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Georgia KoloniariView profile

Associate Professor

• Social Networks
• Graph Databases
• Distributed Systems
• +4 more

Georgia Koloniari is an Associate Professor at the Department of Applied Informatics, School of Information Sciences, University of Macedonia. She holds a PhD in Computer Science from the University of Ioannina (2009), along with an MSc (2003) and BSc (2001) in Computer Science from the same institution. Her research focuses on distributed systems, graph databases, and peer-to-peer (P2P) systems, with notable contributions to social network analysis, XML data management, and privacy-preserving techniques. She has led or participated in multiple funded research projects, including the Cloud9 project (2011–2014) and the Self-Peer initiative (2005–2008). Her teaching roles include instructing courses such as Distributed Systems , Data Structures , and Advanced Information Systems at the undergraduate level, and IT Infrastructure and Databases at the graduate level. She has also contributed to online educational initiatives, including the development of the Data Structures course in the Open Digital Courses project. Dr. Koloniari’s research interests span social network evolution, graph database management, and distributed data processing. She has published extensively on topics like clustered overlay networks, privacy-preserving record linkage, and temporal query processing. Her work integrates game-theoretic approaches and algorithmic solutions to address challenges in P2P systems and cloud computing. Her recent publications highlight advancements in privacy-preserving techniques (e.g., PRIVATEER toolkit) and time-aware social search systems. She remains active in academic service, serving on program committees for conferences like WWW and EDBT, and reviewing for journals such as IEEE Transactions on Parallel and Distributed Systems. Labs/Teams: Her involvement with the DMOD Lab (Digital Media, Data Management, and Distributed Systems Lab) reflects her focus on interdisciplinary research in data management and distributed computing.

Marcelo ArenasView profile

Professor

• Data Management
• Semantic Web
• Database Theory
• +3 more

Marcelo Arenas is a Professor at the Department of Computer Science and the Institute for Mathematical and Computational Engineering at the Pontifical Catholic University of Chile. He is a Fellow of the Association for Computing Machinery (ACM), former director of the Millennium Institute for Foundational Research on Data, and co-founder of the Center for Semantic Web Research. His Ph.D. in Computer Science was obtained from the University of Toronto in 2005. Research interests include data management , applications of logic in computer science , and Semantic Web technologies. He has published extensively on topics such as SPARQL query complexity , graph database systems , and incomplete database theory , with notable works like MillenniumDB and Foundations of Data Exchange . Scientific accolades: 2016 SWSA Ten-Year Award for "Semantics and Complexity of SPARQL" IBM Ph.D. Fellowship (2004) Nine Best Paper Awards across PODS, ISWC, ICDT, ESWC, WWW, and NeurIPS His work on approximate counting algorithms (e.g., FPRAS for #NFA) and explainable AI frameworks has influenced database theory, while serving on program committees for ICDT 2015, ISWC 2015, and PODS 2018 demonstrates leadership in the field. Current projects focus on probabilistic explanations for decision trees and temporal regular path queries in knowledge graphs.

• Microarchitectural Side-Channel Attacks
• System Security
• CPU Security
• +6 more

