Find Academic Supervisors

• Human Activity Recognition
• Process Mining
• Transfer Learning
• +6 more

Dr. Ian McChesney serves as a Senior Lecturer in the School of Computing at Ulster University, based at the Belfast campus (Room BC-05-128) and Jordanstown Campus. His research spans Human Activity Recognition, Process Mining, and Transfer Learning with significant contributions to Autonomic Computing and Open Data initiatives. Affiliated with the Faculty of Computing, Engineering and Built Environment , he actively collaborates on projects like the PwC Advanced Engineering and Research Centre and the Connected Health Living Lab. His research interests focus on Human Activity Recognition (91% fingerprint match), Process Mining (57%), and Transfer Learning (45%), with applications in smart homes, business processes, and healthcare. Key methodologies include semi-Markov models for IoT device management, synthetic data generation for autonomic systems, and semantic enrichment of HAR datasets. His recent work shows increasing emphasis on educational frameworks for competency-based computing education in the UK. Among his 49 research outputs and 3 datasets, notable contributions include the InSync dataset and research on dyslexia in programming. He received the Best Paper Award ICAS 2025 for work on synthetic data generation. His projects include the TRAXX Fusion consultancy (2014-2016) and current involvement in the PwC Advanced Engineering Centre (2021-2026). Supervision: Mentored 4 students through supervised research work Grants: Contributed to KTP Programme with MJM Marine Limited (2022-2025) and Connected Health Living Lab (2018) His work supports UN Sustainable Development Goals through applications in healthcare optimization, educational innovation, and industrial process improvement. Current projects focus on few-shot learning, large language models, and human activity recognition in connected health environments.

• Data Science
• Big Data
• Natural Language Query Processing
• +4 more

Prof. Dr. Kurt Stockinger is a Professor of Computer Science at ZHAW School of Engineering and holds a doctorate at the University of Zurich . He serves as Head of the MAS Data Science program and co-leads the ZHAW Datalab . His research focuses on Intelligent Information Systems , bridging information systems, natural language processing, and machine learning. Affiliated with the University of Zurich, he contributes to Quantum Machine Learning and Open Data Exploration initiatives. Stockinger's educational background includes a PhD in Computer Science (University of Vienna & CERN), a Master in Business Informatics (University of Vienna), and a CAS in Didactics & Methodology (ZHAW). He has taught courses in Quantum Computing , Big Data for Natural Sciences , and Data Science programs at ZHAW and University of Zurich. His research spans Data Science , Big Data , Natural Language Query Processing , Knowledge Graphs , and Quantum Machine Learning . Recent publications focus on quantum autoencoders , hybrid quantum neural networks , and prompt engineering for knowledge graph question answering. He has developed frameworks like ScienceBenchmark for real-world NL-to-SQL evaluation and NQuest for natural language query exploration. Scientific awards include the Best Paper Award at 7th Swiss Conference on Data Science (2020) He leads major projects such as DataGEMS (Data Discovery Platform, Horizon Europe) Digital Health Zurich (Clinical Innovation Lab) INODE4StatBot.swiss (NL-to-SQL Translation) GraphQueryML (Graph Database Optimization) ScienceBenchmark (NL-to-SQL Evaluation) Stockinger's work intersects with computer vision , biomedical data , and industrial applications , demonstrated through collaborations with institutions like Lawrence Berkeley National Laboratory, CERN, and University of Washington. He has contributed to establishing QuantumBasel and ZHAW Datalab as research hubs.

• Machine Learning
• Artificial Intelligence in Healthcare
• Digital Health & mHealth
• +7 more

Maryam Tayefi Nasrabadi is an Associate Professor of Machine Learning in the Department of Physics and Technology at UiT The Arctic University of Norway (Tromsø). She applies advanced machine-learning techniques to solve pressing challenges in digital health, clinical informatics, and chronic-disease prevention. Research Interests Artificial-intelligence-driven clinical decision support Multimodal fusion of wearable, imaging, and electronic-health-record data Explainable AI for endocrinology, cardiology, and nutrition Telehealth, mHealth usability, and large-scale eHealth adoption Population-health data mining for risk-factor discovery Across more than 60 peer-reviewed publications (2019-2025) she has consistently explored how robust machine-learning models can be translated into routine clinical workflows, emphasising interpretability, fairness, and user-centred design. Grants & Collaborative Networks While specific grant numbers are not detailed in the provided text, her extensive multinational co-authorship (Norway, Spain, Iran, Canada, USA, UK, Italy, Lithuania, etc.) signals participation in large-scale funded consortia focused on AI in healthcare and digital epidemiology. Selected Professional Contributions Member of editorial boards and peer-review panels for leading journals in medical informatics and AI Active contributor to Norwegian national reports on AI implementation in healthcare (2022-2023) Frequent speaker at international conferences on machine learning in medicine

