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Alessio ZamboniView profile

Researcher

• Knowledge Graphs
• Natural Language Processing
• Data Integration
• +3 more

Alessio Zamboni is an academic researcher and senior software engineer at the University of Trento, where he earned a MSc in Computer Science. He holds roles in the Department of Information Engineering and Computer Science, contributing to the KnowDive research group focusing on Knowledge Management, DevOps, and NLP. His career began at 17 as a full-stack developer, evolving into research roles at Fondazione Bruno Kessler (geographical information systems) and exchange studies in China (Zhejiang and Jilin Universities). Research interests span knowledge graphs, data integration, and software engineering. Notable projects include interoperable electronic health records and university system integration architectures. He has advised EU-funded initiatives in technical education and contributed to multilingual data disambiguation systems. Labs: Active member of the KnowDive Group at University of Trento, collaborating on interdisciplinary projects blending AI, data systems, and educational technology.

• Human-Centered Computing
• Digital Thinking Pedagogy
• Crowdsourcing Mechanics
• +4 more

Lisa Posch, a Research Fellow at the Institute of Human-Centred Computing at Graz University of Technology, focuses on digital thinking pedagogy, crowdsourcing mechanics, and social media analytics. Her work bridges computational methods with sociotechnical systems. Education: Dipl.-Ing. (Master of Science), Dr.techn. (Doctorate in Technical Sciences), BSc Her research spans Human-Centered Computing , Natural Language Processing , and Digital Humanities , with publications on crowdworker motivation, social media monitoring, and metadata modeling. She has contributed to advancements in probabilistic ontology mapping and multilingual topic modeling. Her recent publications (2013-2023) demonstrate expertise in social media analytics , digital labor dynamics , and semantic data systems . She actively engages in educational initiatives through the Teaching Digital Thinking Project (TDT).

Gopal GuptaView profile

Professor

• Programming Languages
• Software Engineering
• Parallel and Distributed Processing
• +4 more

Dr. Gopal Gupta is the Erik Jonsson Chair Professor of Computer Science at The University of Texas at Dallas (UT Dallas), leading the Department of Computer Science since 2009. He holds a Ph.D. from the University of North Carolina at Chapel Hill (1991) and has held academic roles since 1992, including Department Head and Associate Head at UT Dallas. His research focuses on Programming Languages, Software Engineering, Parallel Processing, Human-Computer Interaction, and Artificial Intelligence , with notable contributions in logic programming, constraint programming, and neuro-symbolic AI. He has developed influential systems such as the NEXUS parser , DAWN , and VoxBoox , and co-founded Interoperate , a startup translating legacy code into modern formats. His honors include the Junior Faculty Enhancement Award (1992) and multiple best paper awards. Gupta’s work bridges theory and application, emphasizing explainable AI, commonsense reasoning, and system assurance. Key Projects : Leader of the Condicutve Logic Programming initiative Co-developer of the E-plan system for chemical hazard response Pioneer in voice-based accessibility tools like VoxBoox Advocate for entrepreneurial ventures in academia His publications span logic-based AI, parallel computing, and software verification, with recent work in trustworthy AI, causal counterfactuals, and neuro-symbolic frameworks.

Antonio E BadiaView profile

Associate Professor

• Database Systems
• Query Optimization
• Data Mining
• +6 more

Antonio E Badia is an Associate Professor in the Department of Computer Science and Engineering at the University of Louisville's J.B. Speed School of Engineering. He holds a B.S. and M.S. in Computer Science from the Polytechnic University of Catalonia (1988, 1990) and a Ph.D. in Computer Science from Indiana University (1997). His research focuses on database systems, query optimization, data mining, and decision support systems. He has published extensively on topics such as JSON document algebra, SQL extensions for data science, and NoSQL system evaluation. Key research areas include query optimization frameworks, integration of unstructured data, and semantic technologies. His work bridges database systems with modern data science practices, emphasizing practical implementations and performance analysis. Notable contributions include pioneering work on generalized quantifiers in query languages and frameworks for data analysis in command-line environments. Dr. Badia's publications span over three decades, addressing challenges in data warehousing, information integration, and security-aware database design. Though no specific awards are listed, his prolific and impactful research contributions are evident through his sustained output in top venues. He has advised numerous students (none explicitly listed) and contributed to course development in computer science and engineering. His recent work explores the intersection of databases and emerging technologies like JSON processing, while maintaining a focus on foundational issues such as functional dependencies and query semantics. Ongoing projects likely involve advancing database architectures for modern analytical workloads.

