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Somali ChaterjiView profile

Associate Professor

• Data Science
• Digital Agriculture
• Computational Biology
• +6 more

Dr. Somali Chaterji is an Associate Professor in the Department of Agricultural & Biological Engineering at Purdue University, with a courtesy appointment in the Elmore Family School of Electrical and Computer Engineering. Her research bridges data science, digital agriculture, and computational genomics, focusing on machine learning for IoT, edge computing, and scalable genomics analysis. She leads the Innovatory for Cells and Neural Machines (ICAN), developing algorithms for efficient data analytics in resource-constrained environments. Education: PhD in Biomedical Engineering (Purdue University), Postdoctoral Fellowship at University of Texas at Austin. Awards include the NSF CAREER Award (2022), ACM BCB Best Paper (2015), and Purdue Seed-for-Success Award (2016). She founded KeyByte LLC, a cloud computing startup optimizing ML workloads. Research spans IoT edge analytics (e.g., drone surveillance, embedded systems) and genomics (single-cell clustering, error correction in sequencing). She is Co-PI of the NSF CHORUS Center and A2I2 Army Institute. Over 20 students are advised, with active projects on serverless computing, federated learning, and genome engineering. Labs/Teams: ICAN Lab, WHIN project (Lilly Endowment), Purdue ABE Extension. Collaborations with Microsoft, Amazon, and Adobe Research. Active in teaching, including courses on applied ML and computational genomics with innovative pedagogy.

• Database Management Systems
• Query Processing and Optimization
• Approximate Query Processing
• +6 more

Dr. Feng (George) Yu is an Associate Professor of Computer Science and Information Systems at Youngstown State University in Youngstown, Ohio. He serves as the Campus Champion of NSF Extreme Science and Engineering Discovery Environment (XSEDE) at YSU and has been collaborating with XSEDE and Pittsburgh Supercomputing Center since 2014 to bring national workshop series on High-Performance Computing to YSU. Ph.D. in Computer Science, Southern Illinois University (2013) M.S. in Pure Mathematics, Shandong University (2008) B.S. in Information and Computation Science, Northeastern University (2005) Dr. Yu's primary research focuses on database management systems, particularly Approximate Query Processing (AQP) for big data analytics. His work spans multiple areas including Cloud Computing , Blockchain , NoSQL Databases , and Bioinformatics . His recent research has centered on error assessment for AQP using bootstrap sampling techniques, as evidenced by his 2024 publications AQPrius and Error Assessment for Multi-Join AQP . He has also made significant contributions to plant genomics through alternative splicing analysis in various crops. His publication trends show a consistent focus on query processing and optimization, with a recent shift toward more sophisticated error estimation techniques in approximate query processing. His work bridges theoretical database research with practical applications in bioinformatics and educational technology, as seen in his 2024 paper on computing curriculum accessibility for students with ASD. Best of QUEST 2024 Finalist (Faculty Advisor) Research Professorship (2023, 2022, 2019, 2018) Distinguished Professor in Scholarship (2020) Best Paper Award at International Conference on Software Engineering and Data Engineering (2019) Faculty Membership in The Honor Society of Phi Kappa Phi (2022) Dr. Yu actively mentors undergraduate researchers, having advised students including Govardhan Gula for the BEST of QUEST project on accelerating bootstrap resampling, and led a CREU-funded project on recommender systems with undergraduate researchers Alyssa Adams, Olivia Bindas, Maddie Cope, and Elizabeth Durflinger. His research has been supported by external funding sources including Amazon Inc. and the Computer Research Association. He directs the YSU Data Lab , which focuses on data-oriented sciences and operates multiple high-performance research clouds including Sarah Cloud and YSU STEM Cloud. The lab conducts cutting-edge research in approximate query processing, blockchain, and heterogeneous cloud infrastructure.

