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Jaakko Timo Henrik JärviView profile

Professor

• Generic Programming
• Programming Language Design
• Graphical User Interfaces (GUI)
• +7 more

Jaakko Timo Henrik Järvi is a Professor in the Department of Informatics at the University of Bergen, Norway, with additional affiliations at the University of Turku, Finland. His research focuses on programming language design, generic programming, and human-computer interaction, particularly in GUI frameworks and software reuse. His research interests include generic programming, programming language design (especially the Magnolia language), high-performance computing, array programming, and GUI engineering. He emphasizes formal methods and algebraic specifications to build reusable and efficient software systems. His work bridges theoretical foundations with practical applications in software development and education. The recent publications highlight a strong trend in declarative GUI frameworks, multi-selection models, and generic programming. His work explores domain-specific languages for GUI structure manipulation, reusable selection semantics across platforms, and optimizing array computations using the Mathematics of Arrays. These efforts reflect a consistent focus on software abstraction, correctness, and reusability. Jaakko Järvi has supervised doctoral students, including Tetiana Yarygina, whose dissertation explored microservice security. While no specific grants are detailed, his work on VisAST was supported by the Research Council of Norway (Project 250683), indicating active external funding. He frequently collaborates with researchers like Magne Haveraaen, Knut Anders Stokke, and Sean Parent. He contributes to tools and frameworks such as the MultiselectJS library and the VisAST educational tool. These are outcomes of collaborative research teams focused on improving software development practices and computer science education.

• Data Mining
• Bioinformatics
• Machine Learning
• +3 more

Domingo Savio Rodríguez Baena is a Professor at Pablo de Olavide University, affiliated with the Department of Computer Languages and Systems. His research focuses on data mining, bioinformatics, and computational biology, with a particular emphasis on biclustering algorithms, gene co-expression networks, and high-performance computing applications. PhD in Engineering, Data Science, and Bioinformatics (2012) from Pablo de Olavide University His work spans interdisciplinary domains, including recommender systems , livestock behavior analysis , and biological data interpretation . Recent articles highlight his contributions to multi-GPU optimization , ensemble learning , and historical database construction . Key collaborations include the DATAi Intelligent Data Analysis and DASE Data Analytics Science & Engineering research groups. He has developed tools like the CyEnGNet–App for gene network visualization and BIGO for gene enrichment analysis. Contact: dsrodbae@upo.es

Bruce WigginsView profile

Associate Professor

• Audio Engineering
• 3D Audio
• Ambisonics
• +5 more

Bruce Wiggins is an Associate Professor in Audio Engineering at the College of Science and Engineering. His research focuses on spatial audio technologies, including Ambisonics, binaural auralization, and 3D audio systems. Notable projects include the GASP guitar system and WHAM webcam-based head-tracked audio solutions. He has contributed to advancements in microphone array calibration, speaker array modeling, and virtual reality audio integration. His work bridges theory with practical applications in music technology and acoustic engineering. Education: PhD in Audio Engineering (2004). Research Interests: Ambisonics, spatial audio capture and reproduction, 3D audio for virtual reality, binaural rendering, and innovative musical instrument design. His work emphasizes practical implementations such as the GASP guitar system and calibration tools for low-cost microphone arrays. Article Trends: Recent publications address virtual stereo microphone techniques (2024), dynamic electrical systems (2024), and browser-based 3D audio (2023). Earlier work explores head-tracking algorithms (2016–2020) and acoustic modeling for domestic environments (2017). Grants/Advising: No explicit grants listed. Advising details unavailable but has collaborated with numerous researchers on projects like WHAM and GASP. Labs/Teams: Active in interdisciplinary teams developing spatial audio tools and instruments, including collaborations on virtual reality auralization and ambisonic guitar systems.