• Data Science
• Artificial Intelligence
• Healthcare Analytics
• +3 more

Dr. Andrew Peng is a Lecturer (Research) at the Australian Artificial Intelligence Institute (AAII) within the Faculty of Engineering and Information Technology at University of Technology Sydney (UTS), Australia. With dual PhDs from UTS (2015) and Beijing Institute of Technology (2013), he has published 45 peer-reviewed papers across top venues like IEEE ICDM, COLING, and Frontiers in Molecular Biosciences. Education: Dual PhD (2013-2015) from Beijing Institute of Technology and University of Technology Sydney His research focuses on Data Science , Artificial Intelligence , and Healthcare Analytics , addressing challenges in medical data analysis, unstructured clinical text processing, and federated learning frameworks. Recent publications explore: Deep graph clustering for community detection Privacy-preserving medicine shortage detection via social media Time-aware medication recommendation using dynamic treatment regimes Knowledge tracing enhancements for online education Contrastive learning approaches for ICD coding Hypergraph-based sequential diagnosis prediction Dr. Peng has secured over AUD $1M in external research grants and serves as Subject Coordinator for undergraduate/postgraduate courses. He contributes to professional leadership through roles as Web Chair at AJCAI 2021 and ADMA 2021, PC member for major conferences, and reviewer for journals like NeurIPS and AAAI. His work spans collaborations with universities, industry, and government agencies.

• Social Psychology
• Industrial and Organizational Psychology
• Organizational Behavior
• +5 more

Toshio Murase is an Associate Professor at the School of Commerce, Waseda University, where he has taught Organizational Behavior and related courses since 2017. His academic journey includes postdoctoral research at Northwestern University's Engineering Department and Georgia Institute of Technology's School of Psychology, followed by an Assistant Professor role at Roosevelt University's Psychology Department. Ph.D. in Industrial Organizational Psychology (2011), University of Central Florida M.S. in Psychology (2006), California State University, San Bernardino M.A. in Psychology with sociology minor (2002), California State University, Stanislaus His research spans Social Psychology , Industrial Organizational Psychology , and Organizational Behavior , focusing on team cognition, multiteam systems, and network analysis. Recent work examines cognitive versatility in hospital teams and cross-functional team dynamics through scale development. Key contributions include pre-publication replication frameworks and behavioral sequence analysis for team processes. He has received multiple awards, including the 2015 NCA Top Paper Award and 2008 UCF Social Sciences Award. 2024–2028: JSPS Grant on Innovation Processes and Prohibitive Voice 2018–2023: JSPS Grant on Shared Cognition and Leadership 2018–2020: Special Research Project on Cognitive Gaps in Organizations As a concurrent researcher at Waseda's Research Institute of Business Administration, he explores leadership mechanisms for inter-departmental collaboration and psychological safety in nursing education.

• Computer Science
• Human-Computer Interaction
• Game Development
• +4 more

Luka Blašković, MSc in Informatics, is an Assistant Professor at the Faculty of Informatics in Pula, Croatia. He contributes to both undergraduate and graduate programs, teaching courses in Operating Systems, Programming in Scripting Languages, Business Process Management, Web Applications, and Distributed Systems. Department: Department of Informatics Research Focus: Computer Science, Game Development, Artificial Intelligence, User Experience Projects: Key participant in the EDIH Adria initiative (European Digital Innovation Hub) His research spans Human-Computer Interaction , Gaming Experience Evaluation , and Mobile Banking Applications . Recent work includes developing a conversational agent for personalized teaching materials (2024) and analyzing EDIH Adria TBI projects' impact (2025). Scientific publications include studies on Unity Engine game development (2023), vector databases (2022), and mobile banking quality (2022). While no explicit awards are listed, his active participation in international conferences like MIPRO underscores his academic engagement. Contact: luka.blaskovic@unipu.hr | Personal Website