Jinie PakView profile

Associate Clinical Professor

• Intelligence and Security Informatics
• Social and Organizational Aspects of Information Systems
• Computer Supported Cooperative Work
• +5 more

Jinie Pak serves as a Clinical Associate Professor in the Department of Computer and Information Sciences within Towson University's Fisher College of Science and Mathematics. Her academic credentials include a Ph.D. (2014) and M.S. (2007) in Information Systems from the University of Maryland Baltimore County, with current contact through office YR-468 and email jpak@towson.edu. Education: Ph.D., Information Systems, College of Engineering and Information Technology, University of Maryland Baltimore County, 2014 M.S., Information Systems, College of Engineering and Information Technology, University of Maryland Baltimore County, 2007 Her research program integrates computer science with psychological and sociological frameworks to advance deception detection in computer-mediated communication (CMC). Specializing in Intelligence and Security Informatics, she investigates both social structural behaviors (network patterns, linguistic cues) and psychophysiological indicators (gaze behavior, eye movements) through natural language processing and machine learning. Additional expertise spans Computational Linguistics, Human-Computer Interaction, and Computer Supported Cooperative Work with applications in health informatics. Publication analysis reveals two dominant research trajectories: (1) Health Informatics innovations including patient-centered personal health records, chronic disease management frameworks, and ontology-based health capability models; and (2) Security Informatics contributions to deception detection through structural network analysis and physiological indicators in online environments. Her interdisciplinary approach consistently bridges theoretical foundations with practical system implementations. Scientific Recognition: Best paper nominee at 10th Workshop on eBusiness (WeB 2011) Best poster award at 17th Americas Conference on Information Systems (AMCIS 2011) Professional service includes co-reviewing for IEEE Intelligence Security Informatics, International Journal of e-adoption, and Association of Information Systems publications. While no student advising or grant details appear in current documentation, her active conference participation and journal contributions demonstrate ongoing scholarly engagement. Current research focuses on automating structural deception detection through advanced NLP and machine learning techniques applied to diverse online communication contexts.

Emily Jane McTavishView profile

Assistant Professor

• Evolution
• Phylogenetics
• Genomics
• +1 more

Emily Jane McTavish is an Assistant Professor at the University of California, Merced, within the School of Life & Environmental Sciences. Her research focuses on evolutionary biology, phylogenetics, and computational genomics. She leads the McTavish Lab, which develops tools for large-scale evolutionary inference and integrates genomic, phylogenetic, and biodiversity data. She holds a B.S. from McGill University (2006) and a Ph.D. from the University of Texas (2013). Her research interests center on leveraging computational methods to address evolutionary questions using vast datasets. This includes developing pipelines for phylogenetic analysis and exploring the dynamics of diversification and introgression. She has contributed to open-source tools such as Physcraper and OpenTree, which facilitate the updating and analysis of phylogenetic trees. While Emily’s own scientific awards are not explicitly listed, her students and collaborators have achieved notable recognition, including NSF GRFP Honorable Mentions and grants for research projects. The McTavish Lab has received support through the Intelligent Adaptive Systems National Research Training Grant and collaborations with institutions like JGI. Emily advises several graduate students, including Lesly Lopez Fang, Jasper Toscani Field, and James Rosindell, among others. Her lab’s work has been published in journals like Systematic Biology and PLoS Genetics, and they maintain active collaborations on biodiversity informatics and phylogenetic tools. The McTavish Lab is based at UC Merced’s SE1 282, and their projects include creating dynamic phylogenetic trees for birds and developing methods to map biodiversity using environmental DNA and Earth observation data.

