Earl Barr is a Professor of Software Engineering at the Department of Computer Science, University College London. His research focuses on software engineering, data management and data science, and neural software engineering approaches. Research Interests: Dr. Barr's work bridges traditional software engineering with modern machine learning techniques. He explores automated program repair, code summarization, log anomaly detection, and the application of neural models to software development workflows. His studies often address both practical challenges in industry and theoretical aspects of code analysis. Article Trends: His recent publications emphasize the integration of large language models (LLMs) into software engineering tasks, including fact selection for program repair, semantic prompt augmentation, and grid-based code representation. Other recurring themes include empirical studies of development practices, fuzzing techniques for program analysis, and the use of surprisal theory in issue tracking systems. Scientific Awards: ACM Sigsoft Distinguished Paper Award (2013) Best Paper Award - IEEE Conference on E-Commerce Technology (2005) I3P Fellowship (2009-10) Professional Activities: Dr. Barr has served as a visiting assistant professor at the University of California Davis (2013) and as a reviewer for top software engineering conferences and journals including ICSE, ISSTA, FSE, SAS, TOSEM, TSE, and the DARPA MURI Program. He also contributed to commercialization efforts through a patented approach leveraging statistical analysis from large software corpora for engineering tools.
Chenglong Fu is an Assistant Professor in the Department of Software and Information Systems at the University of North Carolina at Charlotte. He earned his Ph.D. in Computer and Information Sciences from Temple University (2022) and a BEng in Information Security from the University of Science and Technology of China (2015). His research focuses on security and privacy for Internet of Things (IoT) and Cyber-Physical Systems (CPS), with special emphasis on AI/ML-driven security solutions, anomaly detection, and vulnerability discovery. Ph.D., Computer and Information Sciences, Temple University (2016-2022) BEng, Information Security, University of Science and Technology of China (2011-2015) Research Interests: Security and privacy in IoT and CPS environments Anomaly detection using semantic pattern analysis Vulnerability discovery through formal verification and fuzzing AI/ML applications for real-time threat mitigation Smart Home Systems (SHS) security Industrial Control Systems (ICS) protection Recent Research Trends: His 2024 publications demonstrate focus on large language models for security auditing, few-sample anomaly detection, cross-platform IoT delegation vulnerabilities, and cloud-based IoT attack vectors. These works combine formal verification, empirical analysis, and AI techniques. Scientific Awards: Scott Hibbs Future of Computing Award (2021) Outstanding Undergraduate Scholarship (2014) Seagate Scholarship (2013) Outstanding College Graduate (2015) Teaching: Instructed Secure Programming, Penetration Testing, and OS/Neworks courses at UNC Charlotte (2023-present) and Temple University (2017-2020).
Dr. Borja Sanz Urquijo serves as a Senior Lecturer at the Faculty of Engineering, University of Deusto, and has been a core researcher at DeustoTech-Computing since 2008, including a tenure as Head Researcher (2015-2018). He holds a cum laude PhD in Information Systems (2012) from the University of Deusto, specializing in Android malware detection. Education PhD in Information Systems, University of Deusto (2012, cum laude) His research spans machine learning, big data, and knowledge discovery, with critical expansion into AI ethics, fairness, accountability, and societal impact. He investigates AI applications in domestic violence intervention, health rights, law enforcement transparency, and Edge Computing optimization, consistently bridging technical innovation with social responsibility. His work demonstrates rigorous methodology in small-dataset machine learning and genomic sequence analysis. Dr. Sanz Urquijo's publication trajectory reveals evolving expertise from foundational cybersecurity (Android malware analysis, spam filtering) to contemporary societal challenges (feminist AI frameworks, quantum software security). Recent articles emphasize interdisciplinary collaboration, particularly in feminist technology studies and ethical AI governance, while maintaining technical depth in Edge Computing and genomic analytics. Advising and Projects He has supervised multiple theses including doctoral work on Edge Computing for digital twins and cybersecurity competency frameworks. As lead researcher in over 50 projects (H2020, national, private), he currently directs BEACON (industrial AI systems) and contributes to EU initiatives like IMPROVE (domestic violence response) and ELKARTEK (Industry 5.0 ethics). His collaborations span social organizations, enterprises, and research centers globally. Research Environment As a pillar of DeustoTech-Computing, he operates within a multidisciplinary unit advancing AI, cybersecurity, and Edge Computing applications. His leadership in projects like AI-Driven Cognitive Robotic Platforms and REal tiME control systems demonstrates integration of theoretical research with industrial implementation in smart manufacturing contexts.