Dr. Michael Schwarz is a tenured faculty member at the CISPA Helmholtz Center for Information Security in Saarbrücken, Germany, where he leads the RootSec research group. Since 2020 he has been investigating microarchitectural side-channel attacks, system security, CPU security, transient-execution vulnerabilities, and corresponding defense mechanisms. He previously served as a post-doctoral researcher (2019–2020) and PhD student (2016–2019) at Graz University of Technology, after earning two master’s degrees in computer science and software engineering with a security focus. Education PhD, Graz University of Technology, 2019 — “Software-based Side-Channel Attacks and Defenses in Restricted Environments” (Advisor: Daniel Gruss) MSc, Computer Science, Graz University of Technology MSc, Software Engineering, Graz University of Technology Research Interests Dr. Schwarz’s work centers on microarchitectural side-channel attacks and system security , spanning: CPU security and transient-execution vulnerabilities (Meltdown, Spectre, Fallout, LVI, ZombieLoad, ÆPIC Leak, CacheWarp) Detection, mitigation, and formal guarantees against microarchitectural leakage Hardware–software co-design for secure operating systems and trusted execution environments Reverse engineering of CPU internals, cache architectures, and prefetchers Browser and web-platform security, including sandbox bypasses and fingerprinting Compiler security and constant-time programming enforcement Scientific Awards & Recognition 2022 Busy Beaver Award for “Foundations of Cybersecurity II” 2021 Busy Beaver Award for “Side-Channel Attacks and Defenses” 2020 EuroSys Roger Needham PhD Award 2019 IEEE S&P Distinguished Paper Award (Spectre) 2019 NSA Best Scientific Cybersecurity Paper Competition Honorable Mention (Meltdown) 2019 Open Exploit Award (Meltdown & Spectre) 2018 CSAW Best Paper Award (Meltdown) 2018 Pwnie Awards: Best Privilege Escalation Bug & Most Innovative Research 2023 USENIX Security Noteworthy Reviewer Award 2023 WOOT Best Paper Award (CustomProcessingUnit) 2022 Pwnie Award for Best Desktop Bug (ÆPIC) 2022 AI 2000 Security & Privacy Honorable Mention 2021 CCSW Best Paper Award & CSAW Best Paper 3rd Place Advising & Collaborations Dr. Schwarz currently mentors numerous PhD students and post-docs within the RootSec group. His collaborations extend across CISPA, Graz University of Technology, and an international network visible through extensive multi-author publications at top-tier venues including USENIX Security, IEEE S&P, ACM CCS, NDSS, ESORICS, DIMVA, ASPLOS, WWW, FC, and specialized microarchitecture security conferences. Labs & Teams He heads the RootSec Research Group at CISPA, whose core mission is to uncover, analyze, and mitigate microarchitectural and system-level security threats. The group maintains close links with industry partners, open-source communities, and policy makers to ensure that research findings are rapidly translated into practical defenses deployed in modern operating systems and processors.

• Database Systems
• Data Stream Processing
• Uncertain Data Management
• +4 more

Tingjian Ge is an academic researcher with a focus on database systems, graph stream processing, and uncertain data management. His work spans theoretical foundations and practical implementations in data science, with a career trajectory showing increasing specialization in real-time network analytics and secure data processing. PhD from Brown University (2009) Active in top-tier venues: ICDE, KDD, VLDB, WWW Research Interests include: Approximate query processing under resource constraints Graph stream modeling and edge shedding techniques Security and privacy in data systems Temporal network analysis and predictive modeling Optimization of multi-core and cloud-based query execution Scientific Contributions demonstrate expertise in: Developing sketch data structures for graph streams Creating fairness-aware prediction frameworks Advancing differential privacy mechanisms Designing efficient temporal subgraph algorithms

• Machine Learning
• Algorithm Engineering
• Combinatorial Optimization
• +3 more

Dr. Deepak Ajwani is an Assistant Professor in the School of Computer Science at University College Dublin . His research focuses on leveraging machine learning techniques for solving combinatorial optimization problems, with expertise in algorithm design, algorithm engineering, and graph algorithms. He holds a PhD from the University of Saarland, Germany, and has held postdoctoral positions at Aarhus University (Denmark) and University College Cork (Ireland). He also worked at Nokia Bell Labs (2012–2018) on learning systems for unstructured content analysis. Dr. Ajwani has received over 50 peer-reviewed publications in top-tier conferences and journals. He serves on the editorial board of the Machine Learning journal and is a frequent senior program committee member for conferences like WWW, IJCAI, AAAI, and ALENEX. He is a funded investigator at the SFI Centre for Research Training in Machine Learning (ML-Labs) . His teaching excellence has been recognized through multiple nominations for the UCD Teaching and Learning Award . His students have won the Franz Geiselbrechtinger Medal (2023, 2024) for best final-year projects in theoretical computer science. Key grants include the Irish Research Council and IBM Research Grant (2010–2012) for graph partitioning techniques in exascale computing. His research explores integrating machine learning with optimization algorithms, graph neural networks, and algorithm engineering for real-world applications.