• Computer Science
• Networking Systems
• Software Engineering
• +5 more

Xin Wang is a Professor at Fudan University's School of Computer Science, specifically within the Department of Communication Science and Engineering and affiliated with the State Key Laboratory of ASIC and System in Shanghai, China. With 185 publications spanning two decades (2003-2025), Wang maintains an exceptionally active research profile, particularly evident in recent high-output years including 22 publications in 2019, 19 in 2021, and 13 in 2024. The research portfolio demonstrates deep collaboration networks, most notably with Yang Chen (45 co-authored papers), Yangfan Zhou, and Qingyuan Gong. Wang's research spans multiple critical areas in computer science, with significant contributions to networking systems (particularly CDN optimization, HTTP/3 implementation, and IPv6 infrastructure), software engineering (focusing on work rhythms, testing methodologies, and GUI analysis), mobile applications (including healthcare implementations and accessibility features), and security (especially account security and fraud detection in e-commerce). The interdisciplinary nature of the work is evident through applications in healthcare, e-commerce, campus safety, and IoT systems. Analysis of recent publications (2023-2025) reveals a strong trend toward practical system implementations addressing real-world challenges. The research demonstrates a consistent pattern of moving from theoretical foundations to deployable solutions, with particular emphasis on optimizing performance in networking systems, enhancing security in digital platforms, and improving user experience across diverse application domains. The work frequently incorporates machine learning techniques to solve complex system problems while maintaining practical applicability. While specific grant information isn't detailed in the publication records, the extensive collaboration network spanning multiple institutions in China and internationally suggests substantial research funding support. The consistent publication output across top venues including IEEE/ACM Transactions, INFOCOM, SIGCOMM, and ICSE indicates sustained research productivity and impact.

Giorgio ScorzelliView profile

Researcher

• Extreme Data Management
• Scientific Visualization
• Computational Topology
• +6 more

Giorgio Scorzelli is a researcher at the University of Utah, serving as Director of Software Development for the Center for Extreme Data Management, Analysis, and Visualization (CEDMAV) and the National Science Data Fabric (NSDF) . He specializes in extreme data management, scientific visualization, and computational topology, with a focus on scalable solutions for climate science, materials science, and neuroscience datasets. His work emphasizes democratizing data access through platforms like OpenVisus , enabling efficient analysis of petascale and exascale data. Key contributions include orchestrating cyberinfrastructure, optimizing parallel I/O, and developing real-time visualization systems for heterogeneous resources. Notable scientific contributions include the NSF Grant #2127548 for NSDF development . His projects integrate cloud computing, geo-distributed storage, and FAIR digital objects to lower barriers to data democratization. Giorgio's research spans multi-resolution algorithms , computational topology , and 3D geometric modeling , with applications in infrastructure security, archaeological reconstruction, and biomedical imaging. His work bridges abstract mathematical frameworks (e.g., Boolean algebras, chain complexes) with practical software solutions.

Patrick LamView profile

Associate Professor

• Static Program Analysis
• Verifiable Software Specifications
• Software Engineering
• +8 more

Patrick Lam is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Waterloo, with a cross-appointment to the Cheriton School of Computer Science. His research focuses on applications of programming languages and static analysis to software engineering challenges, emphasizing verifiable software specifications and program understanding. Dr. Lam has held significant grants from NSERC and is recognized for his impactful work, including the First Decade High Impact Paper award for his Soot framework. Education: Doctorate in Computer Science, Massachusetts Institute of Technology, 2007 Master's in Computer Science, McGill University, 2000 Bachelor's in Joint Honours Mathematics and Computer Science, McGill University, 1999 Research Interests: His primary areas include static program analysis, verifiable software specifications, and compiler design, with a focus on linking high-level software designs to low-level implementations. He explores techniques like lightweight specifications and domain-specific languages to enhance software reliability and efficiency. Recent work also addresses empirical studies of programming practices and security through modularization. Publications: Dr. Lam's recent publications span advancements in static analysis tools (e.g., WasmWalker for WebAssembly), formal verification of code generated by AI tools like GitHub Copilot, and empirical studies on C++ immutability usage. His work bridges theoretical programming language research with practical software engineering applications, emphasizing tools for developer productivity and code reliability. Awards and Recognition: First Decade High Impact Paper recognition for "Soot – A Java Optimization Framework" (2010) Teaching and Grants: He has taught courses such as CS 447, ECE 453, and ECE 459 on software testing and performance programming. Active in grant-funded research, he secured NSERC Engage Grant (2013) and an ongoing NSERC Discovery Grant (2013–2018). Lam has also advised graduate students and contributed to the Software Engineering Program at Waterloo as its Director (2016–2019). Labs and Teams: His research group explores topics in program analysis and software engineering, with collaborations on projects like abstract debugging tools (GobPie) and static analysis frameworks (Soot). He maintains an open-source repository on GitHub, contributing to educational materials and research tools.