Carla Fabiana Chiasserini is a Full Professor and Deputy Director at the Department of Electronics and Telecommunications (DET) at the Polytechnic University of Turin. She serves as a Component of the CARS@PoliTO Interdepartmental Center - Center for Automotive Research and Sustainable Mobility and acts as a Spoke leader for research and innovation activities. Her academic career spans multiple prestigious institutions and she maintains active collaborations worldwide. Professor Chiasserini's research interests span algorithm design and analysis, cellular networks, connected cars, edge computing, heterogeneous wireless networks, Internet of Things, machine learning, mobile networks, mobile services, and performance evaluation. Her work bridges theoretical algorithm development with practical applications in next-generation telecommunications systems. She leads the TNG research group at DET and focuses on Machine Learning for Networking, with specific research lines in Network Slicing in 5G, Connected autonomous cars, and Opinion dynamics in social networks. Her research aligns with Sustainable Development Goals including Industry, Innovation and Infrastructure; Sustainable Cities and Communities; and Climate Action. Her recent publications demonstrate a strong trend toward integrating machine learning with edge computing, 5G/6G systems, and automotive applications. The research spans from theoretical algorithm development to practical implementations for XR offloading, distributed service provisioning, reliability assessment of AI-based automotive systems, and satellite networking. Her work shows increasing focus on practical implementations with industry applications, particularly in the automotive sector and next-generation telecom infrastructure. Best Paper Award - Wireless Telecommunications Symposium (WTS) 2018 Best Paper Award - IEEE WoWMoM 2016 Best Paper Award Runner-up at ACM MSWiM 2016 Top Paper Award at the ACM CoNEXT 2016 Cloud-Assisted Networking (CAN) Workshop Best Paper Award at SPACOMM 2014 Best Paper Award at AD HOC NOW 2014 2010 Editor of the Year Award for the Ad Hoc Networks journal (Elsevier) IEEE Fellow (2018-) ACM Fellow (2024-) Professor Chiasserini actively advises numerous PhD students working on cutting-edge topics in network systems, with recent graduates focusing on edge services in 5G networks, resource-aware learning in mobile networks, and deployment of microservices at the network edge. She leads multiple significant research grants including O-RAN (2024-2027), CSI-Future (2023-2025), RESTART - Spoke 4 (2023-2025), PREDICT-6G (2023-2025), and several others funded by PNRR, EU Horizon programs, and industry partnerships. Her work has substantial practical impact through numerous patents including OffloaDNN and SEM-O-RAN. She leads the TNG research group within the Department of Electronics and Telecommunications and participates in the CARS@PoliTO Interdepartmental Center for Automotive Research and Sustainable Mobility. Her laboratory work focuses on practical implementations of theoretical concepts, particularly in the areas of connected vehicles, edge computing, and 5G/6G systems. She maintains strong industry connections through projects with Intel Corporation and other technology partners, ensuring her research has direct real-world applications.
Zhong Li is an Associate Professor in the Department of Civil Engineering at McMaster University, specializing in water resources systems, environmental modeling, and machine learning applications. His research focuses on integrating advanced computational techniques with hydrological processes to address climate change impacts, infrastructure resilience, and water quality management. Key research areas include hydrological modeling under uncertainty, wastewater treatment optimization, and AI-driven environmental systems Develops hybrid models combining machine learning with physical/ontological frameworks for improved predictive accuracy Recipient of the 2025 University Scholar award at McMaster for mid-career research leadership Recent publications demonstrate expertise in global flood forecasting, membrane fouling prediction, SARS-CoV-2 wastewater surveillance, and climate change adaptation strategies. His work spans water treatment plants, Canadian river systems, and transboundary environmental challenges. Scientific Awards: University Scholar 2025 (McMaster University) Teaching responsibilities include courses in environmental engineering, water resources systems design, and specialized civil engineering studies. Collaborates extensively with interdisciplinary teams on infrastructure safety and sustainability projects.