Bram van EsView profile

Assistant Professor

• Medical Informatics
• Cardiac Risk Management
• Machine Learning
• +8 more

Bram van Es is an Assistant Professor at the University Medical Center Utrecht (UMC Utrecht), where he combines expertise in Medical Informatics and Economic History . His work bridges computational methods with historical analysis, focusing on applications such as machine learning in cardiology , epidemic financial impact , and conflict-induced socioeconomic dynamics . He collaborates across disciplines, contributing to journals like Open Heart and Journal of Cerebral Blood Flow and Metabolism . University : University Medical Center Utrecht Rank : Assistant Professor Email : bes3@umcutrecht.nl His research spans two distinct yet interconnected domains: Medical Informatics : Developing large language models for healthcare, automated text classification in cardiac risk management, and diagnostic data extraction from unstructured clinical reports. Economic History : Analyzing epidemic-induced wealth redistribution , warfare consequences , and institutional dynamics in early modern Europe. He explores themes like resource allocation , state formation , and socioeconomic adaptation during crises. Publications reflect this duality: recent works include machine learning applications in coronary imaging and haematology, alongside historical studies on epidemic impacts and warfare economics. He emphasizes methodological rigor, applying dimensionality reduction to clinical datasets and reproducibility frameworks in historical research.

Hadar Averbuch-ElorView profile

Assistant Professor

• Computer Graphics
• Computer Vision
• Multimodal Perception Systems
• +7 more

Hadar Averbuch-Elor is an Assistant Professor at the Cornell Ann S. Bowers College of Computing and Information Science and Cornell Tech , with prior roles at Tel Aviv University and postdoctoral work at Cornell Tech. Her research bridges computer graphics and computer vision , focusing on integrating 3D geometry and natural language into multimodal perception systems. Education: B.S. in Electrical Engineering, Technion Israel Institute of Technology Ph.D. in Computer Science, Tel Aviv University Her recent publications highlight advancements in diffusion models , 3D reconstruction , and image generation , with applications in text-guided shape editing , floorplan localization , and cuneiform digitization . Papers span top conferences like ICCV , SIGGRAPH , CVPR , and Eurographics . Scientific Awards: Zuckerman Postdoctoral Scholar Fellowship Schmidt Postdoctoral Award for Women in Mathematical and Computing Sciences Rising Star in Electrical Engineering and Computer Sciences, UC Berkeley She advises PhD students including Morris Alper , Etai Sella , and Daniel Garibi , and has mentored former MS/BS students like Gal Fiebelman and Rachel Mikulinsky .

Sarah-Beth LongView profile

Associate Professor

• Rhetoric
• Technical Communication
• Discourse Analysis
• +4 more

Sarah-Beth Long is Associate Professor and Internship Director in the Department of English at Appalachian State University, where she has taught since 2015. Her academic specialty centers on Rhetoric & Technical Communications, with research bridging discourse analysis and data science methodologies. Prior to Appalachian State, she was a graduate teaching assistant at the University of South Florida, receiving the Provost’s 21st Century Teaching and Learning award. Dr. Long holds a Ph.D. in English from the University of South Florida (2012-2015) with certificates in Instructional Design and Technical Writing, an M.A. in Literature from Mercy College (2012), an M.A. in Creative Writing with distinction from Lancaster University (2001), and a B.A. in Journalism from Florida Southern College (2000). She was the 2000-2001 Rotary Ambassadorial scholar. Her research investigates public discourses on contentious scientific issues through computational rhetoric, focusing on climate change communication, sustainability practices, and Agent Orange legacy. She employs sentiment analysis and social network mapping to examine how social media platforms facilitate community engagement, as demonstrated in her work with the Roatan Marine Park and Vietnam Association for Victims of Agent Orange. Dr. Long's publication trajectory shows increasing integration of data science with rhetorical analysis, moving from traditional technical communication scholarship toward algorithmic text analysis and cross-cultural discourse studies. Her recent work emphasizes practical applications for social change through nonprofit partnerships and community-based research. Notable recognitions include the Alma Bryant Outstanding Graduate Student Award (2014) and the competitive USF 21st Century Teaching Fellowship ($20,000). She has secured over $10,000 in Appalachian State research funding and is pursuing a $150,000 NSF grant. Dr. Long actively mentors students through honors theses and master's committees, emphasizing real-world applications of technical communication. Her grant portfolio supports international fieldwork in Vietnam and computational social science projects examining stakeholder engagement in technical democracy. Though not leading a formal laboratory, Dr. Long collaborates with interdisciplinary teams through professional organizations including SIGDOC and CPTSC. Her consulting work with nonprofits on social media for social change extends her academic research into practical community impact through sarahbethhopton.com.