Yasir ZakiView profile

Assistant Professor

• Communication Networks
• Wireless Networks
• Cellular Communication
• +9 more

Yasir Zaki is an Assistant Professor of Computer Science at New York University Abu Dhabi and a Global Network Assistant Professor at the Courant Institute of Mathematical Sciences, NYU. He leads the Communication Networks Lab, focusing on next-generation communication systems, performance optimization, and digital equity. University: New York University Abu Dhabi School: Courant Institute of Mathematical Sciences Department: Department of Computer Science Academic Rank: Assistant Professor Email: yz48@nyu.edu Dr. Zaki holds an MSc and PhD in Communication and Information Technology from the University of Bremen, graduating with honors. His research centers on communication and wireless networks, cellular systems, congestion control, and enhancing internet access in developing regions. His work bridges theory and real-world impact, especially in digital inclusion and AI's role in education. His recent publications span top venues like PNAS, IEEE TCSS, and ACM IMC, covering topics such as satellite network performance, digital inequality (Lite-Web), AI in education, and Big Tech's global influence. These works reveal a strong trend toward socially impactful computing, network measurement at scale, and algorithmic transparency. Big Tech Dominance Despite Global Mistrust Perception of AI in Education YouTube's Political Bias Lite-Web for Digital Equity Satellite Network Analysis His research has been recognized through high-profile media coverage in Nature and The National , and his PhD student Hazem Ibrahim received the MIT Technology Review Arabia’s Innovators Under 35 MENA 2023 award. This reflects the lab's excellence in computational social science and AI policy. Dr. Zaki mentors students in the Capstone and Research Seminar courses and actively advises PhD and research assistants. He has secured research funding through NYUAD and collaborative projects, enabling field deployments in 56 countries. His lab, the Communication Networks Lab, fosters interdisciplinary work, involving researchers from computer science, social sciences, and policy.

Shrideep PallickaraView profile

Professor

• Machine Learning
• Deep Learning
• Large-Scale Systems
• +12 more

Shrideep Pallickara is a Professor in the Department of Computer Science at Colorado State University, where he also directs the Center for eXascale Spatial Data Analytics and Computing (XSD) . His research is funded by the National Science Foundation, Department of Homeland Security, Environmental Protection Agency, Department of Agriculture, and the UK's e-Science program. Research Interests: His research lies at the intersection of machine learning and large-scale systems, focusing on: Spatiotemporal data management and analytics Extreme-scale storage systems Stream processing for IoT and cyber-physical systems Deep learning over petabyte-scale, high-dimensional datasets Model construction for forecasting natural and urban phenomena His work addresses challenges in computational tractability, resource utilization, and convergence in distributed environments. Systems developed in his lab are deployed in domains such as urban sustainability, agriculture, epidemiology, environmental monitoring, healthcare, and defense. Research Trends in Publications: His recent publications demonstrate a strong focus on scalable analytics for geospatial and environmental data. Key themes include deep learning for soil moisture and salinity prediction, efficient visualization of massive satellite datasets, spatiotemporal search and summarization, and model performance profiling across spatial domains. The work integrates scientific domain knowledge with machine learning and systems innovation. Scientific Awards: NSF CAREER Award Board of Governors Award for Excellence in Undergraduate Teaching OLIE Award N. Preston Davis Award Monfort Professorship Best Paper Award at IEEE/ACM CCGrid 2019 Best Paper Award at BDCAT 2023 Best Paper Award at IEEE Cluster 2012 Best Student Paper Award at IEEE CloudCom 2010 Shortlisted for ACM DEBS-2015 Grand Challenge Award One of the Six Best Papers at ACM/IEEE GRID 2005 Advising and Grants: He advises numerous graduate students, many of whom are co-authors on his publications. His research is supported by major grants from NSF, DHS, EPA, USDA, and UK e-Science, enabling the development of open-source systems such as Granules, NaradaBrokering, Galileo, Funnel, and Spindle. Labs and Teams: He leads the XSD Center, which develops and maintains large-scale open-source software systems involving over 2500 classes and a million lines of code. These systems are used in academic, commercial, and defense applications.

