Tilmann Rabl is a Professor affiliated with the Hasso Plattner Institute (HPI) at the University of Potsdam, Germany. His research focuses on database systems, distributed computing, and scalable data processing. He leads projects exploring serverless cloud infrastructure, stream processing, and machine learning integration with databases. Key areas of research include optimizing GPU-based data processing, developing benchmarks like TPCx-IoT and TPCx-AI, and advancing techniques for distributed systems, including RDMA and NVLink-based architectures. His work emphasizes practical systems, such as Skyrise (serverless data processing), Rhino (distributed state management), and PROTEUS (scalable machine learning). Rabl has contributed to foundational tools like BlockJoin for matrix partitioning and has explored performance trade-offs in persistent memory and CXL device memory. His collaborative projects address challenges in real-time data analytics, sensor data coherence, and interoperable data science workflows.
Minos Garofalakis is a Professor at the School of Electronic & Computer Engineering at the Technical University of Crete, specializing in data stream management, complex event processing, and privacy-preserving analytics. His work bridges theoretical and applied computer science, with a focus on scalable algorithms for high-velocity data. Best Paper Award at VLDB 2024 for 'OmniSketch' Leader in sketch-based and distributed stream processing Pioneer in differential privacy for relational data Research interests span stream analytics , probabilistic databases , and interactive query systems . Recent work includes oblivious parallel joins (2025), relational data synthesis under privacy constraints (2024), and cross-platform analytics frameworks (2020). His publications demonstrate a consistent focus on error-controlled approximations and real-time distributed processing. Scientific contributions have been recognized at premier conferences like VLDB, with awards highlighting innovations in multi-dimensional stream analysis and privacy-preserving operations . Collaborations span academia and industry, particularly in bioinformatics and distributed systems.
Amélie Marian is a Professor in the Department of Computer Science at Rutgers University. She maintains an active research program with numerous recent publications and research projects. Her office is located in CoRE 324 on the Busch Campus, and she can be reached at amelie.marian@rutgers.edu or by phone at (848) 445-8324. Dr. Marian's primary research interests focus on Data Management and Algorithms, with specific emphasis on Accountability and Transparency of Algorithms for Decision-Making, Explainable Rankings, Personal Information Management, Data Integration, and Data Corroboration. Her work bridges theoretical computer science with practical applications in decision systems, personal data management, and privacy-preserving technologies. She leads several major research initiatives including YourDigitalSelf (connecting, searching, and understanding personal digital traces), Explainable Rankings (toward transparent ranking functions), and Decentralized Collaborative Filtering (privacy-aware personal recommendations). Analyzing her recent publications reveals a clear trajectory toward increasingly important societal challenges in algorithmic transparency and accountability. Her work spans technical aspects of database systems and information retrieval while addressing critical social implications of algorithmic decision-making. The research shows strong interdisciplinary connections between computer science, social choice theory, human-computer interaction, and public policy. Microsoft Live Labs Award (2006) Google Research Awards (2008, 2010, 2012) NSF CAREER award (2009) NSF MCA Grant Award for Transparent and Accountable Decision Systems (2022) Multiple Google Research Awards for projects including Remembrance of Data Past and PERSEUS Dr. Marian actively mentors graduate students including PhD candidates Yehuda Gale and Shuchang Liu, and MS student Shuyuan Xu. Her research has been supported by significant grants including multiple NSF awards (NSF-SES 2218975, NSF-IIS 0844935, BCS-CDI-Type I 1027801), Google Research Awards, and Microsoft funding. She leads the YourDigitalSelf research group focused on personal information management systems and has established collaborative projects with researchers across multiple institutions.
Artem Barger is a researcher specializing in blockchain technology, distributed systems, and database optimization. With affiliations primarily in blockchain development and academic research, he has contributed extensively to Hyperledger Fabric enhancements and decentralized information systems. Research Interests Optimizing state databases for blockchain platforms Byzantine Fault Tolerance in distributed networks Permissioned blockchain architectures Tokenization of real-world assets AI applications in soft skills evaluation Recent Publications Barger's work focuses on improving blockchain scalability and security through techniques like certification blocks, Patricia Merkle tries, and verifiable randomness. He has also explored tokenization applications in charity and energy sectors.
