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Michela TauferView profile

Associate Professor

• Heterogeneous Computing
• Cloud Computing
• Volunteer Computing
• +4 more

Michela Taufer is the David L and Beverly JC Mills Chair of Computer and Information Sciences and an Associate Professor at the University of Delaware. She earned her Masters degrees in Computer Engineering from the University of Padova (Italy) and her doctoral degree in Computer Science from the Swiss Federal Institute of Technology (Switzerland). Her research interests include scientific applications and their advanced programmability in heterogeneous computing (i.e. multi-core and many-core platforms, GPUs); performance analysis; modeling and optimization of multi-scale applications on heterogeneous computing; cloud computing and volunteer computing; numerical reproducibility and stability of large-scale simulations on multi-core platforms; and big data analytics and MapReduce. Dr. Taufer has published research on protein folding, molecular dynamics, and workflow execution on cloud and grid platforms. Her work often focuses on performance characterization and optimization of scientific applications on distributed and parallel systems. From 2003 to 2004 she was a La Jolla Interfaces in Science Training Program (LJIS) Postdoctoral Fellow at the University of California San Diego (UCSD) and The Scripps Research Institute (TSRI) From 2005 to 2007, she was an Assistant Professor at the Computer Science Department of the University of Texas at El Paso (UTEP) She joined the University of Delaware in 2007 as an Assistant Professor and was promoted to Associate Professor with tenure in 2012

Charles ReissView profile

Assistant Professor

• Computer Science Education
• Computer Systems
• Cloud Computing
• +3 more

Charles Reiss is an Assistant Professor in the Computer Science Department at the University of Virginia, focusing on teaching and research in computer systems and education. He holds a PhD from UC Berkeley (2016) and has a strong background in computer architecture and distributed systems. His research interests include computer science education methodologies, cloud computing, and systems optimization. Education: B.S. Computer Science (Georgia Tech, 2008), M.S. Computer Science (UC Berkeley, 2011), Ph.D. Computer Science (UC Berkeley, 2016). Teaching: Reiss has taught courses such as CS 3130 (Computer Systems and Organization), CS 3330 (Computer Architecture), and CS 4630 (Defense Against the Dark Arts). His courses emphasize hands-on learning with tools like HCLRS, an educational hardware description language. Awards: Winner of the SoCC 2021 Test of Time Award for work on Google cloud trace analysis. Contributions: Developed educational software like HCLRS and CPU cache exercises. Active in curriculum design for computer systems and security education.

• Logic Programming
• Knowledge Representation
• Graph Databases
• +7 more

Ana Edelmira Pasarella Sanchez is a Professor in the Department of Computer Sciences at the Faculty of Mathematics and Statistics, Universitat Politècnica de Catalunya (UPC). She is a member of the ALBCOM research group, focusing on algorithms, bioinformatics, complexity, and formal methods. Her work bridges theoretical computer science with practical data systems. PhD in Computer Science, Universitat Politècnica de Catalunya Her research centers on logic programming, knowledge representation, and graph databases. She explores how formal methods can enhance data processing, particularly through dynamic pipelines and trust-aware access control. Her work integrates Datalog, semantic reasoning, and big data frameworks to improve scalability and correctness in knowledge systems. She has contributed to foundational semantics of logic programs and their applications in security and data integration. Her recent publications highlight a trend toward efficient, adaptive data processing systems, especially for graph analytics and knowledge graphs. She compares paradigms like MapReduce and pipelining, advocating for dynamic, functional approaches to big data. Her work increasingly addresses real-world challenges in federated knowledge graphs and access control. SACMAT 2017 Best Paper Award Pasarella has been involved in multiple competitive R&D+i projects, such as 'Modelos y Técnicas para el Procesamiento de Información a Gran Escala' and 'Modelos y métodos basados en grafos para la computación en gran escala,' indicating sustained funding and collaborative leadership. She advises on research direction within her group and mentors through collaborative publications. She has served on the scientific committee of the Latin American Informatics Conference (CLEI), contributing to the broader academic community. She is part of the ALBCOM research group and collaborates extensively with researchers like Fernando Orejas, Maria-Esther Vidal, and Elvira Pino, working on logic-based frameworks for data and security systems.

