Find Academic Supervisors

• Semantic Web
• RDF
• SPARQL
• +3 more

Louise Parkin serves as a Lecturer in the Department of Networks and Telecommunications at the University Institute of Technology of Blois (IUT Blois), University of Tours, with additional affiliations at the Polytechnic School of the University of Tours (EPU) and the Fundamental and Applied Computer Science Laboratory of Tours (LIFAT). Her academic profile centers on advancing cooperative methods for knowledge base interrogation and semantic data management. Her research specializes in Semantic Web technologies, focusing on resolving critical challenges in RDF and SPARQL query processing including plethoric answer sets, unexpected results, and scalability limitations. She develops cooperative frameworks that enhance user interaction with knowledge bases like Wikidata through query explanation mechanisms, adaptive result filtering, and on-demand analysis architectures. Her work bridges theoretical database concepts with practical knowledge engineering applications. Within LIFAT laboratory, Parkin contributes to cutting-edge research in semantic data management systems. Her recent projects include Rankingdom's cooperative architecture for Wikidata analysis and scalable techniques for knowledge base exploitation, demonstrating sustained innovation in query processing methodologies from 2020 through 2025 publications.

• Machine Learning
• Big Data
• Knowledge Discovery
• +7 more

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.

Craig EvansView profile

Lecturer

• Database Technologies
• Social Network Analysis
• Data Science
• +5 more

Craig Evans serves as a Lecturer at the School of Information Sciences, University of Illinois Urbana-Champaign, teaching undergraduate database courses and professional development. He holds an MS in Library and Information Science from the same institution, with office located at Room 303, 501 E. Daniel St., and contactable via csevans2@illinois.edu. Education: MS, Library and Information Science, University of Illinois Urbana-Champaign His research integrates database technologies, social network analysis, and unmanned aviation systems, drawing from 25+ years of cross-industry experience in IT consulting, manufacturing, and drone operations. He emphasizes how aggregated data insights transcend individual data points, with specific interests in data curation, information retrieval, and network science. Publications from 2002-2016 reveal consistent focus on social network dynamics, including corporate communication patterns, sentiment analysis applications, and entity resolution challenges. Earlier work explores adaptive web interfaces and wiki-based collaboration, demonstrating enduring engagement with information systems design and human-computer interaction evolution. Evans teaches multiple sections of Intro Database Concepts & Apps (IS206), Consulting for Info Pros (IS380A), and Graph Databases (IS390GDB), connecting academic theory to real-world industry contexts through his extensive professional background. Professionally, he is an FAA Certificated Part 107 Pilot who co-founded a university-spinoff drone operations business, actively applying academic research to entrepreneurial unmanned aviation systems development.

• Time Series Analysis
• Deep Learning
• Data-Intensive Systems
• +6 more

Tomasz Wiktorski is a Professor at the Faculty of Science and Technology , University of Stavanger , where he serves as Study Program Manager for MSc and PhD programs in Computer Science and Data Science. His research integrates conventional time series analysis with deep learning for applications in biomedical data (e.g., wearable devices), oil and gas drilling automation, energy systems prediction, and cloud infrastructure optimization. Education : Not explicitly detailed in the text. Research interests span data-intensive system modeling, focusing on: Biomedical time series analysis (wearables, ECG signal correction) Drilling process optimization via transfer learning and temporal models Energy systems prediction using machine learning Cloud infrastructure monitoring Curriculum development in data science education Scientific trends reveal expertise in recurrent neural networks, support vector machines, and hybrid data modeling for sensor networks across domains like health, petroleum, and cloud computing. Leadership includes designing data science programs and contributing to the EDISON Data Science Framework for global standards.

• Human-Computer Interaction
• Usability Research
• Eye Tracking
• +2 more

Kasper Hornbæk is a Professor at the Department of Computer Science, University of Copenhagen, specializing in Human-Centred Computing. He conducts research in human-computer interaction (HCI), usability, eye tracking, visualization, and software engineering. Primary Fields: Human-Computer Interaction, Usability Research, Eye Tracking, Visualization, Software Engineering Recent Collaborations: International (country/territory-level) partnerships in HCI and AI Research Outputs: 228 publications focusing on multimodal interaction, VR, and causal modeling His work explores audio-tactile integration, theoretical frameworks in HCI, and principles for user interface design through empirical studies and meta-analyses. Recent projects include heartbeat resonance interfaces and critiques of experimental methodology. Contact: kash@di.ku.dk | Research Website