Gergely KocsisView profile

Associate Professor

• Information spreading
• Agent-based simulations
• Cellular automata
• +1 more

Dr. Gergely Kocsis serves as an Associate Professor at the University of Debrecen's Faculty of Informatics, Department of Informatics Systems and Networks. His office is located in the Faculty of Informatics building at 4028 Debrecen, Kassai út 26, ground floor, IF13 (Lecturers' room), with contact email kocsis.gergely@inf.unideb.hu and central telephone +36 52 512 900 75013. Dr. Kocsis's research program focuses on: Information spreading phenomena Agent-based and individual-based simulations Cellular automata applications Network structure and dynamics His scholarly output reveals a sophisticated research trajectory evolving from foundational work on cellular automata modeling of social dynamics (2007-2014) to contemporary investigations of transportation networks, VANETs, and AI applications. The 2023-2025 publications demonstrate particular expertise in network extraction methodologies, containerized computing environments, and the application of generative AI to productivity challenges. His work consistently applies computational modeling approaches to understand complex spreading phenomena across diverse network structures. Dr. Kocsis maintains comprehensive scientific profiles across major academic platforms including Google Scholar, ResearchGate, ORCID, Scopus, and Web of Science, demonstrating active participation in the international research community. His departmental colleagues work in complementary areas such as complex networks, embedded systems, and neural networks, suggesting rich collaborative opportunities within the Faculty of Informatics.

Peter ButcherView profile

Lecturer

• Immersive Analytics
• Human-Computer Interaction
• Data Visualization
• +4 more

Peter Butcher is a Lecturer in Human-Computer Interaction at Bangor University's School of Computer Science and Engineering. With a PhD in Computer Science from the University of Chester, he specializes in visualization techniques and immersive analytics, particularly focusing on Virtual Reality, Augmented Reality, and WebXR platforms. 2015-2020: PhD, Computer Science, University of Chester 2014-2015: MSc, Computer Science, Bangor University 2011-2014: BSc, Computer Science, Bangor University His research interests center on Immersive Analytics , Human-Computer Interaction , and Data Visualization . He explores how contextual awareness and multivocality in visualization can enhance user experience and data interpretation. His work spans from technical frameworks like WebXR to applications in e-lexicography and flood risk management. Recent publications highlight his contributions to collaborative platforms ( DashSpace ), attention-aware systems ( Attention-Aware Visualization ), and magic-inspired tools ( Wizualization ). He also investigates educational applications of visualization, particularly in critical thinking development. Scientific achievements include the Best Full Papers Honourable Mention at IEEE VIS 2021 and recognition as a Fellow of the Higher Education Academy (FHEA) via Bangor University's CPD Recognition Scheme. He actively supervises doctoral students in areas like AI for Legal Technologies and Multivocality in Visualization. Peter contributes to teaching modules on Web Technologies , Game Production , and High Performance Computing . He also co-founded Sim Broadcasts and serves as Chairman of Rethink Racing , demonstrating his engagement beyond pure academia.