Rudolf SchneiderView profile

Researcher

• Deep Learning
• Information Extraction
• Information Retrieval
• +3 more

Rudolf Schneider is a researcher in Data Science and Artificial Intelligence at Beuth University of Applied Sciences Berlin. He holds a PhD in Computer Science (2023) with focus on medical text mining and neural information retrieval. His academic roles included Research Assistant at DATEXIS and doctoral studies under Prof. Alexander Löser's supervision. Key research interests span Deep Learning applications in healthcare, information extraction from clinical notes, and neural retrieval systems. Current work involves BMWi-funded MACSS project for medical service solutions. Notable contributions include SECTOR (topic segmentation) and Smart-MD (medical paragraph retrieval) models. Publications highlight advancements in Open Information Extraction (OIE) benchmarking, relational database interactions, and NLP system evaluations. Active in conferences like ISWC and WWW. Organizational involvement includes heavy metal festival coordination and Chaos Computer Club participation.

• Blockchain Technology
• Cryptocurrency Forensics
• Decentralized Finance (DeFi)
• +4 more

Bernhard Haslhofer is a Professor at the University of Vienna, affiliated with the Department of Information Systems within the Faculty of Computer Science and Mathematics. His research focuses on blockchain technology, cryptocurrency analytics, and digital libraries, with a particular emphasis on forensic applications of blockchain, decentralized finance (DeFi), and semantic web technologies. He co-leads the Cryptoasset Analytics Workshop (CAAW) and contributes to projects like GraphSense, a cryptoasset analytics platform. His work spans academic collaborations with institutions worldwide, including contributions to standards like ResourceSync and Open Annotation Collaboration (OAC) models. Research interests include machine learning applications in finance, decentralized governance mechanisms, and improving the interoperability of digital resources. Publications highlight trends in DeFi composability, blockchain forensics for crime mapping, and ethical considerations in AI-driven cryptocurrency analysis. He has organized major workshops at venues like The Web Conference (WWW) and Financial Cryptography (FC), fostering interdisciplinary dialogue between academia and industry. No notable scientific awards are explicitly listed in the provided text, though his extensive publication record reflects significant contributions to the field.

Hermann MaurerView profile

Professor

• Networked Multimedia Systems
• Societal Implications of Informatics
• e-Learning
• +7 more

Hermann Maurer is Full Professor of Computer Science at Graz University of Technology (since 1978), with additional roles as Honorary Visiting Professor at Danube University and Honorary Research Fellow at University of Auckland (till 2012). He has held leadership positions including Dean of Computer Science (2004-2007) and Chair of Informatics Section in Academia Europaea (2007-2012). Key Institutions: Graz University of Technology, Austria-Forum, JOANNEUM RESEARCH, Academia Europaea Academic Honors: Member of Finnish Academy of Sciences, Cross of Honour for Arts and Science (Austria), multiple honorary doctorates Research Focus: Networked Interactive Digital (NID) Books, Austria-Forum (1.1M entries), hypermedia systems, anti-monopoly digital frameworks, and societal impacts of technology. He pioneered second-generation web systems and co-founded conferences like ED-MEDIA and I-KNOW. Key Trends in Publications: Recent works emphasize NID systems, interactive encyclopedias, web reliability, and the future of digital libraries. His research spans e-learning, information integration, and combating web-based plagiarism. Scientific Awards: Honorary Doctorates (St. Petersburg, Karlsruhe, Calgary) AACE Fellowship (2003) Integrata Prize (2000) Enter Prize (1999) Advising & Grants: Supervised over 400 M.Sc. and 50 Ph.D. theses. Led multimillion-dollar projects including Hyperwave AG, COSTOC, and Austria-Forum. His work on optical storage and color-graphic microcomputers (MUPID) exemplifies technological innovation.

Serafeim ChatzopoulosView profile

Researcher

• Data Mining
• Scientific Databases
• Scientometrics
• +5 more

Serafeim Chatzopoulos is a Researcher at the Information Management Systems Institute (IMSI) of the ATHENA Research Center since 2014 and a software engineer at OpenAIRE AMKE since 2022. He holds a PhD in Computer Science from the University of the Peloponnese (2022), an MSc in Computer Systems, and a BSc in Informatics and Telecommunications from the University of Athens. His research focuses on data mining, scientific databases, scientometrics, and big data management, with notable contributions to systems like OpenAIRE Graph and BIP! Finder. His work emphasizes improving academic search engines, citation analysis, and researcher assessment tools. Key projects include developing algorithms for expert recommendation (VeTo toolkit), impact-based literature search (BIP! Finder), and scalable data science tools like SciNeM and SmartDataLake. He has collaborated on EU and national research initiatives, leveraging technologies such as Apache Spark, Java, and React.js. Chatzopoulos has co-authored over 30 publications in venues like TPDL, WWW, and VLDB, addressing challenges in scientific impact measurement, metadata integration, and open science infrastructure. His PhD dissertation explored data mining in scholarly networks under Prof. Christos Tryfonopoulos' supervision. He remains active in advancing tools for academic transparency and reproducibility, including narrative CV frameworks and article deduplication systems.