İBRAHİM RIZA HALLAÇView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Parallel and Distributed Systems
• +3 more

İbrahim Rıza Hallaç is an Assistant Professor at RAFET KAYIŞ FACULTY OF ENGINEERING, Department of Computer Engineering. He holds a PhD (2021), Master's (2014), and Bachelor's (2011) in Computer Engineering from Firat University, with doctoral research on social media analysis using deep learning methods under advisor Galip Aydın. His research focuses on: Artificial Intelligence and language models Machine learning for distributed systems Big data analytics and cloud computing Natural language processing applications Social media sentiment analysis Radar-based object detection systems His publications (2015-2024) demonstrate consistent focus on machine learning applications across diverse domains including social network analysis, distributed computing, Turkish NLP, and sensor data processing. Recent works emphasize radar-based multi-target detection (2024), credit scoring models (2023), and wind speed estimation (2023), reflecting expertise in scaling AI solutions for real-world problems. He has led four national R&D projects: Deep Learning-Based Face Recognition Authorization System (2017-2019) DEĞİRMEN Big Data Analysis Platform for Turkish Armed Forces (2017-2021) KOSGEB Innovation Program for Face Recognition Systems (2017-2019) Cloud-Based Social Media Reputation Analysis System (2015-2017) No students, awards, or contact emails are documented in available records.

Dario ColazzoView profile

Professor

• Databases
• Programming Languages
• Cloud Computing
• +7 more

Dario Colazzo is a Professor at Université Paris-Dauphine, affiliated with the LAMSADE laboratory. His research focuses on database systems and programming languages, particularly in cloud databases, type systems for semi-structured data, and query processing optimizations. He has contributed extensively to XML and XQuery research, including parallel query execution and static analysis techniques. His teaching includes courses on programming (Java), UML, database systems, and semantic web technologies at both undergraduate and graduate levels (in French). Key research interests span XML/XQuery optimization, JSON processing, distributed systems, and type systems for safe and efficient data handling. His work emphasizes scalable cloud-based solutions and efficient query execution models. Publications highlight advancements in parallel query processing (e.g., PAXQuery), type-based optimizations for XML updates, and semantic graph analytics. He has collaborated with institutions like EDBT, WWW, and VLDB conferences.

Nikolaus AugstenView profile

Professor

• Database Systems
• Data-Centric Applications
• Approximate Matching
• +4 more

Prof. Nikolaus Augsten is a Professor and Head of the Database Research Group at the Department of Computer Science, University of Salzburg. He previously held positions at the Free University of Bozen-Bolzano, Italy, and visited Technische Universität München (TUM) and Washington State University. His research focuses on data-centric applications, approximate matching techniques for complex data structures, and efficient index structures for similarity search. Education: PhD from Aalborg University (Denmark, 2008), supervised by Prof. Michael Böhlen. Master's from Adam Mickiewicz University (Poland). Research interests include hierarchical data similarity (e.g., tree structures), scalable algorithms for tree edit distance, and applications in e-government and XML search engines. His work emphasizes practical solutions for tree-to-tree matching and similarity joins. Selected Awards: ICDE 2010 Best Paper Award for TASM: Top-k Approximate Subtree Matching Advising includes PhD student Mateusz Pawlik (thesis: Efficient Computation of the Tree Edit Distance). His group develops open-source tools like the RTED algorithm and approximate tree matching libraries. Labs/Teams: Database Research Group at University of Salzburg, collaborating on projects like Tree Edit Distance reference implementations and set similarity join algorithms.