Rodriguez Zalepinos Ramon AntonioView profile

Associate Professor

• distributed systems
• databases
• Big data
• +6 more

Dr. Ramon Antonio Rodriguez Zalepinos is an Associate Professor at the Department of Software Engineering, Faculty of Computer Science, National Research University Higher School of Economics (HSE). With 16+ years of scientific and teaching experience, he specializes in geospatial data systems, distributed databases, and high-performance computing. Doctor of Science in Computer Science (2024) Candidate of Technical Sciences (2013) Master's in Computer Science (2008, Donetsk National Technical University) His research focuses on geospatial array databases , distributed systems , and Big Earth Data engineering , particularly through his ChronosDB and Quantum Tensor DBMS projects. He has pioneered cloud-native solutions for multi-terabyte environmental datasets and developed novel approaches for in-database road traffic simulations. Key publication trends show 7+ years of contributions to VLDB and SIGMOD conferences, with special emphasis on: Quantum-enhanced geospatial processing Web-based array database systems Cellular automata integration High-speed raster data aggregation Scientific recognition includes: Best Teacher award (2017-2021, 2023) HSE Personnel Reserve member Additional post-doctoral funding (2024-2027) Multiple publication bonuses (2019-2023) He supervises student research in geospatial data science and leads projects on satellite data processing systems, with implementations at major institutions including Amazon and Planet Labs.

Brian R. TooneView profile

Assistant Professor

• Web Engineering
• Software Engineering
• Trust and Security
• +6 more

Brian R. Toone serves as an Assistant Professor in the Department of Mathematics and Computer Science within Samford University's Howard College of Arts and Sciences. A native of Hoover, Alabama, he teaches in Samford's growing computer science program with a focus on one-on-one student interaction. Toone holds a BS in Computer Engineering from Clemson University and both MS and PhD degrees in Computer Science from the University of California-Davis. His educational background prepared him for a career that blends academic research with practical software development experience. His research spans multiple areas of computer science with particular focus on web engineering, software engineering, trust and security systems, programming languages, database applications, and parallel processing. Toone is actively working to apply software engineering concepts to web application development and optimization, with special interest in bringing parallel computation capabilities to web applications. He is currently building a virtual supercomputer by networking department computers to donate idle processing cycles across campus. His publication history reveals consistent research in trust mediation for distributed information systems, with significant work between 1998-2008. His research evolved from control flow prediction in high-performance computing (1998) to web engineering and Ajax applications (2006-2008), showing adaptability to changing technology landscapes. Toone is an active member of professional organizations including the Association for Computing Machinery (ACM) and the Institute for Electrical and Electronics Engineers (IEEE). His research has been presented at numerous conferences including the ACM Mid-Southeast Chapter, Alabama Academy of Science, and various security and information systems conferences. Beyond academia, Toone is a competitive cyclist who has participated in major endurance cycling events including Paris-Brest-Paris (2023) and Race Across America (2017), where he raised awareness for Duchenne Muscular Dystrophy through the Ride4Gabe initiative.

Akos LedecziView profile

Professor

• Computer Science Education
• Wireless Sensor Networks
• Model Integrated Computing
• +3 more

Akos Ledeczi is a Professor of Computer Science and Electrical and Computer Engineering at Vanderbilt University's School of Engineering. His research focuses on computer science education and wireless sensor networks (WSN) , with notable contributions to visual programming tools like NetsBlox and anti-poaching systems like WIPER. He holds a Ph.D. from Vanderbilt University and a Diploma from the Technical University of Budapest. His education initiatives include the NetsBlox platform for K-12 STEM education and a popular Coursera MOOC on introductory programming. In WSN, his team developed a countersniper system with real-time shooter localization and a wearable system for military applications. He is also a leader in Model Integrated Computing , creating tools like the Web-based Generic Modeling Environment. Recent work includes NSF-funded projects on cybersecurity education in middle schools and animal-borne acoustic monitoring. His awards include the ACM SenSys Test of Time Award (2014) and the Best Showpiece Award at IEEE VL/HCC (2021). Key grants and collaborations span NSF projects, Vodafone innovation programs, and the Cyber Makerspace initiative. His lab develops tools like DeepForge and RoboScape Online , emphasizing open-source and collaborative platforms. Students supervised include Devin Jean (K-12 tools), Gordon Stein (robotics simulations), and Brian Broll (NetsBlox extensions).