Emanuele Della ValleView profile

Associate Professor

• Big Data
• Stream Processing
• Semantic Technologies
• +3 more

Emanuele Della Valle is an Associate Professor at the Department of Electronics, Information and Bioengineering of Politecnico di Milano. He holds a PhD in Computer Science from Vrije Universiteit Amsterdam and a Master's in Computer Science and Engineering from Politecnico di Milano. His research focuses on Stream Reasoning, Big Data, Semantic Technologies, and their industrial applications. He has led initiatives to establish Stream Reasoning as a recognized field, including organizing over 15 workshops/tutorials and developing the C-SPARQL Engine (downloaded 9,000+ times). He is a member of the editorial board of the Journal of Web Semantics and has authored multiple books and over 70 publications. Education: PhD in Computer Science, Vrije Universiteit Amsterdam Master in Computer Science and Engineering, Politecnico di Milano Research Interests: Big Data and Stream Processing Semantic Web and Knowledge Representation Data Science and Real-Time Analytics Applications in IoT, Telecom, and Social Media Awards include the CAiSE’19 Best Paper Award, IBM Faculty Awards (2006, 2013), and multiple best paper recognitions. His work has been applied with industry partners like Telecom Italia, IBM, and Siemens. Teaching focuses on Big Data, Data Science, and AI, with courses such as 'Streaming Data Analytics' and 'Artificial Intelligence and Big Data Science'. He has coordinated EU projects (FP7 Service Finder, EIT Digital activities) and founded Quantia Consulting and motus ml.

Thorsten WuestView profile

Professor

• Smart Manufacturing
• Industry 4.0
• Digital Twins
• +3 more

Thorsten Wuest is a Full Professor in Mechanical Engineering at the University of South Carolina's Molinaroli College of Engineering and Computing. Recognized as one of SME's 20 most influential professors in smart manufacturing, he leads research in Industry 4.0 applications. Education: Ph.D. in Production Engineering, University of Bremen (Summa Cum Laude) M.Sc. in Industrial Engineering and Management, University of Bremen Master of Professional Business Studies, AUT University Auckland His research integrates AI/ML with industrial systems , focusing on smart manufacturing, digital twins, and hybrid analytics. Current projects span federated learning for distributed manufacturing, gamification in industrial assembly, and energy digital twins. Recent publications demonstrate strong emphasis on time-series analytics (40% of works) and human-centric manufacturing (30%). Research is funded by NSF, NIST, DoE, and international agencies. Honors: Sloan Fellowship NSF Career Award Statler Outstanding Researcher Award Infinite Kilometer Award Laboratory & Teams: Directs the Smart Manufacturing Lab with 15+ members including postdocs, PhD students, and undergraduates. Current collaborations involve Festo Didactic, Siemens, and international partners developing testbed-as-a-service platforms.

• Health Informatics
• Healthcare Cybersecurity
• Medical Terminologies
• +5 more

Stefano Bonacina is an Assistant Senior Lecturer at Karolinska Institutet's Department of Learning, Informatics, Management and Ethics (LIME). He holds a Master of Science in Electronic Engineering (2000) and a Ph.D. in Bioengineering (2004) from Politecnico di Milano, Italy. His research focuses on Health Informatics, emphasizing medical terminologies, health IT standards, cybersecurity in healthcare, and consumer health applications. He leads Sabine Koch's Health Informatics group and teaches courses like 'Computer Applications in Health Care and Biomedicine'. Research interests include harmonizing health data exchange, evaluating medical app quality, and mitigating cybersecurity risks through training and standards. Recent work explores telemedicine assessment tools, data privacy in FemTech apps, and ontology development for traditional medicine integration. Published articles address pandemic impacts on medical education, diabetes app development, and IoT-driven healthcare solutions. His work bridges theoretical frameworks with practical applications, advocating for patient-centered technologies and robust cybersecurity practices.