Professor Klaus Berberich is a faculty member at htw saar (Saarland University of Applied Sciences), where he serves as Professor in the Databases & Information Systems department within the Faculty of Engineering. He is the Laboratory manager of the software laboratory (SWL) and Chairman of the examination boards for Practical Computer Science, Communication Informatics and Production Informatics. His research focuses on Information Retrieval, Machine Learning, Data Mining, and Web Archives, with significant contributions to knowledge graphs, temporal information retrieval, and neural information retrieval models. Professor Berberich has developed innovative approaches for quantity extraction from web tables, structuring text into tables, and knowledge graph querying. His publication record shows a consistent trend toward increasingly sophisticated neural approaches to information retrieval, evolving from traditional temporal search techniques to modern deep learning models. Recent work demonstrates strong integration of knowledge graphs with neural information retrieval systems, particularly in handling quantities and temporal aspects of information. Professor Berberich has received numerous prestigious awards throughout his career: 2020: Test of Time Award, ECIR 2020 2018: Honorable Mention for Best Poster Award, WWW 2018 2017: Prominent Paper Award, Artificial Intelligence Journal 2014: Highly Commended Poster Presentation Award, IIiX 2014 2013: Honorable Mention for Best Paper Award, CIKM 2013 2011: Best Demo Award, WWW 2011 2009: Best Late-Breaking Result Award, WSDM 2009 As an active researcher and educator, Professor Berberich serves on numerous program committees for major conferences including WSDM, CIKM, SIGIR, and ICTIR. He has been a consistent reviewer for prestigious journals in the field and is a member of the executive committee of the Information Retrieval specialist group of the German Informatics Society. His teaching portfolio includes courses in Databases, Information Retrieval, Data Science, Machine Learning, and Deep Learning. Professor Berberich leads the software laboratory (SWL) at htw saar and has been instrumental in developing research infrastructure for knowledge-centric tasks, including the GYANI indexing infrastructure. His research group has made significant contributions to temporal information retrieval, particularly in the context of web archives and news archives.
Pradip K Srimani is a Professor at the School of Computing , Clemson University , with research focus on Parallel and Distributed Computing , Self-Stabilizing Systems , and Graph Theory Applications . He is an IEEE Life Fellow and ACM Distinguished Scientist with over 250 publications. Research Interests Self-stabilizing algorithms for network graphs Ontology-based biomedical information retrieval High-performance computing resource management Biologically inspired distributed algorithms Network topology computation Recent publications demonstrate expertise in token circulation protocols, genome assembly frameworks, and semantic similarity measurement. He has served on program committees for APDCM , GPC-2018 , and IEEE BigMM conferences, while maintaining editorial roles at International Journal of High Performance Computing and Journal of Big Data . Scientific Awards IEEE Life Fellow ACM Distinguished Scientist
Francesca Boccuni is a Professor at the Faculty of Philosophy, Università Vita Salute San Raffaele, with additional contractual teaching roles at Università della Svizzera Italiana (USI). Her research focuses on the Foundations of Mathematics , Classical and Non-Classical Logics , and the Metaphysics of Abstract Objects , alongside work in Philosophy of Language and Ontology . Current affiliations: USI (Professore a Contratto), UNISR (Università Vita Salute San Raffaele) Major grants: PRIN 2023 (Italian Ministry of Research), British Academy Postdoctoral Fellowship (2008) Research Trends : Her publications (2020–2024) emphasize plural logic , neo-logicism , and Fregean foundations , with recent work extending to qualitative social ontology and eidetic phenomenology . Scientific Awards : British Academy Postdoctoral Fellowship (2008), PRIN Grant (2023) Editorial roles: Argumenta, Bulletin of Symbolic Logic, Logique et Analyse Academic Leadership : Co-founder of the Italian Network for Philosophy of Mathematics (FilMat, since 2012); served on program committees for international philosophy of mathematics conferences.