Catherine FaronView profile

Professor

• Artificial Intelligence
• Knowledge Management
• Semantic Web
• +8 more

Catherine Faron is a Full Professor at Université Côte d'Azur , affiliated with the I3S laboratory and Inria center . She serves as vice-head of the Wimmics joint research team and leads the Artificial Intelligence and Data Engineering (IAID) program at Polytech Nice Sophia engineer school. Habilitation à diriger les recherches (HDR) in Computer Science, UCA (2017) PhD in Computer Science, Univ. Paris 6 (1997) Her research focuses on Artificial Intelligence , particularly in Knowledge Representation and Reasoning (KRR) and Semantic Web technologies. She develops hybrid intelligent systems combining KRR with machine learning for knowledge extraction, integration, and exploitation across education, health, and digital humanities. Recent publications highlight her work on: 2025 : Knowledge graphs for historical zoological data 2024 : Semantic annotation frameworks in agronomy 2023 : Agricultural data mapping and medical record enrichment 2022 : Visual exploration of big linked data Scientific recognitions include: 2023: Best Paper Award, ESWC 2017: Scientific Excellence Award, UCA 2016: Best Demo Award, ISWC 2015: Best Paper Award, IC 2008: Best PhD Paper Award, ECPPM She has supervised 19 PhD/Master's students and leads/has led projects like D2KAB , DEKALOG , and ZOOMATHIA , with partnerships across academic and industrial institutions.

• Dependability Engineering
• Performability Analysis
• Cloud Computing
• +7 more

Ermeson Carneiro de Andrade is a Professor at the Department of Systems and Computer Engineering within the Center of Informatics at the Federal University of Pernambuco (UFPE) in Brazil. His research focuses on dependability engineering, performability analysis, and fault tolerance in distributed and embedded systems. Over his career spanning more than 15 years, he has established himself as a prominent researcher in the field of system reliability through numerous publications in top-tier journals and conferences. Dr. Andrade's research interests primarily center on the analysis and modeling of system dependability, with particular expertise in UAV-based monitoring systems, cloud computing environments, and IoT architectures. His work bridges theoretical modeling with practical applications, particularly in environmental monitoring, disaster recovery solutions, and mission-critical systems. He has made significant contributions to understanding software aging phenomena in various computing environments and developing performability-aware solutions for real-time systems. The analysis of his recent publications reveals a strong focus on UAV systems for environmental monitoring, particularly deforestation detection, with increasing attention to weather impacts and vehicle density-aware traffic monitoring. His research demonstrates a consistent pattern of applying stochastic modeling techniques to solve practical problems in distributed systems, with recent work expanding into NoSQL database performance, satellite constellation dependability, and the performance-interpretability trade-offs in machine learning models. This evolution shows his ability to adapt to emerging technologies while maintaining core expertise in system reliability. Dr. Andrade has been actively involved in mentoring students and collaborating with researchers across Brazil and internationally. His work often involves interdisciplinary teams addressing complex system challenges. While specific awards aren't detailed in the available publication records, his consistent output in high-impact venues demonstrates recognition within the dependability engineering community. His laboratory work appears to focus on system modeling and analysis, with particular emphasis on experimental validation through simulation and real-world testing. Current projects suggest involvement in UAV-based monitoring systems for environmental applications, with strong connections to public sector institutions in Pernambuco state.

• Big Data Analytics
• Machine Learning
• Data Mining
• +7 more

Dr. Sikha S Bagui is a Distinguished University Professor in the Department of Computer Science at the Hal Marcus College of Science and Engineering, University of West Florida . She served as the former Chair of Computer Science and was the Founding Director of the Center for Cybersecurity . Her research spans database design, Big Data analytics, machine learning, and cybersecurity . Research Focus: Machine Learning, Data Mining, Network Traffic Analysis, Graph Databases, and Resampling Techniques for Imbalanced Data Awards: Askew Fellow (2018–2021), multiple Excellence in Teaching and Distinguished Research Awards (2001–2024) Contributions: Authored books on databases/SQL (translated internationally), developed the UWF-ZeekData datasets for cybersecurity research, and served as Associate Editor for multiple journals Tools & Frameworks: Active in Hadoop, Spark, Memgraph, and MITRE ATT&CK-based threat modeling Publication Trends: Recent work focuses on MITRE ATT&CK datasets , graph-based cybersecurity , resampling rare attacks , and educational impacts in computing . Her research bridges theoretical and applied domains, including clinical decision support systems and K-12 computer science education. Scientific Awards: Askew Fellow (Reubin O’D. Askew Institute for Multidisciplinary Studies, 2018–2021) Excellence in Teaching and Advising Award (UWF, 2012) Distinguished Research and Creative Activities Award (UWF, 2007, 2012) Excellence in Undergraduate Teaching and Advising Award (UWF, 2001–2006) Leadership & Service: Directed the Center for Cybersecurity, contributed to journals as Associate Editor, and engaged in initiatives like NCWIT Aspirations in Computing and the Association for Women in Computing.

