and the Faculty of Business Administration and Economics. He is a member of the Institute for Mathematical Economic Research (IMW) and contributes to the Collaborative Research Centre SFB 1283, project C7: 'Markovian dynamics under model uncertainty'. He is also associated with the Bielefeld Graduate School of Economics and Management (BIGSEM) and the Bielefeld Graduate School in Theoretical Sciences, indicating a strong interdisciplinary focus on theoretical and mathematical approaches to economic problems. His research interests center on mathematical economics and stochastic analysis, particularly in the context of model uncertainty and Markovian processes. These areas are fundamental for understanding dynamic systems in economics and finance where future states are uncertain and depend probabilistically on current conditions. His work within SFB 1283 suggests a deep engagement with cutting-edge methods for taming uncertainty in complex analytical models. Fabian Fuchs is actively pursuing his doctoral studies while contributing to significant research projects. He is based in office UHG B4-122 and is part of a vibrant research environment that fosters interdisciplinary collaboration between mathematics and economics. His current work contributes to the university's strategic research focus on the 'Mathematical World' and 'Quantifying and Managing Uncertainty' (QUAMU). No scientific awards or publications are currently listed in the public database. His email address is not publicly accessible and requires university login credentials.