Samuel Dahan is an Associate Professor at Queen’s University Faculty of Law and Director of the Conflict Analytics Lab. He holds visiting faculty positions at Cornell University, Paris Dauphine University, ENA, and Harvard University. His expertise spans Legal AI, Dispute Resolution, and Global Work, with a focus on open-source platforms like OpenJustice and the Deel Lab for global employment policy. Education: MA: Sorbonne-Ecole Normale Supérieure Ulm LLM: Katholieke Universiteit Leuven PhD: University of Cambridge Research Interests: Dahan’s work integrates data science with legal challenges, including AI-driven legal tools, conflict resolution analytics, and global workforce compliance. He leads initiatives like OpenJustice, an open-source platform with over 20,000 monthly queries, and the Deel Lab for HR tech innovation. Notable Achievements: Secured $1.1M in grants (NSERC-MITACS Partnership). Published in top venues: AAAI, ICML, McGill Law Journal. Keynote speaker at the French Supreme Court Hearing, Stanford Codex, and Law Society of Ontario. Labs & Teams: Directs the Conflict Analytics Lab at Queen’s University and co-leads the Deel Lab. His teams develop tools like the Deel AI Classifier and MyOpenCourt, addressing legal challenges through data-driven solutions.
Ali José Mashtizadeh is an Associate Professor at the Cheriton School of Computer Science, University of Waterloo. His research focuses on operating systems, distributed systems, and storage, with expertise in system reliability, network optimization, and concurrent programming. Education: Ph.D., Computer Science, Stanford University (2017) M.S., Computer Science, Stanford University (2017) M.Eng., Electrical Engineering and Computer Science, MIT (2007) B.S., Electrical Engineering, MIT (2006) His research centers on designing scalable and reliable systems, with recent publications exploring TCP network frameworks, in-memory data persistence, and microsecond-scale scheduling. Key themes include optimizing tail latency, neutralization-based memory reclamation, and fault-tolerant distributed services. His articles consistently demonstrate innovations in low-latency networking, operating system architecture, and cloud infrastructure, with recent emphasis on serverless benchmarks and processor customization. No scientific awards or advising relationships are detailed in the provided materials.
Reinhard Pichler is a Full Professor at the Vienna University of Technology (TU Wien), affiliated with the Faculty of Informatics and the Department of Databases and Artificial Intelligence . His research focuses on Database Theory , Computational Logic , and Parameterized Complexity . He leads multiple research projects like DeConquer (2023–2027) and HyperTrac (2018–2022), addressing challenges in query optimization and hypergraph decompositions. He holds the prestigious START Prize (2014–2022) for young researchers. His work spans theoretical foundations (e.g., hypertree decompositions) and practical applications (e.g., SPARQL query processing systems like SparqLog). He contributes to academic governance, serving on faculty councils and curriculum commissions. His research innovations bridge algorithmic theory and real-world database systems, emphasizing efficient query evaluation and tractability analysis. Key contributions include advancing fractional hypertree decompositions , SPARQL query optimization , and consistent query answering . His projects often involve collaborations with industry and international funders like the Austrian Science Fund (FWF) and Vienna Science and Technology Fund (WWTF). He actively publishes in top venues like Journal of the ACM , ACM Transactions on Database Systems , and Proceedings of the VLDB Endowment . His academic leadership extends to course design, teaching advanced topics like Complexity Theory and Theoretical Computer Science . He mentors doctoral students and oversees research teams exploring cutting-edge areas like uncertain databases and cloud-based computational social choice .
Zaiwen Feng is a researcher actively contributing to data governance, semantic modeling, and causal inference. His work focuses on knowledge graphs, graph-based methods, and service-oriented architectures through collaborations with institutions like the University of Queensland and universities in China. Research Focus : Graph Differential Dependencies, Entity Resolution, Causal Effect Estimation, and Ontology Alignment Methodologies : Machine Learning, Variational Autoencoders, Prompt Engineering, and Semantic Retrieval Application Areas : Biomedical Data, Property Graph Recommendation, and Process Model Repositories Key trends in his publications include automated semantic modeling , neural approaches for entity resolution , and causal inference with graph structures . He frequently collaborates with researchers like Keqing He, Wolfgang Mayer, and Selasi Kwashie across conferences such as HPCC, BIBM, and WISE.