• Information Systems Engineering
• Privacy Threat Modeling
• IoT Security
• +4 more

Dimitri Van Landuyt serves as an Associate Professor in the Department of Computer Science at KU Leuven , affiliated with the Information Systems Engineering Research Group (LIRIS) . He leads and co-promotes multiple high-impact research projects focused on security and privacy engineering, including initiatives on model-driven security risk analysis , privacy by design , and IoT security . His work spans GDPR compliance, synthetic data management, and threat modeling innovations. Academic Leadership : Member of the Council of FEB and Campus Council Leuven/Kortrijk Research Pillars : Privacy threat modeling, security automation, IoT systems, GDPR technical implementation His publications demonstrate expertise in privacy-enhancing technologies, with recent work analyzing LLM applications in threat modeling, developing tree-based privacy analysis frameworks, and creating adaptive trust management architectures. He explores serious games for security training, synthetic data quantification standards, and runtime threat assessment mechanisms. Dimitri contributes to educational programs through courses in ICT Service Management , Security & Privacy by Design , and Research Methodologies . He supervises student research while collaborating with industry and academia on data protection challenges.

Uwe RoehmView profile

Associate Professor

• Distributed Data Management on Modern Hardware
• Integrated Machine Learning with Databases
• Interactive Big Data Analytics
• +5 more

Associate Professor Uwe Roehm is a faculty member at the University of Sydney's School of Computer Science, specializing in database systems and big data analytics. He holds a PhD from ETH Zurich and joined the University of Sydney in 2004. His research focuses on distributed data management, integrating machine learning with databases, and scalable data processing for bioinformatics. Education: PhD in Computer Science, ETH Zurich (2002) Diplom-Informatics (MSc equivalent), University of Passau Research Interests: Database Systems and Transaction Processing Human-Centred Data Management Freshness-Aware Scheduling (FAS) Big Data Analytics and Cloud Computing Key Projects and Contributions: Developed Serialisable Snapshot Isolation (SSI), implemented in PostgreSQL Created the Master of Data Science program at the University of Sydney Recipient of the 2018 ACM SIGMOD Test of Time Award Awards: 2018 ACM SIGMOD Test of Time Award 2013 Dean's Award for Outstanding Teaching 2008 ACM SIGMOD Best Paper Award Teaching and Grants: Teaches courses on database systems, data science platforms, and cloud computing Principal Investigator on ARC-funded projects, including Linkage and Discovery grants Labs and Teams: Leads the Database Research Group and contributes to the Human-Centred Technology Research Cluster at the University of Sydney.

Diego Sevilla RuizView profile

Associate Professor

• Software Engineering
• Database Systems
• NoSQL Technologies
• +3 more

Diego Sevilla Ruiz is an Associate Professor at the University of Murcia's Faculty of Informatics, Department of Computer Engineering and Technology, where he leads initiatives in Software Engineering research. He completed his PhD in 2008 on distributed component models (CORBA-LC). His research focuses on database technologies, particularly schema evolution in NoSQL and relational databases, model-driven engineering approaches, and unified metamodeling. Recent work develops Skiql (a schema query language), Athena (schema definition language), and methodologies for referential integrity in graph databases. His publications advance database abstraction techniques, schema migration frameworks, and automated tools for database management and modernization.

• Blockchain Technology
• Distributed Systems
• Permissioned Blockchains
• +3 more

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.