Huy VoView profile

Associate Professor

• Big Data Systems
• Data Science
• High-Performance Computing
• +3 more

Huy Vo is an Associate Professor in the Department of Computer Science at the City College of New York (CCNY), part of the City University of New York (CUNY) system. His research focuses on Big Data Systems, Data Science, High-Performance Computing, Large-Scale Visualization, and Scalable Display Building. He is affiliated with CCNY's School of Engineering and maintains an active website at https://www-cs.ccny.cuny.edu/~hvo/ . His work spans urban analytics, visualization technologies, and machine learning applications. Notable contributions include analyzing global urban road networks using GNNs, developing semiconductor-specific large language models (SemiKong), and optimizing fleet management for ride-pooling in NYC. He also explores public health technologies, such as asthma self-management apps and pandemic policy impact visualizations. Vo's research often integrates interdisciplinary approaches, combining geospatial data, computational modeling, and real-time systems. His recent work highlights trends in scalable computing, AR visualization, and data-driven decision-making in urban environments. No scientific awards are explicitly mentioned in the provided text. His advising and grants sections remain unspecified, but his extensive publication record indicates active research collaborations and grant-funded projects. The SAT-Hub project exemplifies his focus on smart urban infrastructure, while his work on visualization frameworks like Unitypic underscores innovations in interactive 3D systems.

Panagiotis G. IpeirotisView profile

Professor

• Crowdsourcing
• Human-AI Collaboration
• Data Science
• +11 more

Panagiotis G. Ipeirotis is a Professor and George A. Kellner Faculty Fellow at the Department of Technology, Operations, and Statistics at the Leonard N. Stern School of Business, New York University. He is also affiliated with the Center for Data Science and the Computer Science department at NYU. His work bridges computer science, data science, and business analytics, with a focus on integrating human and machine intelligence. PhD in Computer Science, Columbia University (2004) MSc in Computer Science, Columbia University (2001) BSc in Computer Engineering & Informatics, University of Patras, Greece (1999) His research centers on crowdsourcing, human-AI collaboration, online labor markets, and social media analytics . He is widely recognized as a pioneer in human computation and has developed foundational techniques for ensuring data quality in crowd-sourced environments. His interdisciplinary work combines insights from computer science, economics, and social psychology to model user behavior and improve decision-making systems. His recent publications explore algorithmic fairness in hiring, theoretical voting models in crowdsourcing, and the economic impact of user-generated content. These works reflect a consistent trend toward building robust, ethical, and scalable human-machine systems that enhance data quality and decision accuracy across domains. Notable scientific awards include: Lagrange Prize in Complex Systems (2015) SIGKDD Test of Time Award (2020) NSF CAREER Award Best Paper Award at WWW 2011 Best Paper at SIGMOD 2006 Multiple best paper recognitions in Management Science and KDD Professor Ipeirotis has led significant research initiatives, including a $1.5 million Google Research Grant to integrate crowdsourcing with machine learning for visual search. He advises graduate students and contributes to academic leadership through editorial and collaborative roles. His work has been featured in prominent media outlets such as Forbes, WIRED, and Bloomberg Businessweek, highlighting its real-world impact. He is actively involved in research labs and initiatives at NYU, particularly those focused on data science, behavioral research, and computing. His affiliations with the Center for Research Computing and the Center for Behavioral Research underscore his interdisciplinary approach to solving complex problems at the intersection of technology and human behavior.