Mohammad Kazemi Beydokhti is a Research Fellow (Level A) at RMIT University's Research & Innovation Capability department. He is also a PhD candidate with expertise in Machine Learning, NLP, and Geospatial Domain applications. His research focuses on qualitative spatial reasoning, GeoQA systems, knowledge graphs, and LLMs, with notable contributions to projects like Dynamic Vicmap (awarded the 'Innovation Award' by GCA) and the RMIT AWS Cloud Supercomputing Hub. He has collaborated with institutions like Utrecht University and has taught courses such as Advanced Imaging Technology (GEOM2112), Geospatial Programming with Python (GEOM2157), and Applied Geospatial Techniques (GEOM2450) as a tutor at RMIT from July 2021 to August 2024. His scientific contributions span geospatial question-answering systems, probabilistic spatial reasoning, and spatial-temporal modeling of seismic activity. Key projects include the DBSCAN-based seismic province analysis in Iran and ANP-OWA method applications in air quality monitoring station placement. Research Awards: Innovation Award (GCA) for Dynamic Vicmap project Labs/Teams: Involved in RMIT's AWS Cloud Supercomputing Hub and geospatial collaboration networks
Tao Yang is a Professor in the Department of Computer Science at the University of California, Santa Barbara, where he has been a faculty member since 1993. His research spans web search and mining, database and information systems, machine learning and data mining, parallel and distributed systems, and cloud computing. He serves as an active educator, teaching courses including CS170 Operating Systems (Spring 2024), CS291A Neural Information Retrieval (Fall 2024), and CS140 Parallel Computing (Winter 2025). PhD in Computer Science, Rutgers University ME in Artificial Intelligence, Zhejiang University MS in Computer Science, Rutgers University BS in Computer Science, Zhejiang University Professor Yang's research focuses on advancing the field of information retrieval with particular emphasis on neural approaches to search and ranking. His recent work explores neural document ranking, privacy-aware search systems, and versioned data search. He has led significant projects including Neptune clustering infrastructure, Sorrento self-organizing storage cluster, and TMPI for MPI execution optimization. His research bridges theoretical advances with practical implementations, particularly in scaling search architectures to handle billions of documents while maintaining relevancy, performance, and freshness. His publication record shows a clear evolution from foundational work in parallel and distributed systems toward contemporary research in neural information retrieval. Recent publications demonstrate expertise in optimizing both sparse and dense retrieval methods, with particular focus on efficiency improvements for multi-vector representations. His work consistently addresses real-world challenges in search scalability and privacy preservation. Faculty Research Award, Google Research Research Initiation Award, NSF (1994) UC Regents' Junior Faculty Award (1994) Computer Science Faculty Teacher Award (1995) CAREER Award, NSF (1997) Noble Jeeviant Award, AskJeeves (2002) Professor Yang has supervised numerous graduate students, many of whom have gone on to prominent positions at companies like Google, Apple, and Coursera, or academic positions at universities worldwide. His industry experience as Chief Scientist for Ask.com (2001-2010) and founding Chief Scientist for Teoma (2000-2001) has informed his research direction and provided valuable practical context for his academic work. He has served on program committees for major conferences including WWW, SIGIR, KDD, WSDM, CIKM, ECIR, and EMNLP. His research group maintains active projects in neural information retrieval, privacy-aware search, similarity computing, and parallel computing systems. The group collaborates closely with industry partners, particularly in the search technology space, and has developed systems that power major search engines serving over 100 million users.
Maribel Acosta is a Professor of Databases and Information Systems at Ruhr-Universität Bochum's Faculty of Computer Science. She holds affiliations with the Institut für Neuroinformatik (INI), focusing on interdisciplinary research combining natural and artificial cognitive systems. Her work spans databases, semantic web technologies, artificial intelligence, and machine learning applied to knowledge graphs. Acosta earned her Ph.D. (Summa Cum Laude, 2017) and Master's in Computer Science from Karlsruhe Institute of Technology (KIT). She has held roles including Junior Professor at RUB (2020–present), Deputy Professor at KIT (2019–2020), and Assistant Lecturer at Heidelberg University (2020). Research Interests: Her primary research focuses on efficient querying of knowledge graphs, integration of machine learning in data management, and semantic web technologies. Key areas include knowledge graph population, federated query processing, and stability in neural networks. Recent work explores applications in automotive systems and social science data integration. Awards: Notable recognitions include a 2022 WWW Best Paper nomination, Aminer's Top-100 Influential Scholars (2020–2021), and multiple teaching excellence awards from KIT (2017–2020). Teaching & Labs: She teaches courses on database systems, knowledge graphs, and artificial intelligence at RUB. Her lab contributes to projects like SMART-KG and has pioneered federated SPARQL query frameworks. She actively supervises doctoral research in knowledge graph applications and machine learning.
Anne-Marie Kermarrec is a Senior Researcher at INRIA (France), with prior roles at Microsoft Research (UK) and the University of Rennes 1 (France). Her work focuses on decentralized systems, including gossip protocols, peer-to-peer networks, social networks, and collaborative filtering. PhD in Computer Science (Rennes, 1996) Her research spans epidemic algorithms, content-based search in large-scale networks, and scalable group communication. She pioneered gossip-based peer sampling and multicast infrastructures like Scribe and SplitStream. Her publications highlight applications in decentralized news recommendation (AllYours), network coding, and privacy-preserving social platforms (Gossple). Key article trends include gossip protocols , P2P systems , social network analysis , decentralized storage , and collaborative filtering . She received the Michel Monpetit Award (2011), ERC Starting Grant (GOSSPLE, 2008-2013), and an ICDCS Best Paper Award (2010). Michel Monpetit Award (2011) ERC PoC AllYours (2013) ERC Starting Grant GOSSPLE (2008-2013) ICDCS Best Paper Award (2010) Kermarrec led program committees for major conferences (e.g., EuroSys, Middleware) and chaired the ACM Software System Award. Her software contributions include AllYours (news recommender), GossipLib (gossip library), and GossipPeer (P2P platform maintenance).