Peiyuan PanView profile

Senior Lecturer

• Software System Development
• Web-based Applications
• Artificial Intelligence Technologies
• +3 more

Dr. Peiyuan Pan is a Senior Lecturer in Computer Science at London Metropolitan University, affiliated with the School of Computing and Digital Media. He holds a PhD in Computer-aided Manufacturing Engineering, a postgraduate certificate in Teaching and Learning in Higher Education, and a BSc (Hons) in Computer Science. He specializes in teaching OO programming, web systems development, and e-commerce applications. His research focuses on software system development, AI technologies, embedded systems, e-manufacturing, and supply chain management. Dr. Pan has led several research projects, including a Virtual Surgery system for Java programming education (2010–2011) and an Internet-based supply chain improvement system (1999–2002). He received the Vice Chancellor's Teaching Fellowship Award in 2010–2011. His publications span e-learning methodologies, robotics, and manufacturing automation. He contributes to interdisciplinary work in AI-driven design systems, fuzzy logic protocols, and web-based expert systems. His teaching responsibilities include leading the Computing and Business Information Technology FdSc program. Professional affiliations include the ACM and active participation in international conferences on computing and manufacturing systems.

• Computer Science
• Game Design
• Database Systems
• +3 more

Walker M. White is the Stephen H. Weiss Provost's Teaching Fellow and Director of the Game Design Initiative (GDIAC) at Cornell University's Department of Computer Science. He leads the undergraduate game design minor and teaches core courses like CS/INFO 3152 (Introduction to Game Design) and CS/INFO 4152 (Advanced Topics in Game Design). His research focuses on data-driven games, database systems (e.g., Cayuga), and inquiry-based learning. He previously served as an assistant professor at the University of Dallas, specializing in mathematics and computer science education. His work bridges game design, database efficiency, and pedagogical innovation. Education & Teaching: PhD in Mathematical Logic (implied by research background) Assistant Professor of Mathematics and Computer Science at University of Dallas Developed inquiry-based learning methods (Moore Method) for proof-based courses Research Interests: Data-driven game design, declarative processing for games, scalable database systems, and educational technology. His projects include the SGL language for game logic and Cayuga for data stream processing. Teaching Contributions: Transitioned CS 1110 to Python, lowering barriers for beginners Independent study projects featured at indie game festivals Liaison for game studio recruitment (EA, Valve, etc.) Awards & Recognition: None explicitly listed, but contributions to game design education and database research are highlighted. Advising & Labs: Oversees GDIAC, which connects multiple departments. Manages competitive independent study programs and advises projects on mobile game development and NPC behavior systems.

• Biomedical Informatics
• Machine Learning
• Data Mining
• +5 more

Dr. Yanqing Ji is a Professor in the Department of Electrical and Computer Engineering at Gonzaga University, specializing in biomedical informatics and high-performance computing. He serves as an editor for the Journal of Digital Signal Processing and a reviewer for 23 international journals. Education: Ph.D. in Computer Engineering, Wayne State University M.Sc. in Physical Electronics, University of Science & Technology of China B.E. in Industrial Automation, Qingdao University Dr. Ji's research focuses on biomedical association mining, adverse drug reaction detection, and parallel/cloud computing applications in healthcare. His work integrates machine learning, fuzzy logic, and multi-agent systems to enhance drug safety surveillance and biomedical literature search. His publications emphasize scalable MapReduce solutions for biomedical text mining, temporal association mining for drug interactions, and intelligent agent systems for pharmacovigilance. These span prestigious conferences like IEEE BIBM, ITNG, and NAFIPS, alongside journals such as BMC Bioinformatics and IEEE Transactions on Knowledge and Data Engineering . Scientific awards include NIH R21, AHRQ R18 grants, and NSF-funded projects (IGERT 9987598, MRI 9977815, ITR 0081696). He has led system design for distributed healthcare applications and optimized decision models using genetic algorithms.