• Cloud Computing
• Distributed Systems
• Data Processing
• +4 more

Luis Antunes Veiga is an Associate Professor and Senior Researcher at INESC-ID Lisbon, affiliated with the Distributed Systems Group and the Computing Systems and Communication Networks Laboratory. His research focuses on Cloud Computing, Edge Computing, Distributed Systems, and Big Data processing. He teaches courses such as 'Cloud Computing and Virtualization' and 'Operating Systems, Virtualization and Cloud Computing.' His work emphasizes scalable systems, network-aware workflows, and resource-efficient data processing. Research Interests: His primary areas include distributed systems architectures, edge computing frameworks, graph processing algorithms, and software-defined systems. He explores topics like latency-aware network design, resource auction mechanisms for edge environments, and interoperable service workflows. His contributions span theoretical frameworks and practical implementations, such as the RATEE system for edge resource trading and the VeilGraph incremental graph processing framework. Publications: His recent work addresses challenges in distributed systems, such as elastic scaling of stream processing, efficient graph processing in Spark, and latency optimization in internet-scale workflows. These publications reflect a trend toward integrating software-defined approaches with edge and cloud infrastructures. Notable Awards: Best Young Researcher INESC-ID, Excellence in Teaching (IST 2012), and a Best-Paper Award at ACM/IFIP/Usenix Middleware 2007. Labs & Teams: Active in the Computing Systems and Communication Networks Laboratory, leading projects on edge computing and distributed systems.

Michele LanzaView profile

Professor

• Software Engineering
• Software Visualization
• Software Evolution
• +7 more

Michele Lanza is a Full Professor at the Faculty of Informatics, Università della Svizzera italiana (USI), Lugano, where he co-founded the faculty in 2004. He is the founder and director of the Software Institute (since 2017) and leads the REVEAL research group, focusing on software visualization, evolution, and analytics. He holds a PhD from the University of Bern (2003), where he also earned his MSc (1999), and was a postdoctoral researcher at the University of Zurich. Research Interests: Software Engineering Software Visualization (notably the 'City Metaphor') Mining Software Repositories (MSR) Software Evolution and Analytics Program Comprehension Reverse Engineering and Architecture Recovery His recent publications (2023–2025) emphasize immersive software visualization in virtual reality, automated documentation, code refactoring, and empirical studies on developer behavior and documentation. There is a strong trend toward human-centric, visual, and data-driven approaches, often leveraging VR and interactive tools for deeper software understanding. Scientific Awards: Ernst Denert Award (2003) Credit Suisse Teaching Award (2007, 2009) Best Paper Awards (SANER 2024, VISSOFT 2022, ICPC 2016) Most Influential Paper Award (MSR 2010) Advising and Grants: Prof. Lanza has supervised over 13 PhD students and numerous MSc and BSc theses, fostering a vibrant research group. He leads multiple funded projects, including SNF grants (FORCE, TUML, INSTINCT, PROBE, ESSENTIALS, BIGDATA), FFL (CSRD), and industry R&D initiatives. Funding supports travel, hardware, software, and PhD positions, enabling cutting-edge research in software engineering. Labs and Teams: He leads the REVEAL research group and the Software Institute at USI, which serve as hubs for innovation in software visualization, mining, and evolution. These teams are highly collaborative, interdisciplinary, and active in top-tier conferences (ICSE, FSE, ICSME, MSR, VISSOFT).

Oscar Romero MoralView profile

Professor

• Data Management
• Data Science
• Data Governance
• +7 more

Oscar Romero Moral is a Professor at the Polytechnic University of Catalonia (UPC), affiliated with the Department of Services and Information Systems Engineering at the Barcelona School of Informatics (FIB). He leads research in the inSSIDE, inLab FIB, and DTIM groups, focusing on data management, data science, and big data technologies. His work emphasizes knowledge graphs, data governance, and machine learning integration with data systems. Affiliations: UPC, inSSIDE, inLab FIB, DTIM Group Research Interests: Data Management, Data Engineering, Big Data, Knowledge Graphs, Data Governance, Machine Learning Integration He has authored over 276 academic contributions, including peer-reviewed articles on federated healthcare data systems, GPU-accelerated workflows, and graph-driven data integration. His recent work addresses challenges in heterogeneous computing, automated data governance, and scalable data architectures. Romero has served on the program committees of major conferences like VLDB, ICDE, and EDBT, and led competitive research projects in data systems and analytics. He collaborates extensively with industry partners and academic institutions, driving innovations in distributed data management and edge computing.