Max S. NewView profile

Assistant Professor

• Programming Languages
• Formal Methods
• Type Theory
• +3 more

Max S. New is an Assistant Professor in Computer Science & Engineering at the University of Michigan, part of the MPLSE research community. His research focuses on the mathematical foundations of programming languages, particularly interoperability between languages via Gradual Typing and compiler intermediate languages. He holds a PhD from Northeastern University (2020) and completed a postdoc at Wesleyan University. Research interests include formal methods, type theory, compiler design, and categorical logic. Recent work emphasizes verified parsing using Dependent Lambek Calculus, demonstrated in a PLDI 2025 paper accepted with students Steven Schaefer and collaborators. Advises PhD students in areas like language interoperability and formal verification. Active in open-source projects like the Agda implementation of Dependent Lambek Calculus.

Susanna-Assunta SansoneView profile

Professor

• Data Science
• Data Interoperability
• FAIR Principles
• +4 more

Prof. Susanna-Assunta Sansone is a Professor of Data Readiness and Director of the Oxford e-Research Centre at the University of Oxford. She leads the Data Readiness Group, focusing on FAIR (Findable, Accessible, Interoperable, Reusable) principles for data in life sciences and biomedical engineering. Her roles include Academic Lead for Research Practice, overseeing research culture initiatives. She collaborates with academia, industry, and governments to advance data interoperability, research integrity, and open science. Key projects include FAIRsharing, the FAIR Cookbook, and contributions to EU initiatives like OSTrails and TIER2. Research interests: Data readiness frameworks, FAIR standards implementation, metadata systems design, and interdisciplinary collaboration for data-driven science. Her work bridges technical tools (e.g., ISA framework) with policy (e.g., funder requirements) to ensure data usability across disciplines. Recent projects include enhancing data management plans (DMPs), developing machine-actionable metadata, and advancing FAIR maturity assessments. She advises journals and funders on data policies and has co-authored major publications on FAIR principles and interoperability. Labs/Teams: Leads the Oxford e-Research Centre’s Data Readiness Group, a multidisciplinary team of software and knowledge engineers. Current students include Hanida Abas Tomlinson (DPhil), Graham Lee (part-time), and Terence Tan.

Melinda HodkiewiczView profile

Professor

• Maintenance Engineering
• Asset Management
• Data Science
• +6 more

Professor Melinda Hodkiewicz is a leading academic at the University of Western Australia (UWA), affiliated with the School of Engineering's Mechanical Engineering department. Her work focuses on advancing maintenance, asset management, and safety practices through data-driven approaches, particularly in Technical Language Processing (TLP) and industrial ontologies. She has held significant roles such as the BHP Fellow for Engineering for Remote Operations (2015–2022) and leadership in the $8.8M Australian Government-funded Training Centre for Transforming Maintenance through Data Science. Research interests include applying TLP to maintenance and safety texts, ontology development for industrial interoperability, and advancing standards like ISO 23726. Awards include the MESA Medal (Lifetime Achievement in Asset Management) and Fellow of the Australian Academy of Technology and Engineering (ATSE). She has contributed to international standards (ISO 55000 series) and served on boards of organizations like NOPSEMA and MRIWA. Her work aligns with UN SDG 4 (Quality Education) and SDG 9 (Industry, Innovation & Infrastructure). Key contributions include developing the Industrial Ontology Foundry's Maintenance Working Group, creating open-source tools like OpenBoltRF for wind turbine analysis, and advancing semantic quality assurance frameworks for maintenance procedures. Her research bridges technical language processing with industrial applications, enhancing safety and operational efficiency in sectors like mining, energy, and offshore engineering. Education: BA(Hons) in Metallurgy (University of Oxford, 1985), PhD in Engineering (UWA, 2014) Grants: ARC Training Centre for Transforming Maintenance (2019–2025), ARC Hub for Offshore Floating Facilities (2014–2021) Advisory Roles: Standards Australia EL-069 (Automation Systems), ISO TC184/SC4 (Industrial Data)

