Find Academic Supervisors

• Information Systems
• Big Data Analytics
• Online Customer Reviews
• +7 more

Wenjie Fan is a Visitor (Faculty) at the Department of Information and Service Management, School of Business, Aalto University, Finland. He holds a Doctor of Science in Economics and Business Administration (majoring in Information Systems Science) from Aalto University, awarded on June 27, 2022. His academic journey includes two master's degrees in Business and Economics from the University of Arizona and City University of Hong Kong, and a bachelor's degree in Engineering and Technology from Beijing University of Posts and Telecommunications. Doctor of Science in Economics and Business Administration (Information Systems Science), Aalto University, 2022 Master's degree in Business and Economics, University of Arizona, 2013 Master's degree in Business and Economics, City University of Hong Kong, 2012 Bachelor's degree in Engineering and Technology, Beijing University of Posts and Telecommunications, 2008 His research focuses on leveraging big data to understand consumer behavior, managerial responses, and digital platform dynamics. Key interests include online customer reviews, crowdfunding, tourism industry analytics, and service management. His work integrates theories from information systems, behavioral economics, and data science to explore how digital interactions shape business outcomes. The most recent articles reflect a consistent trend toward empirical analysis of large-scale datasets from online platforms. These studies employ advanced statistical methods to investigate review helpfulness, environmental impacts on tourism, investor behavior in crowdfunding, and engagement patterns on social media. The research bridges academic rigor with practical implications for businesses and policymakers. No scientific awards were explicitly mentioned in the provided text. There is no public information available about Wenjie Fan’s advisees or research grants. However, his doctoral thesis and publications suggest active involvement in interdisciplinary research projects, likely supported by institutional or collaborative funding. His work has been disseminated through high-impact journals and conferences in information systems and management. Wenjie Fan’s research is conducted within the Department of Information and Service Management at Aalto University’s School of Business. While no specific lab or research group is named, his work aligns with digital service innovation, customer analytics, and sustainable business models — themes central to contemporary information systems research.

• Natural Language Processing
• Applied Data Science
• Computational Linguistics
• +3 more

Dr. Pablo Mosteiro Romero is an Assistant Professor at Utrecht University's Faculty of Social and Behavioural Sciences within the Department of Methodology and Statistics . His work bridges Natural Language Processing (NLP) , Applied Data Science , and Human-centered Artificial Intelligence , focusing on language change, morphology-syntax trade-offs, and applications in healthcare and open societies. Education: B.Sc. in Physics, Rutgers University (Summa Cum Laude, 2007) Ph.D. in Computer Science, Princeton University (2014, Centennial Fellowship) Research Interests revolve around computational linguistics, information-theoretic approaches to linguistics, and ethical AI. He investigates synonym evolution, morphosyntactic interactions, and fairness in mental health-focused AI systems. Recent work includes de-identification in medical texts and taxonomy induction via reinforcement learning. Publications span top venues like ACL, IEEE, and Springer, covering topics such as topic modeling interpretability, bias discovery in ML, and multimodal negation processing. Collaborations with colleagues like Scheepers, Spruit, and Kaymak highlight interdisciplinary applications in electronic health records. Awards include the Centennial Fellowship at Princeton and Summa Cum Laude at Rutgers. As a University Teaching Qualification holder, he teaches courses in data science, Python programming, and research methods, supervising theses in AI and Business Informatics. His work aligns with Utrecht's Institutions for Open Societies (IOS) theme, addressing gender diversity and global justice in data-driven systems.

• Natural Language Processing
• Clinical Text Mining
• Artificial Intelligence
• +7 more