Sharma V. Thankachan is an Associate Professor in the Department of Computer Science at North Carolina State University since 2022, previously serving as an Assistant Professor at the University of Central Florida (2017-2022). His academic journey includes postdoctoral research at the University of Waterloo with Ian Munro and Georgia Institute of Technology with Srinivas Aluru. His educational credentials feature: Ph.D. in Computer Science from Louisiana State University (2014) B.Tech in Electrical and Electronics Engineering from National Institute of Technology Calicut, India (2006) Dr. Thankachan's research centers on string algorithms and compressed data structures with critical applications in computational biology, particularly addressing pan-genomic data modeling and gapped pattern matching challenges. His theoretical work bridges algorithmic design with practical genomic analysis, focusing on space-efficient representations for repetitive biological sequences. Analysis of his 2022-2025 publications reveals dominant themes in BWT-based indexing, non-overlapping pattern constraints, and external memory optimization. These contributions advance core areas of data compression, bioinformatics algorithms, and scalable text processing systems, demonstrating consistent innovation in handling genomic-scale data. His distinguished recognition includes the NSF CAREER award: National Science Foundation Faculty Early CAREER Development Award (2022) Dr. Thankachan mentors multiple doctoral students including Mano Prakash Parthasarathi, Paul Macnichol, and Oliver Chubet (co-advised with Donald Sheehy), with alumni Paniz Abedin, Daniel Gibney, and Sahar Hooshmand now holding faculty positions. His research is supported by two major NSF grants: CAREER: Algorithmic Aspects of Pan-genomic Data Modeling, Indexing and Querying (2023-2027, $513,686) AF: Small: Theoretical Aspects of Repetition-Aware Text Compression and Indexing (2023-2026, $414,034)
Professor Nikolaos Pelekis is a faculty member at the University of Piraeus, Greece, holding the position of Professor in the Department of Statistics and Insurance Science within the School of Finance and Statistics. His academic career focuses on Data Science, with a specialized emphasis on Mobility Data Management and Mining, a field he has contributed to for over two decades. He has authored two monographs and over 100 peer-reviewed articles, earning five Best Paper Awards and over 5,000 citations. Research Interests: Mobility Data Management and Mining Big Data Analytics Machine Learning Geographical Information Systems His work spans trajectory analysis, maritime and vessel traffic forecasting, and spatiotemporal data management. Notable projects include leading roles in EU-funded initiatives like GREEN.DAT.AI and EMERALDS, focusing on energy-efficient AI and urban mobility analytics. Awards and Recognition : Five Best Paper Awards, including the Ralf H. Güting Best Research Paper Award (2021) and Best Demo Paper Awards at SIGSPATIAL (2021). He also ranked 1st in the SemEval-2017 Task 4 for sentiment analysis. Professional Contributions : Serves on editorial boards (e.g., ECML PKDD, DMKD) and organizes workshops like BMDA. His teaching spans undergraduate and graduate courses in Data Science, Big Data Management, and Statistical Data Mining. Labs and Teams : Co-founder of the Data Science Lab at the University of Piraeus, collaborating across 4 departments and multiple researchers. Current efforts include maritime digitalization (VesselAI) and real-time trajectory prediction frameworks like ARGO.
Laura Andrea Cecchi is an active researcher specializing in computer science education, logic programming, and ontology engineering. Her work focuses on developing innovative pedagogical approaches for teaching computational thinking at early educational stages, with significant contributions to gender-inclusive STEM education. Research Interests: Her primary research explores: Logic programming curriculum development for primary education Visual tools for ontology engineering and knowledge representation Eye-tracking and HCI applications in programming pedagogy Gender-inclusive strategies for computer science education Automated reasoning in ontology-based systems Publication Trends: Her recent work (2020-2024) shows a strong focus on educational technology and democratizing programming education, while earlier publications (2015-2019) centered on formal methods and ontology engineering. She consistently publishes at premier computer science conferences including ICLP, CLEI, and IJCAI. Collaborations: Frequent collaborations with Jorge Pablo Rodríguez and Verónica Dahl appear across her publication record, particularly in education-focused research.