• Data Science
• Machine Learning
• Recommender Systems
• +7 more

András A. Benczúr is a prominent researcher affiliated with the Hungarian Academy of Sciences, specifically with SZTAKI (Institute for Computer Science and Control). He has maintained a prolific research career spanning multiple decades with over 160 publications documented in the dblp database. His work primarily focuses on data science, machine learning, and network analysis, with significant contributions to recommender systems and big data analytics. Dr. Benczúr's research interests include Data Science, Machine Learning, Recommender Systems, Network Analysis, Big Data Analytics, Graph Theory, Information Systems, Web Mining, Stream Processing, and Artificial Intelligence. His work demonstrates a consistent focus on developing theoretical foundations while addressing practical applications in various domains. He has made significant contributions to understanding information networks, developing efficient algorithms for data streams, and creating novel approaches to recommendation systems. His recent publications (2023-2025) show a continued focus on cutting-edge topics including neural network uncertainty, AI-driven communication for 6G networks, network embedding applications for public health, and theoretical evaluations of explainable AI methods. These works demonstrate his ability to bridge theoretical computer science with practical applications across multiple domains including telecommunications, public health, and blockchain technology. Dr. Benczúr has collaborated extensively with researchers across Hungary and internationally, with notable long-term collaborations with Bálint Daróczy (30 joint publications), Róbert Pálovics (27 joint publications), and Domokos Kelen (14 joint publications). His work appears in prestigious venues including IEEE Access, ICLR, WWW, RecSys, and numerous IEEE and ACM conferences. His research has practical applications in diverse areas including social network analysis, telecommunications infrastructure, public health monitoring, and blockchain technology. The Hexa-X project publications indicate his involvement in shaping the future of 6G communication standards through AI integration, while his work on network embeddings has been applied to vaccine skepticism detection, demonstrating the societal impact of his research.

Soumen ChakrabartiView profile

Professor

• Graph Neural Networks
• Knowledge Graphs
• Natural Language Processing
• +3 more

Soumen Chakrabarti is a Professor in the Department of Computer Science and Engineering at Indian Institute of Technology Bombay. His work bridges graph theory, natural language processing, and knowledge representation, focusing on scalable solutions for complex information retrieval problems. He has held visiting positions at Carnegie-Mellon University and Google. Education: B.Tech from IIT Kharagpur, PhD from UC Berkeley Key research areas: Graph Neural Networks, Knowledge Graphs, Code-Switched Text Analysis, Temporal Reasoning His recent publications emphasize neural graph matching , code-switched NLP , and multi-modal QA systems , often integrating symbolic reasoning with deep learning. Awards include the WWW 1999 Best Paper Award and ECML/PKDD 2008 Best Student Paper . He leads projects like CRUSH4SQL for text-to-SQL parsing and CSAW for annotated web search. His lab collaborates with IBM and Google on graph representation learning and temporal knowledge graph completion.

• Machine Learning Techniques
• Knowledge Mining
• Knowledge Mining in Distributed Environments
• +6 more

Maria Chalkidi is an Associate Professor at the Department of Digital Systems within the School of Information and Communication Technologies at the University of Piraeus. She contributes significantly to the academic community through her research and teaching in computer science and digital systems. She received her B.A. from the Department of Computer Science of the University of Piraeus in 1997, followed by a Master's Degree in Information Systems and a PhD from the Athens University of Economics and Business (AUEB) in 1999 and 2003, respectively. Dr. Chalkidi's research spans several key areas including Machine Learning Techniques, Knowledge Mining in Distributed Environments (P2P Systems, Sensor Networks), and WWW & Databases with specialization in Web Document Clustering and Web Content Management. Her work integrates cognitive approaches with database technologies to address complex information management challenges. Among her notable achievements is the prestigious Marie Curie Outgoing International Fellowship awarded by the European Commission for 2004-2005, which supported her postdoctoral research at the University of California, Riverside's Department of Computer Science & Engineering. She has actively participated in national and European research programs, working with the DB-NET research group at AUEB. Dr. Chalkidi has served on numerous program committees for International Conferences in Knowledge Mining and Machine Learning, contributing to the advancement of these fields globally.

• Recommender Systems
• E-Commerce
• Social Media
• +7 more

Ido Guy is a researcher at Yahoo Labs, specializing in recommender systems, e-commerce, and social media analytics. He has extensive publications in top-tier venues like WWW, SIGIR, WSDM, and NeurIPS, often collaborating with researchers such as Sharon Hirsch, Kira Radinsky, and Slava Novgorodov. Key research areas: Recommender systems, temporal modeling, graph neural networks, and e-commerce search. Notable projects: Automated category tree construction, calibration error measurement, and event-driven consumer demand prediction. His work integrates natural language processing and machine learning to solve practical challenges in product recommendation, review analysis, and enterprise social media. While no awards or students are listed here, his contributions to algorithm design and data-driven system development are significant.