• Information Retrieval
• Machine Learning
• Evaluation
• +4 more

Maria Maistro is a Tenure Track Assistant Professor at the Department of Computer Science, Faculty of Science, University of Copenhagen. Her research focuses on Information Retrieval (IR) with emphasis on evaluation, reproducibility, click log analysis, learning to rank, expert search, and machine learning applications to IR. She contributes to the AMAOS project aiming to advance expert search methodologies. PhD in Computer Science (IR) from University of Padua (2017) MSc in Mathematics (probability, stochastic methods) from University of Padua (2014) Maria's research addresses critical challenges in IR evaluation through stochastic modeling and user signal analysis. She investigates fairness-relevance tradeoffs in recommender systems, cross-cultural retrieval frameworks, and explainability techniques for language models and recommendation algorithms. Her work combines theoretical foundations with practical applications in healthcare records, insurance domains, and culinary contexts. Recent publications (2024–2025) demonstrate her expertise in: Retrieval-Augmented Generation (RAG) for cross-cultural adaptation Fairness evaluation in recommender systems EEG-based semantic relevance analysis Session-based recommendation explainability Language model pruning consistency Reproducibility frameworks for IR experiments Maria actively participates in academic activities through conference organization roles, including: Organizer, CENTRE@CLEF 2019 Organizer, CENTRE@NTCIR 2019 Organizer, CENTRE@CLEF 2018 Organizer, LEARNER 2017

Jim WhiteheadView profile

Professor

• Artificial Intelligence
• Software Engineering
• Computer Games
• +6 more

Jim Whitehead is a Professor of Computational Media in the Baskin School of Engineering at the University of California, Santa Cruz. His research bridges artificial intelligence, software engineering, and computer games, with notable contributions to procedural content generation, software bug prediction, and serious games. He serves as Associate Dean for Undergraduate Affairs, focusing on student success initiatives including equity gap reduction, retention strategies, and experiential learning programs like first-year engineering design and senior capstone sequences. Academic Leadership: Former Chair of Computer Science (2000-2014) and Computational Media (2017-2019) Community Building: Founder of Society for the Advancement of Science of Digital Games (SASDG), procedural content generation communities, and games/software engineering collaborations Research interests span AI for games , software evolution , and human-AI collaboration in creative domains. Articles highlight trends in procedural generation (2020), music sentiment modeling (2019), and code change analysis (2008-2015). Administrative expertise includes UC faculty search committees, personnel case management, and curriculum development. He pioneered UCSC's computer games major (ranked top in the US) and masters program in games/playable media. GitHub activity shows active involvement in open-source projects like smt_dungeon (SMT solver-based dungeon generator) and MovementLab (interactive media tools). His website template is based on the al-folio Jekyll theme.

• Scientific Visualization
• Computer Graphics
• Flow Estimation and Interpolation
• +7 more

Dr. Steffen Frey is an Assistant Professor in the Scientific Visualization and Computer Graphics group within the Bernoulli Institute at the University of Groningen's Faculty of Science and Engineering. He also maintains an affiliation with the Faculty of Medical Sciences/UMCG in the Robotics and image-guided minimally-invasive surgery (ROBOTICS) research group. His work bridges computer science with practical applications in medical and geoscientific domains. His research interests focus on scientific visualization, computer graphics, and interactive visualization techniques. Dr. Frey specializes in developing novel methods for flow estimation, temporal interpolation, parameter sensitivity analysis, and visualization of complex scientific data, particularly in porous media and fluid dynamics. His work often involves creating scalable solutions for big data visualization problems and applying visualization techniques to medical applications such as bone cement simulation. Analysis of his recent publications reveals a strong focus on machine learning approaches to scientific visualization, particularly in temporal interpolation and ensemble data analysis. His work combines traditional computer graphics techniques with modern deep learning methods to solve challenging visualization problems in scientific domains. The research demonstrates increasing sophistication in handling complex multi-dimensional datasets while maintaining interactive performance. 2019 IEEE Scientific Visualization Contest Winner Dr. Frey actively collaborates with researchers internationally, as evidenced by his co-authorship with scientists from various institutions worldwide. His work contributes to multiple Sustainable Development Goals, particularly those related to clean water and sanitation through his porous media flow research. He has supervised multiple students through research projects and thesis work, though specific names are not listed in the provided materials.