• Database Systems
• Integration of heterogeneous databases
• Data quality and data integrity
• +6 more

Thomas Kudraß serves as Professor for Database Systems at the Faculty of Computer Science, Mathematics and Natural Sciences (IMN) at Leipzig University of Applied Sciences (HTWK Leipzig). With over 25 years of academic and industry experience, he teaches courses including Database Basics, Database Application Programming, Data Warehousing, and Big Data Technologies. His leadership roles include Internship Coordinator for Computer Science since 2001, Member of the Academic Senate since 2011, and Contact person for the IMN faculty alumni association since 2005. Dr. Kudraß earned his Dipl.-Ing. in Computer Science from Technical University of Dresden (1985-1990) and completed his doctorate at Darmstadt University of Technology (1992-1997), where his dissertation received the prestigious Jos Schepens Memorial Award. Prior to academia, he worked as an Information Systems Architect at UBS AG Zurich and Database Specialist at Swiss Bank Corporation Basel. His research spans database technologies with focus on heterogeneous database integration, data quality, data privacy, and modern database concepts. Recent work explores cloud-native billing applications for 5G, NoSQL databases, and privacy-preserving record linkage techniques. His publication record shows continuous evolution from traditional database systems to contemporary challenges in big data and distributed systems. Jos Schepens Memorial Award for doctoral dissertation (1997) Active participation in German Informatics Society (GI) specialist groups Organizer of multiple database-related workshops and conferences Professor Kudraß has significantly contributed to academic governance as E-Learning Officer (2000-02), Faculty Council member (2009-12), and liaison lecturer for the German Society for Computer Science (2001-14). He has coordinated numerous student projects and served on program committees for major conferences including INFORMATIK 2017 and BTW series.

Tobias BlankeView profile

Professor

• Artificial Intelligence
• Digital Humanities
• Big Data Ethics
• +7 more

Professor Tobias Blanke is a leading scholar at the University of Amsterdam and the Institute for Logic, Language, and Computation , with additional affiliation as Visiting Professor of Social and Cultural Informatics at King’s College London. His work bridges artificial intelligence and humanities disciplines. PhD in Computer Science and Political Philosophy Director of DARIAH (2012–2016) Head of Digital Humanities Department at KCL (2016–2019) Blanke’s research focuses on artificial intelligence and big data applications within the human sciences, emphasizing ethical implications such as: Predictive policing systems Algorithmic othering Platform monopolization Computational genealogy XML retrieval frameworks His recent publications demonstrate increasing interdisciplinary collaboration in digital humanities , with key partnerships at the Open Data Institute , TacticalTech , and British Library . While no formal awards are listed in available materials, his leadership in infrastructural projects like the European Holocaust Research Infrastructure highlights significant contributions to the field. Blanke maintains active teaching and research roles, leading initiatives like the International Artificial Intelligence PhD Academy courses for social sciences. His technical expertise spans: XML retrieval systems NoSQL database applications Algorithmic governmentality Digital publishing frameworks

• Cryptography
• Blockchain Technology
• Network Security
• +7 more

Danilo Gligoroski is a Professor at the Department of Information Security and Communication Technology at NTNU. His research focuses on cryptography, blockchain technology, network security, and 5G systems. He has collaborated extensively on projects involving decentralized ledgers, cybersecurity in healthcare, and hardware implementations of cryptographic algorithms. Key contributions include work on blockchain-based trust systems, secure network slicing, and privacy-preserving protocols. Affiliations: NTNU, Department of Information Security and Communication Technology Primary Research Areas: Cryptography, Blockchain, 5G Networks, Cybersecurity Notable Projects: GRAND Parallel Decoding Framework, GDPR-compliant healthcare blockchain Research interests span cryptographic protocol design, distributed ledger technologies, and their applications in healthcare, telecommunications, and IoT. His work often addresses scalability, privacy, and compliance challenges in decentralized systems. Publications highlight trends in blockchain consensus mechanisms, cryptographic hardware optimization, and network security for next-gen communication systems. Collaborations with industry and academic partners emphasize practical implementations and real-world use cases. Advising and grants include supervision of interdisciplinary projects and contributions to EU-funded cybersecurity initiatives. Active in open-source software development for network slicing and blockchain frameworks.