Robert Glück is a Professor at the Department of Computer Science under the Faculty of Science , University of Copenhagen. He also served as a Visiting Professor at the National Institute of Informatics, Tokyo . His research spans programming languages , reversible computing , and metaprogramming , with a focus on energy-efficient computation. Email: glueck@di.ku.dk Phone: +45 29611655 Address: Universitetsparken 5, Building B, 2100 Copenhagen Ø Glück's research interests center on reversible computing , program generation , and metaprogramming , particularly for low-energy systems. His work includes developing reversible logic circuits, invertible interpreters, and tools for program inversion via term rewriting systems. Recent publications highlight advancements in reversible flowchart languages , partial evaluation techniques , and compiler design . Key themes include garbage-free reversibility, memory-efficient algorithms, and formal verification of reversible systems. Scientific Awards: Japan Society for the Promotion of Science (JSPS) Fellowship Grants & Projects: Presto Basic Research Grant (JST) Danish Council for Strategic Research (DSF) project Danish Council for Independent Research (FNU) project Administrative Roles: Current member of the Study Board for Mathematics and Computer Science Former Head of Studies for the Master in Computer Science Professional Activities: IFIP Technical Committee WG 2.11 member (2004–) Steering Committee roles at LOPSTR 2024, FLOPS 2024, HCVS 2024, RC 2024 Editorial Board member for New Generation Computing (2005–)
Ludmila Ivanova Torlakova is an Associate Professor at the Department of Foreign Languages, University of Bergen, specializing in Arabic linguistics and phraseology. Her work explores idioms, figurative language, and metaphorical frameworks across political, literary, and educational contexts, with a focus on Modern Standard Arabic and its socio-cultural dimensions. Her research spans metaphor analysis in crisis communication (e.g., Oman's COVID-19 discourse), political rhetoric, and literary texts like Hanan al-Shaykh's novels. She investigates ʾiḍāfah constructions, body-part idioms, and pedagogical strategies for teaching multi-word expressions, contributing to lexicography and media studies. Torlakova’s publications highlight trends in Arabic phraseology, including the persistence of historical idioms in contemporary media and metaphorical framing in political and health-related discourses. Her academic lectures address topics such as curriculum design for Arabic programs and the intersection of language and cultural identity.
Dr Fredrik Dahlqvist serves as a Lecturer in Computer Science at Queen Mary University of London's School of Electronic Engineering and Computer Science, where he contributes to the Centre for Fundamental Computing and AI. His academic profile reflects a strong commitment to theoretical foundations in computing with direct applications to artificial intelligence research. His research spans theoretical computer science with focused expertise in probabilistic programming, probability theory, category theory, and mathematical logic. Dahlqvist investigates the mathematical frameworks governing probabilistic computation, developing rigorous methodologies for uncertainty quantification in computational systems. His work establishes formal connections between abstract category theory and practical probabilistic programming implementations, creating bridges between pure mathematics and applied AI. Recent publications (2023-2024) demonstrate cohesive thematic development across probabilistic programming semantics, neural network optimization, and formal verification. Key trends include error analysis in probabilistic floating-point systems, neural network pruning techniques based on geometric similarity, and categorical foundations for graded computation. His research consistently merges theoretical depth with practical AI challenges, particularly in verification of probabilistic models and optimization of learning architectures. Dr Dahlqvist has not received any documented scientific awards or fellowships according to available sources. He actively supervises two PhD students: Gregor Meehan researching "Representation Learning For Musical Audio Using Graph Neural Network-Based Recommender Engines" and Niki Omidvari conducting theoretical work in computer science foundations. Dahlqvist currently holds a £15,000 grant from the Academy of Medical Sciences for "Learning from Each Other: Neural Networks and Finite Automata" (March 2025-March 2026), investigating formal connections between neural architectures and automata theory. As an integral member of Queen Mary's Centre for Fundamental Computing and AI, Dahlqvist collaborates within a multidisciplinary research environment focused on advancing theoretical underpinnings of artificial intelligence. His work contributes to the Centre's mission of developing mathematically rigorous frameworks for next-generation AI systems through category-theoretic approaches and probabilistic reasoning.