• Human-Computer Interaction
• Accessibility
• Health Communication
• +5 more

Hidy Kong is an Assistant Professor at the School of Information , Golisano College of Computing and Information Sciences, Rochester Institute of Technology. Her work bridges human-computer interaction, health communication, and accessibility research with a focus on neurodivergent populations. Teaching: HCIN-620 (Information and Interaction Design), HCIN-630 (Usability Testing) Research: Conversational agents, assistive technology, data visualization, and inclusive design Kong's research explores chatbot-based social support systems , robot dog co-design for neurodivergent users , and infographic effectiveness in public health . Her publications demonstrate a focus on creating technology that balances user control with AI assistance, particularly for vulnerable populations. Recent work examines conversational agent strategies (2022-2025), data visualization communication (2019-2022), and chatbot training applications (2025). She also investigates accessibility in online resource discovery (2024) and behavioral visualization tools for medical crowdfunding (2016-2020).

Christine LargouetView profile

Associate Professor

• Artificial Intelligence
• Explainable AI
• Complex Systems Modelling
• +6 more

Christine Largouet is an Associate Professor in Computer Science at Institut Agro Rennes-Angers and a member of the IRISA Laboratory's LACODAM team. Her research focuses on Explainable AI, Complex Systems Modelling, and applications in Ecosystem Management and Agroecology. She leads the Computer Science Teaching Unit at Institut Agro Rennes-Angers since 2006 and has held academic roles at the University of New Caledonia (2003-2006) and IRD Nouméa. She holds a HDR (2019) and PhD (2000) from Université de Rennes 1. Education: Habilitation Defense (HDR): "Artificial Intelligence for Decision Support of Dynamical Complex Systems" (Université de Rennes 1, 2019) PhD: "Interpretation of a sequence of remote-sensing images" (Université de Rennes 1, 2000) Research Interests: Her work bridges AI and environmental/agricultural systems, with emphasis on: Explainable AI for decision-making Machine learning in precision agriculture Timed behavioral models & discrete event systems Data-driven approaches for ecosystem management Publications Trends: Recent work emphasizes: AI explainability in user-centric applications Livestock management using sensor-based machine learning Formal methods for system modeling (timed automata) Interdisciplinary applications in agroecology Teaching & Supervision: Supervised 17+ PhD/Master students since 2002 Teaches programming, AI, and data science at multiple academic levels Labs & Teams: Active contributor to: IRISA Laboratory's LACODAM team Collaborations with INRAE and INRIA

Monika AkbarView profile

Associate Professor

• Artificial Intelligence (AI)
• Machine Learning (ML)
• Cybersecurity
• +5 more

Monika Akbar is an Associate Professor in the Department of Computer Science at the University of Texas at El Paso (UTEP), where she has served since 2017. Previously, she was a Research Assistant Professor and Assistant Director of the CyberShare Center of Excellence at UTEP. She holds a Master's from Montana State University and a Ph.D. from Virginia Tech. Education: Ph.D. in Computer Science, Virginia Tech M.S. in Computer Science, Montana State University Her research focuses on AI/ML-driven solutions for cybersecurity, public health analytics, and educational technology. Key areas include threat modeling in industrial control systems (ICS), malware detection, disease outbreak prediction using mobility data, and culturally responsive computational thinking education. Her recent work emphasizes integrating heterogeneous data sources for risk assessment, with applications in cybersecurity (e.g., CyManII collaborations) and public health informatics. Over 50 publications span conferences like IEEE Big Data, CIKM, and journals like IEEE Security & Privacy. Notable collaborations include the Sol y Agua project, a game-based STEM education initiative, and partnerships with the National Science Foundation (NSF) for CS education studies in El Paso schools. Her lab develops tools like AWEB for cybersecurity threat analysis and mobile platforms (e.g., Dysgu) to enhance out-of-class learning engagement.

• Condensed Matter Physics
• Computational Materials Science
• Artificial Intelligence in Materials Discovery
• +11 more

Aldo H. Romero is the Eberly Family Distinguished Professor of Physics and Astronomy at West Virginia University, where he serves as Director of Research Computing . His research integrates computational materials science, artificial intelligence, and high-performance computing to advance understanding of condensed matter systems, including strongly correlated materials, 2D materials, and magnetic compounds. Education : Ph.D. in Physics and Chemistry from University of California, San Diego (1998) Awards : Eberly Family Distinguished Professorship Romero’s group specializes in Density Functional Theory (DFT) , Dynamical Mean Field Theory (DMFT) , and machine learning for materials discovery. They develop open-source tools like PyProcar , MechElastic , and ABINIT , focusing on properties such as electronic structure, elastic behavior, and magnetic interactions. Recent work explores chiral materials , AI-driven optimization , and high-throughput computational methods for material design. Key trends in publications include studies on chiral symmetry in kagome lattices, machine learning for material properties, symmetry-based structure prediction, and DFT+DMFT analysis of correlated systems. Collaborations span Germany, France, China, Spain, and Italy, with applications in energy storage, quantum technologies, and spintronics. Scientific contributions encompass: Development of Fireball and ABINIT codes Machine learning for Hubbard U parameter optimization Chiral material discovery via symmetry arguments Pioneering work on 2D Rashba systems Labs and teams include: AI WVU Discussion Group (founded by Romero) West Virginia University Computational Materials Group Collaborative efforts with international institutions Grants and proposals highlight initiatives like the NSF & NVIDIA Open Multimodal AI Infrastructure (OMAI) program and Spencer Foundation support for AI in education. The group works with over 20 graduate students and 4 postdoctoral researchers, emphasizing interdisciplinary training and global collaboration.