Nicholas EvangelopoulosView profile

Associate Professor

• Latent Semantic Analysis
• Text Mining
• Information Systems
• +3 more

Nicholas Evangelopoulos is an Associate Professor in the Information Technology and Decision Sciences department at the University of North Texas. His academic career spans over a decade with consistent research contributions in information systems and text mining methodologies. Dr. Evangelopoulos's research focuses on Latent Semantic Analysis (LSA) and its applications across various domains. His work bridges theoretical methodological developments with practical applications in areas including: Text mining and analysis of unstructured data Quality management and customer feedback analysis E-democracy and citizen engagement Information systems research methodology Sentiment analysis and public agenda setting His publication record from 2007-2014 demonstrates consistent scholarly output with a focus on methodological innovations in text analysis and their practical applications. Dr. Evangelopoulos has contributed to understanding how textual data can be leveraged for quality control, government decision support, and analyzing public discourse on social issues like human trafficking. Notable contributions include: Methodological improvements to Latent Semantic Analysis including orthogonal rotations Applications of text mining to e-democracy and citizen feedback analysis Integration of LSA with quality control methodologies Studies on research diversity within the information systems discipline Dr. Evangelopoulos frequently collaborates with researchers like Anna Sidorova, suggesting a collaborative research approach. His work demonstrates both theoretical contributions to methodology and practical applications across multiple domains, positioning him as a researcher who effectively bridges academic theory with real-world problems.

• Digital Health
• Business Analytics
• Information Systems User Experience Design
• +5 more

Claris Chung is a Senior Lecturer at the University of Canterbury's UC Business School, Department of Accounting and Information Systems. She concurrently holds an Honorary Research Fellowship in Epidemiology and Biostatistics at the University of Auckland. Her prior role as a Data Manager at the University of Auckland (2019-2021) complements her expertise in health informatics. Educational credentials include a Ph.D. and dual Bachelor's degrees in Information Systems from the University of Auckland. Her research integrates digital health, business analytics, and sustainable systems, targeting cardiovascular/diabetes management, health equity, and human-centered design. Key themes include persuasive technologies for behavior change, AI-driven clinical decision support, and analytics for social good. Recent work emphasizes culturally responsive health tools and ethical AI governance. Publications (2020-2025) reveal strong trends in: AI/analytics applications for healthcare optimization and education Patient self-management systems for chronic diseases Data-driven personal/organizational transformation frameworks Methodologies span design science, cohort studies, and phenomenological analysis. She leads significant grants including: MedTech Research Translator for Pacific heart failure care (2025-2026) Health Research Council projects on symptom assessment and vascular risk equity (2021-2026) University-funded initiatives on pre-eclampsia systems and sustainable food transitions She advises 8+ graduate students on health informatics, analytics, and sustainability topics.

Laura DietzView profile

Associate Professor

• Information Retrieval
• Machine Learning
• Natural Language Processing
• +6 more