Giovanni Sileno is an academic specializing in Artificial Intelligence, Logic Programming, and Knowledge Representation, with teaching appointments at the University of Amsterdam, EPITA Paris, and Université Pierre et Marie Curie. His primary affiliation is with the Informatics Institute at the University of Amsterdam where he serves as a Lecturer. His research interests span multiple domains within computer science, focusing particularly on formal methods for normative systems, agent-based programming, and logic-based knowledge representation. His work bridges theoretical computer science with practical applications in policy modeling, forensic science, and business information systems. Dr. Sileno has developed several notable software projects including AgentScriptCC for single-threaded intentional agents, DCPLschema for normative policy specifications, and libraries for normative primitives in Answer Set Programming. His GitHub activity shows consistent contributions from 2012 through 2025, indicating active engagement in both research and development. His publications reflect a strong focus on formal methods applied to practical problems, with particular emphasis on normative systems, agent architectures, and logic programming applications. The research trajectory shows evolution from foundational topics in logic and numeral systems toward increasingly sophisticated applications in policy modeling and agent-based systems. As an educator, he has taught across multiple institutions and disciplines, covering topics from basic programming paradigms to advanced knowledge representation and formal modeling techniques. His teaching spans undergraduate to master's level courses in information studies, cognitive science, data science, and forensic science.
Dirk K.J. Heylen serves as Full Professor at the Digital Society Institute within the University of Twente, holding a dual appointment in the Human Media Interaction department. His academic profile demonstrates extensive engagement in artificial intelligence research with particular focus on human-centered applications. Research interests span Artificial Intelligence, Autonomous Agent Systems, Natural Language Processing , and Knowledge Representation , with significant applications in healthcare, well-being, and social robotics. His work bridges technical AI development with societal context considerations, particularly examining human-AI interaction patterns in daily life scenarios. Recent publications reveal strong emphasis on conversational agents, virtual human interaction, and predictive health technologies. Notable scientific recognition includes the Best Student Paper award at the 22nd ACM International Conference on Multimodal Interaction (2018). His research activities demonstrate consistent output across conferences, journals, and datasets, with particular growth in healthcare applications since 2020. Professional activities include editorial work for major conferences (ICMI 2020), organization of specialized events (Narrative Matters 2018), and hosting academic visitors. His supervised work encompasses 22 research projects and multiple datasets supporting human-AI interaction studies.
Teague Henry is an Assistant Professor at the University of Virginia with joint appointments in the Department of Psychology (Quantitative Psychology program) and the School of Data Science . His work bridges network science, dynamical systems modeling, and Bayesian estimation to analyze complex data in neuroscience, clinical science, and natural language processing. Methodological expertise: Graph theory, exponential random graph models, functional neuroimaging Substantive applications: Autism, ADHD, semantic networks, social media topic analysis His research spans functional connectivity, network psychometrics, and language networks, with a focus on cross-disciplinary similarities in network data. Recent publications emphasize time-series modeling, imputation methods, and neurodevelopmental disorders. Software development: R packages like gimme and netjack Future student advising: Plans to recruit graduate students starting 2022-2023
Jason Braasch serves as an Associate Professor in the Department of Learning Sciences at Georgia State University's College of Education and Human Development. He maintains an active affiliation with the university's Adult Literacy Research Center, contributing to interdisciplinary initiatives focused on literacy development. His academic preparation includes: Ph.D. in Psychology, University of Illinois at Chicago (2009) M.A. in Psychology, University of Illinois at Chicago (2006) B.A. in Psychology, University of Wisconsin-Milwaukee (2001) Dr. Braasch's research program centers on critical thinking processes during internet-based reading, with particular emphasis on source evaluation mechanisms and individual differences in learning. His work investigates how readers assess content credibility, navigate conflicting information, and develop strategies for discerning trustworthy sources. This research has direct implications for educational interventions aimed at improving digital literacy across diverse learner populations. Analysis of his recent publications (2021-2025) reveals consistent focus on multiple source comprehension, particularly regarding health-related controversies (e.g., vaccination debates), belief-driven processing biases, and contextual factors affecting conflict detection. His scholarship demonstrates increasing integration of epistemic cognition frameworks with practical classroom applications, showing how intellectual humility and mindset theories influence information processing. Dr. Braasch has secured external research funding from prominent organizations including the Spencer Foundation and Facebook, supporting his investigations into internet literacy and critical reading development. As an active researcher within the Adult Literacy Research Center, he contributes to advancing evidence-based practices for literacy instruction while mentoring graduate students in learning sciences methodology and research design.