• Natural Language Processing
• Information Extraction
• Social Media Analysis
• +4 more

Dr. Mark Greenwood is a Research Associate at the School of Computer Science, University of Sheffield. His work focuses on Natural Language Processing (NLP), Information Extraction, and Semantic Technologies. He has contributed to projects like the GATE framework, developing tools for text processing and social media analysis. His research addresses real-time semantic annotation, online abuse detection in political discourse, and healthcare data mining. Notable collaborations include work with the National Archives and medical institutions to improve text analysis in clinical records. Greenwood has authored over 30 peer-reviewed publications, with recent focuses on social media analytics and genomic data integration. His expertise spans dependency parsing, temporal expression extraction, and cross-media knowledge systems. Education details are not explicitly stated, but his academic trajectory includes significant contributions to computational linguistics and NLP since the early 2000s. Research interests are anchored in practical NLP applications such as question-answering systems, genic interaction extraction, and media influence quantification. He has led teams on EU-funded projects and advised on large-scale semantic search initiatives like Khresmoi. Current work emphasizes partisanship analysis in digital spaces and automated text pattern recognition.

• Work & Organisational Psychology
• Human-Computer Interaction
• Eye Tracking
• +6 more

Patrik Pluchino is an Assistant Professor at the Department of Psychology, University of Padua, affiliated with the Human Inspired Technology Research Centre. His research focuses on cognitive processes in human-computer interaction, psychophysiological measures of attention, and IoT applications in workplace and living environments. He investigates attention allocation, multimedia learning, and ethical decision-making through eye-tracking and neurophysiological methods. Research interests include cognitive workload assessment using EEG/ERP, IoT systems for safety and sustainability, and VR-based training for disaster response. He leads projects like SAFE GYMS and DOMHO, developing smart solutions for elderly care and workplace safety. His work bridges psychology, technology, and design to enhance human-centered systems in Industry 5.0 contexts. Pluchino collaborates on EU-funded initiatives addressing aging workforce needs and accessible technologies for disabled users. His publications span 20+ years, emphasizing interdisciplinary approaches to human factors, educational technology, and assistive systems. Key contributions include studies on attentional blink, implicit learning mechanisms, and ethical behavior through attention dynamics.

Laura HaasView profile

Professor

• Data Management
• Database Systems
• Information Integration
• +4 more

Laura Haas serves as the Donna M and Robert J Manning Dean at the University of Massachusetts Amherst's College of Information and Computer Sciences. Prior to this, she spent 36 years at IBM, where she rose to the level of IBM Fellow and held leadership roles such as Director of the Accelerated Discovery Lab and Director of Computer Science at IBM's Almaden Research Center. Her research focuses on data integration, database systems, and accelerating discovery through data-driven approaches. She pioneered systems like Starburst, Garlic, and Clio, and founded IBM's Accelerated Discovery Lab to enhance data analysis tools for domain experts. Haas holds a PhD from the University of Texas at Austin (1981) and an AB from Harvard University (1978). She has held sabbaticals at the University of Wisconsin-Madison and ETH Zurich. Her contributions earned her awards like the ACM SIGMOD Codd Innovation Award and Anita Borg Institute Technical Leadership Award. She is a Fellow of the ACM and member of the National Academy of Engineering. Her research spans federated data systems, schema mapping, and big data challenges. She advocates for data science education reforms and chairs key initiatives like the National Academies Computer Science and Telecommunications Board. The DREAM Lab under her leadership explores data systems for exploration and analytics. Education: PhD, University of Texas at Austin, 1981 AB, Harvard University, 1978 Awards: ACM SIGMOD Codd Innovation Award Anita Borg Institute Technical Leadership Award IBM Fellow Member, National Academy of Engineering Labs/Teams: DREAM Lab (Data systems Research for Exploration, Analytics, and Modeling) Her work bridges theoretical foundations and practical systems, emphasizing tools that empower domain experts over technical complexities. Current initiatives focus on democratizing data science through education and tooling advancements.