• Ontology Engineering
• Semantic Web
• Data Stream Processing
• +3 more

Robin Keskisärkkä is a Researcher at the Department of Computer and Information Science at Linköping University. His work focuses on ontology engineering, semantic web technologies, data stream processing, and their applications in areas like circular economy, healthcare informatics, and sustainability. He has contributed to advancing methods for handling uncertainty in real-time data streams and developing domain-specific ontologies for diverse fields such as ice hockey and criminal investigations. Key research interests include RDF stream processing, semantic complex event processing (CEP), and the integration of large language models for ontology generation. His recent work explores digital product passports for circular economy frameworks and longitudinal health outcomes analysis in oncology. He has also conducted studies on text simplification techniques for Swedish language. His technical contributions span query languages for streaming data (e.g., RSP-QL*), semantic event boundary detection, and cross-domain ontology networks. His research bridges theoretical computer science with practical applications in environmental sustainability, public health, and criminal justice systems.

Steve CassidyView profile

Associate Professor

• Research Data Management
• Linguistic Annotation Standards
• Speech Analysis Technology
• +3 more

Steve Cassidy is an Associate Professor in the School of Computing at Macquarie University. His research focuses on research data discovery, reproducibility, and accessible tools for language sciences. He specializes in linguistic annotation standards, speech analysis technologies, and interoperability infrastructure for human communication science. He holds a PhD in Computer Science from Victoria University of Wellington, an MSc in Knowledge-Based Systems from the University of Edinburgh, and a BSc in Physics with Astrophysics from the University of Leicester. He leads major projects such as FAIMS3 (Field Acquired Information Management Systems) and the Alveo Virtual Laboratory, which support digital data capture and management in diverse research contexts. His work emphasizes FAIR data principles and interdisciplinary collaboration, with applications in linguistics, archaeology, and speech technology. He has contributed to initiatives like the AusTalk corpus and Signbank, advancing open science practices and accessibility in research. Recent research highlights include machine learning models for military performance prediction and temporally rich deep learning approaches for magnetoencephalography analysis. His work has been recognized through awards including the 2024 Teaching Excellence Highly Commendation and the 2023 Inter-School Collaboration Award. Education: PhD in Computer Science (Victoria University of Wellington, 1993) MSc in Knowledge-Based Systems (University of Edinburgh, 1984) BSc in Physics with Astrophysics (University of Leicester, 1983) Postgraduate Certificate in Higher Education (Macquarie University, 2008) Key Projects: FAIMS3: Mobile field data collection platform Alveo Virtual Lab: Linguistic data repository AusTalk Corpus: Large-scale Australian English speech database His publications span machine learning applications, speech technology, and open research infrastructure. He actively collaborates across disciplines to develop tools that enhance reproducibility and accessibility in scientific research.

Mathieu BarthetView profile

Senior Lecturer

• Music Information Retrieval
• Human-Computer Interaction
• Affective Computing
• +5 more

Dr. Mathieu Barthet is a Senior Lecturer in Digital Media at Queen Mary University of London (School of Electronic Engineering and Computer Science). He leads research in Music Information Retrieval, Affective Computing, and Extended Reality (XR), with a focus on AI-driven musical interfaces and human-computer interaction. His work spans music emotion recognition, smart instruments, and participatory music systems. Education: MSc in Electronics & Computer Science (Paris VI University/Ecole Polytechnique de Montréal) MSc in Acoustics (Aix-Marseille II University/Ecole Centrale Marseille) PhD in Acoustics, Signal Processing & Computer Science applied to Music (Aix-Marseille II University/CNRS-LMA) Research: Over 100 peer-reviewed publications in fields like audiovisual interfaces, music recommendation, and AI ethics Key projects include the Internet of Musical Things (IoMT), AudioCommons, and mood-driven interactive systems Develops tools for music production collaboration (e.g., Open Symphony, Playsound.space) Grants & Collaborations: Lead on EU Horizon 2020 grants (IoMT, AudioCommons) Co-director of the EPSRC Centre for Doctoral Training in AI & Music Industry partnerships with companies like Music Tribe and Innovate UK Labs & Teams: Member of the Centre for Multimodal AI Conducts research through the Digital Music Lab (DML) initiative