• Scan-to-BIM
• Geomatics
• Building Information Modeling
• +7 more

Maarten Bassier is an Assistant Professor (tenure track) at KU Leuven, affiliated with the Department of Civil Engineering within the Faculty of Engineering Technology. He is based at the Geomatics unit operating at the Ghent and Aalst Campuses. His academic profile combines research, teaching, and institutional service, with significant contributions to the field of digital construction technologies. As senior academic staff, he serves on both the Council of the Faculty of Engineering Technology and the Civil Engineering Department Council, actively participating in institutional governance while maintaining a robust research program focused on Scan-to-BIM methodologies and geospatial applications in construction. Dr. Bassier's research centers on Scan-to-BIM methodologies, which involve converting 3D scans of existing buildings into Building Information Models. His work bridges geomatics, computer vision, and civil engineering, with applications in construction progress monitoring, infrastructure inspection, and heritage documentation. He applies machine learning techniques to automate aspects of the modeling process, particularly semantic segmentation of point clouds and integration of UAV (drone) data. His research increasingly incorporates deep learning approaches for object detection, segmentation, and completion in complex built environments, with practical applications spanning road construction, bridge inspections, and electrical substation modeling. His interdisciplinary approach connects civil engineering with computer science to solve practical construction challenges through digital innovation. Bassier's recent publication record demonstrates a strong focus on automating the Scan-to-BIM process through advanced computational techniques. His work spans multiple application domains while maintaining a consistent methodological thread of integrating sensing technologies with semantic understanding of construction environments. The trajectory of his research shows increasing sophistication in machine learning applications, moving from basic point cloud processing to complex semantic understanding and automated model generation. His publications appear in high-impact journals across civil engineering, remote sensing, and computer vision domains, reflecting the interdisciplinary nature of his work. SESAME - Semantic Segmentation of Electrical Substations and Derived Models for Engineering (2024-2026) - Promotor UAV-assisted bridge inspections (2022-2027) - Co-promotor XR-empowered dynamic reality modeling for AECO applications (2021-2026) - Co-promotor Digitization in road construction: automating as-built models (2020-2026) - Co-promotor SCAN-to-BIM Automation of as-built BIM production through digitization and machine learning (2020-2025) - Co-promotor As a member of the Division Digital and Sustainable Civil Engineering and the Subdivision Geomatics Ghent, Dr. Bassier contributes to KU Leuven's research ecosystem focused on digital transformation in civil engineering. His teaching portfolio includes courses on BIM, industrial measurements, Scan-to-BIM, 3D modeling, and geomatics, preparing the next generation of civil engineers for the digital construction landscape. His work represents the cutting edge of digital construction technologies, with practical applications that address real-world challenges in infrastructure development and maintenance.

John PaparrizosView profile

Assistant Professor

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

John Paparrizos is an Assistant Professor of Computer Science and Engineering at The Ohio State University's College of Engineering, where he directs The DATUM Lab (Data Analytics, Understanding, Mining, and Management Lab). He maintains an adjunct affiliation with the School of Informatics at Aristotle University of Thessaloniki. His research spans databases, data science, machine learning, and artificial intelligence , with focus areas including: Time-series analysis (clustering, anomaly detection) Scalable data mining for structured/unstructured data Adaptive algorithms for resource-constrained environments Foundational technologies for data-intensive applications His work addresses real-world challenges across relational, time-series, multimedia, text, graph, web, and IoT data domains. Notable recognition includes: 2025 ACM SIGMOD Test-of-Time Award for k-Shape time-series clustering 2023 IEEE TCDE Rising Star Award ACM SIGMOD Research Highlight Award NetApp Faculty Award His research has been featured in New York Times (front page), Washington Post , Forbes , and adopted by Fortune 500 companies (Exelon, Nokia) and the European Space Agency. He actively serves on program committees for premier conferences including ACM SIGMOD, VLDB, IEEE ICDE, ACM SIGKDD, and NeurIPS. His open-source tools have exceeded 100,000 downloads and are integrated into academic curricula at Brown, Columbia, Purdue, and University of Chicago.