Fadila BentayebView profile

Associate Professor

• NoSQL Data Warehouses
• Text Warehousing
• Personalization & Recommendation Systems
• +5 more

Fadila Bentayeb is an Associate Professor (HDR) at the University of Lyon 2, affiliated with the ERIC Laboratory and the Department of Informatics and Statistics (DIS). She leads the Complex Data Warehousing and OLAP research team (SID) and has served as Head of the first year of the Master's degree in Computer Science at the Institute of Communication (ICOM) since 2014. Her work bridges academic research and practical applications in data-intensive systems. HDR (Habilitation to Direct Research), University of Lyon 2 (2011) Ph.D. in Computer Science, University of Orléans (1998) Her research focuses on NoSQL data warehouses, text warehousing, and personalization systems, with emphasis on scalable architectures and context-aware analytics. Recent work explores AI-driven security in IoT networks and temporal modeling for evolving data structures. Key trends in her publications include: (1) Graph-based multidimensional modeling (GAMM framework), (2) Optimization of distributed big data warehouses using machine learning, (3) Medical text classification via hybrid CNN-LSTM models, and (4) Temporal data warehouse evolution. As part of the ERIC Laboratory’s leadership team, she contributes to strategic research directions and mentors students in advanced data systems. Her technical expertise extends to Hadoop/Spark optimization, OLAP cube design, and semantic integration of heterogeneous data sources.

LI RongView profile

Professor

• Cellular Dynamics
• Mechanobiology
• Biomechanics
• +6 more

LI Rong is a Professor (courtesy appointment) in the Department of Mechanical Engineering at the National University of Singapore (NUS), and Director of the Mechanobiology Institute (MBI), a Research Centre of Excellence at NUS. She concurrently holds a Distinguished Professorship in the Department of Biological Sciences at NUS. Previously, she served as Bloomberg Distinguished Professor of Cell Biology at Johns Hopkins School of Medicine and Department of Chemical and Biomolecular Engineering at Johns Hopkins University. Her research focuses on cellular dynamics and mechanics, integrating genetics, quantitative imaging, biophysical measurements, mathematical modeling, genomics, and proteomics. This interdisciplinary work aims to understand how eukaryotic cells transmit genomes, adapt to environments, and organize for specialized functions. Her recent publications span entity profiling, temporal data management, and big data analytics systems. Key contributions include the MAROON+ system for temporal entity profiling, HC-Store for hybrid MapReduce-RDBMS systems, and frameworks for optimizing algorithmic trading strategies using MapReduce. LI Rong's leadership roles include directing the Mechanobiology Institute and holding distinguished appointments across multiple institutions. Her work bridges engineering, biology, and computational sciences, reflecting her expertise in both mechanobiology and big data systems.

Joseph LetourneauView profile

Lecturer

• Incident Response
• Digital Forensics
• Computer Networking
• +1 more

Joseph Letourneau is an Instructor in the Division of Information Technology & Sciences at Champlain College. He holds expertise in Incident Response, Digital Forensics, and Cybersecurity, with a focus on enhancing student engagement through structured and interactive teaching methodologies. His professional background includes work in Managed Security Services, covering areas like Vulnerability Assessments, PCI Investigations, and Malware Analysis. Research Interests: His research spans cybersecurity practices, digital forensics techniques, and the application of clustering algorithms in big data analysis. He emphasizes practical, hands-on learning to prepare students for real-world challenges in IT security and networking. Articles Overview: His publications explore density-based clustering methods for big data, sentiment analysis frameworks, and cybersecurity threat detection. Notably, he has contributed to optimizing clustering algorithms using MapReduce and enhancing digital forensic methodologies. Advising & Grants: No specific advising or grant information is highlighted in the provided materials.