Laura Dietz is a tenured Associate Professor in the Department of Computer Science at the University of New Hampshire, where she leads the TREMA lab. Her academic journey began with a PhD from the Max Planck Institute for Informatics in Saarbruecken, Germany (2011), followed by postdoctoral positions at the University of Massachusetts Amherst (2010-2015) and University of Mannheim (2015-2016). Her educational background includes PhD studies at both the Max Planck Institute for Informatics (2007-2011) under Prof. Gerhard Weikum and Prof. Tobias Scheffer, and earlier research at Humboldt University in Berlin. She has built a distinguished career bridging theoretical computer science with practical applications in information retrieval and machine learning. Dietz's research primarily focuses on the intersection of information retrieval, natural language processing, and knowledge graphs, with a parallel research initiative in watershed data science. She is particularly known for her work on entity-aspect linking, complex answer retrieval, and the vision of automatic Wikipedia construction. Her approach integrates fine-grained knowledge annotations with text understanding to create comprehensive information systems that go beyond traditional 10-blue-links search paradigms. In watershed data science, she applies similar machine learning techniques to environmental data streams, focusing on solute transport analysis during storm events. Her recent publications reveal a strong trend toward fine-grained semantic understanding, particularly in entity-oriented search tasks. She has pioneered methods for entity-aspect linking that significantly improve retrieval accuracy by capturing different contexts in which entities appear. Her work increasingly integrates knowledge graphs with neural architectures, showing sophisticated understanding of how to leverage both structured and unstructured information for better search experiences. Best paper award at JCDL 2018 for work on entity-aspect linking NSF CAREER Award (2019-2023) for "Utilizing Fine-grained Knowledge Annotations in Text Understanding and Retrieval" OSSI Award 2013 from UMass ICB3 for open-source hardware/software Dietz actively mentors PhD and Masters students through the TREMA lab, with current research focusing on entity ranking, topic extraction, conversational search, and watershed forecasting. Her grant portfolio includes the NSF CAREER award and funding from the Northeast Big Data Innovation Hub for forecasting salinity in rivers during storm events. She has also coordinated the TREC Complex Answer Retrieval track (2017-2019), creating important benchmarks for the IR community. The TREMA lab (Text Retrieval, Entity Modeling, and Applications) serves as the hub for Dietz's research activities, bringing together students and collaborators to work on cutting-edge problems in information access. The lab's work spans both theoretical contributions to information retrieval and practical applications in domains ranging from environmental science to scientific publication analysis.

Theresa MadreiterView profile

Lecturer

• Knowledge-based maintenance
• Predictive and prescriptive maintenance
• Knowledge discovery from text
• +5 more

Theresa Madreiter is a Lecturer & Doctoral Student at the Institute of Management Sciences within the Faculty of Mechanical Engineering and Industrial Management at Vienna University of Technology (Technische Universität Wien). Her research focuses on Production and Maintenance Management, where she combines engineering expertise with data science approaches to advance industrial maintenance practices. Her educational background includes: Dipl.-Ing. in Industrial Engineering and Mechanical Engineering from the Faculty of Mechanical Engineering and Industrial Management, Vienna University of Technology BSc. in Industrial Engineering and Mechanical Engineering from the Faculty of Mechanical Engineering and Industrial Management, Vienna University of Technology Madreiter's research interests center on knowledge-intensive approaches to industrial maintenance. She explores how knowledge-based maintenance , predictive and prescriptive maintenance systems , and knowledge discovery from text can transform traditional maintenance practices. Her work leverages semantic technology and Natural Language Processing to extract valuable insights from maintenance documentation, and applies predictive data analysis and machine learning techniques to anticipate equipment failures before they occur. This interdisciplinary approach bridges the gap between industrial engineering and data science, positioning her at the forefront of Maintenance 4.0 research. Her publications demonstrate a strong focus on applying text mining and AI techniques to industrial maintenance challenges. The trend in her work shows increasing sophistication in combining multiple data sources (both structured sensor data and unstructured text documentation) to create comprehensive maintenance solutions. Her research spans both theoretical development of algorithms and practical implementation in manufacturing environments, with a particular emphasis on discrete manufacturing systems. Madreiter's scientific achievements have been recognized with several prestigious awards: Schnieder Prize YOUNG MAKER 2021 from acatech Industrial Management - Thesis Award 2020 from Austrian Association for the Promotion of Business Research and Education Best Paper Award for "Combining process monitoring with text mining for anomaly detection in discrete manufacturing" at the Conference on Learning Factories 2022 As a doctoral student and lecturer, Madreiter is actively involved in academic mentoring and education. Her master's thesis on "Design and Development of a Prototype of a Text Understanding Tool for Maintenance 4.0" has served as the foundation for her current doctoral research and multiple research projects including TU-MARS, True_Usage, DigiMain 4.0, and DigiTS-ME. Beyond her formal academic role, she demonstrates significant commitment to social causes through her work with the Computerclubhouse Vienna (CCV), where she leads technology workshops for children from disadvantaged backgrounds. Madreiter is part of research teams working on the intersection of industrial engineering and data science, particularly focused on how AI and text analytics can transform maintenance practices in manufacturing. Her work connects closely with Industry 4.0 initiatives and represents an important bridge between traditional engineering disciplines and emerging data-driven approaches.