Kevin WebbView profile

Associate Professor

• Computer Networks
• Distributed Systems
• Computer Science Education
• +3 more

Kevin Webb is an Associate Professor in the Computer Science Department at Swarthmore College. His research focuses on networks, distributed systems, and computer science education. He earned his Ph.D. in 2013 from UC San Diego under advisors Alex Snoeren and Ken Yocum. Webb is a co-author of the free textbook *Dive into Systems*, designed for introductory computer systems education. He has taught courses including CS31 (Computer Systems), CS43 (Computer Networks), CS45 (Operating Systems), and directed readings on systems security. Education : Ph.D. in Computer Science, UC San Diego, 2013 Research Interests : Webb’s work spans networked systems, distributed computing, and educational innovations in CS curricula. He emphasizes integrating parallel computing concepts into early coursework and developing validated assessment tools like the BDSI concept inventory for data structures. His educational initiatives aim to improve accessibility and engagement in foundational CS topics. Publications : His recent work includes contributions to computer science education, parallel computing pedagogy, and distributed systems design. Notable publications focus on textbook development, concept inventory validation, and network optimization techniques. Teaching & Mentoring : Webb has advised numerous courses and student projects. He emphasizes hands-on learning through lab-based assignments and peer instruction models. His teaching portfolio includes systems programming, operating systems, and network architecture courses.

Ilias TachmazidisView profile

Senior Lecturer

• Knowledge Representation and Reasoning
• Semantic Web
• Artificial Intelligence in Medicine
• +3 more

Ilias Tachmazidis is a Senior Lecturer in the Department of Computer Science at the University of Huddersfield's School of Computing and Engineering. He actively contributes to research within the Centre for Autonomous and Intelligent Systems and the Centre of Artificial Intelligence for Mental Health. His academic journey includes a PhD from the University of Huddersfield (2015) and previous research roles in the PARK group. Education: University of Crete (B.Sc. 2010, M.Sc. 2012) Current academic rank: Senior Lecturer Active research centers: Centre for Autonomous and Intelligent Systems, Centre of Artificial Intelligence for Mental Health His research spans foundational AI concepts and applied implementations, with specific emphasis on: Knowledge Representation and Reasoning Semantic Web Technologies High Performance and Distributed Computing Artificial Intelligence Applications in Medicine and Law Recent publications highlight his work at the intersection of large language models and formal verification, alongside practical implementations for ADHD diagnosis using AI. The research demonstrates consistent engagement with UN Sustainable Development Goals through technology solutions that balance scientific rigor with societal impact. While not explicitly listing scientific awards, his activities show substantial contributions through peer-review work at IEEE conferences (2016-2020) and collaborative projects like MIREL (MIning and REasoning with Legal texts) that produced notable outputs in regulatory compliance systems.

Theodoridis IoannisView profile

Professor

• Data Science
• Databases
• Big Data Management
• +3 more

Professor Theodoridis Ioannis is a distinguished faculty member in the Department of Informatics at the University of Piraeus, where he serves as Director of the Data Science Laboratory within the School of Information and Communication Technologies. With a career spanning over two decades, he has established himself as a leading expert in data management and analysis. His research interests focus on Data Science, particularly in databases, big data management, data mining, and geoinformatics. Professor Theodoridis has made significant contributions to spatial database systems, time series analysis, and distributed data processing. His work bridges theoretical foundations with practical applications in areas such as smart cities, mobility analytics, and scientific data management. His publication record demonstrates consistent research productivity with over 100 peer-reviewed articles in top-tier venues, accumulating more than 10,000 citations. His research output shows a clear evolution from foundational database techniques toward contemporary challenges in big data analytics, machine learning integration, and privacy-preserving methods. Member of editorial board of ACM Computing Surveys (since 2016) Reviewer for numerous international journals and conferences Active participant in data management conference committees Professor Theodoridis has secured significant research funding through Horizon 2020 projects, serving as project coordinator and research team leader since 2001. His work demonstrates strong industry and academic collaboration, with applications spanning multiple domains. He has also co-authored three influential monographs in his field. He leads the Data Science Laboratory, which serves as a hub for interdisciplinary research at the intersection of database systems, machine learning, and domain-specific applications. The laboratory fosters collaboration between computer scientists, domain experts, and industry partners to address real-world data challenges.