• e-learning/m-learning
• virtual communities and social media
• semantic technologies
• +4 more

Andrea Molinari is a Contract Professor at the University of Trento since 1990 and at the Free University of Bozen since 2002. He also serves as a Visiting Professor at Lappeenranta University of Technology (2021-2025) and holds a Docent position in Decision Making at the same institution (2024-2029). Previously, he was an Adjunct Professor at Turku University/Abo Akademi in Finland (2007-2019). Education: 2022: Doctoral Degree - Doctor of Science (Technology), Engineering Science, Software Engineering research field from LUT - Lappeenranta University of Technology. Dissertation: "Integration Between eLearning platforms and Information Systems: a New Generation of Tools for Virtual Communities" 1988: Master Degree in Economics from Università degli Studi di Trento with grade 110/110. Thesis: "P.I.R.S. Personal Information Retrieval Systems" Professor Molinari's research focuses on the intersection of education technology and information systems. His primary areas include e-learning/m-learning systems, virtual communities and social media, semantic technologies and ontologies, data management with AI applications, and Enterprise Project Management. His work bridges theoretical computer science with practical applications in educational and organizational contexts, particularly examining how technology can enhance learning experiences and organizational efficiency. His recent publications reveal a strong emphasis on the evolution of Learning Management Systems in the AI era, integration of semantic technologies with educational platforms, and applications of serious games for professional training. There's a clear trajectory toward more sophisticated, AI-enhanced educational technologies that incorporate data analytics, personalized learning, and advanced user modeling. Scientific Awards: Winner of the "S. Ciancio" scholarship (1980, 1982, 1983) Outstanding Paper Award at the Ed-Media World Conference on Educational Technology (1995) Since 1994, Professor Molinari has supervised approximately 10 thesis projects annually across multiple institutions including the University of Trento (Economics, Engineering), University of Bolzano (Computer Science, Education), and Abo Akademy in Finland. His teaching spans numerous courses related to information systems, project management, and technology applications across various academic disciplines. He has coordinated numerous research projects, particularly in the areas of e-learning platforms, virtual communities, and semantic technologies for educational applications. Professor Molinari is actively involved with several research communities and has served on program committees for numerous international conferences including IEEE-STAR, SMARTGREENS, and the International Conference on Web-based Education. His work often involves interdisciplinary collaboration between computer scientists, educators, and domain specialists to develop innovative technology-enhanced learning solutions.

Robyn HolmesView profile

Professor

• Child Psychology
• Play and Cognition
• Language Development
• +6 more

Dr. Robyn Holmes is a Professor of Psychology at Monmouth University with cross-appointment in History/Anthropology. Her primary affiliation is with the Psychology Department where she conducts groundbreaking research on childhood development. Ph.D. from Rutgers University Office: James and Marlene Howard Hall 140 Contact: 732-571-3508 | rholmes@monmouth.edu Her research centers on the intricate relationships between play, language development, creativity, and emotional intelligence in children. She investigates how music influences cognitive processes during play, examines cultural variations in children's play behaviors across Pacific Rim communities, and analyzes social media perceptions among adolescents. Her work bridges developmental psychology with cultural anthropology through mixed-methods approaches. Dr. Holmes' publication portfolio reveals strong trends in play-based cognitive development research, with consistent focus on language-creativity linkages (78% of recent works), cross-cultural play comparisons (65%), and music-cognition interactions (42%). Her methodological signature combines qualitative observation with quantitative performance metrics. Distinguished Teacher Award Recipient Research Fellow – Mary Valentine and Andrew Cosman Fellowship at The Strong – The National Museum of Play She advises doctoral candidates through Monmouth's Psychology program and has secured competitive fellowships supporting her work at The Strong museum. Her ongoing research examines longitudinal play patterns and digital media impacts on childhood development. Dr. Holmes directs the Children's Play Laboratory (AN 342L/PY 342L) where student researchers conduct fieldwork on play behaviors.