Hercules Dalianis is a Professor at the Department of Computer and Systems Sciences, Stockholm University. His research focuses on Natural Language Processing, particularly in clinical text mining for Swedish language data. He leads the Natural Language Processing Research Group and serves as director of the Health Bank - Swedish Health Record Research Bank infrastructure. MSc in Electrical Engineering (1984), KTH PhD in Technology (1996), KTH Professor of Computer and Systems Science (2011), Stockholm University His research addresses privacy-preserving NLP for clinical text analysis, including automated de-identification , domain adaptation of BERT models , and clinical entity recognition . Current projects like DataLEASH and Privacy-Preserving Techniques explore machine learning solutions that balance data utility with patient confidentiality. Key publication trends show emphasis on Swedish clinical text processing , ICD-10 coding automation , and privacy-aware language modeling . Collaborations span Karolinska University Hospital, Nordic healthcare institutions, and international AI research communities. He teaches courses in Internet Search Techniques and Business Intelligence (ISBI) , Natural Language Processing (NLP) , and Principles and Foundations of Artificial Intelligence (PFAI) . His work has produced the open-access textbook Clinical Text Mining: Secondary Use of Electronic Patient Records , establishing foundational frameworks for clinical NLP in low-resource languages.

• Database Systems
• Natural Language Processing
• Knowledge Graphs
• +1 more

Nicolas HIOT is a Post-doctoral fellow at the University of Orleans affiliated with the LIFO laboratory (Laboratoire d'Informatique Fondamentale d'Orléans) and the Pamda project. His research bridges database systems, natural language processing, and medical informatics with a focus on text-to-database integration and consistency maintenance. His research interests center on: Database Systems for medical applications with emphasis on consistency and evolution Natural Language Processing for clinical text analysis and relation extraction Knowledge Graph construction from unstructured textual data Medical Informatics applications for healthcare data management Analysis of his 15 most recent publications (2020-2024) reveals a cohesive research trajectory at the intersection of databases and NLP. Key thematic clusters include automated medical database construction from clinical texts, consistency management in evolving RDF/property graph systems, and clinical entity/relation extraction for knowledge graphs. His work consistently addresses real-world challenges in healthcare data integration through tools like DataFix and ArchiTXT, demonstrating strong translational potential. Nicolas HIOT actively contributes to the LIFO research laboratory at the University of Orleans, collaborating extensively with Jacques CHABIN, Mirian HALFELD-FERRARI, and Dominique LAURENT. His technical output includes multiple software systems for database evolution management and clinical text processing, reflecting both theoretical contributions and practical implementations in semantic data management.

• 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.

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.

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.

Nadija PetramView profile

Professor

• Artificial Intelligence
• Information Mining
• Business Information Systems
• +1 more

Nadija Petram serves as Professor of Computer Science/Business Information Systems and Director of the Institute of Artificial Intelligence Methods and Information Mining (AIM) at Bremerhaven University of Applied Sciences, where she leads academic instruction and advanced research initiatives in AI-driven data science applications. Her research centers on Artificial Intelligence and Information Mining, with strong emphasis on Business Information Systems integration. As AIM Institute Director, she drives innovation in machine learning algorithms, pattern recognition, and data extraction methodologies applied to commercial and industrial contexts, bridging theoretical computer science with practical business solutions through interdisciplinary collaboration. While specific student advisement details remain undisclosed in available sources, her leadership role implies oversight of research teams within the AIM Institute. Grant activities likely focus on AI commercialization projects though explicit funding sources aren't documented in the provided materials. The Institute of Artificial Intelligence Methods and Information Mining (AIM) operates as her primary research ecosystem, developing cutting-edge tools for structured and unstructured data analysis with applications in logistics, maritime industries, and business intelligence systems unique to the Bremerhaven region's economic profile.

Oliver J. Bear Don’t Walk IV, ApsáalookeView profile

Assistant Professor

• Natural language processing
• Machine learning
• Community engagement
• +6 more

Oliver J. Bear Don’t Walk IV, Apsáalooke, serves as Assistant Professor in the Department of Biomedical Informatics and Medical Education at the University of Washington with special responsibilities at the Center for Indigenous Health. His work fundamentally reimagines biomedical informatics through Indigenous knowledge systems and community-driven ethics. His academic foundation includes: PhD in Biomedical Informatics, Columbia University MA, Stanford University BS, Stanford University Dr. Bear Don’t Walk's research pioneers epistemological justice in health AI through three interconnected pillars: technical development of NLP/ML systems for social driver identification, community governance models ensuring Tribal data sovereignty, and theoretical integration of intersectionality into algorithmic design. His projects actively collaborate with Indigenous communities to transform EHR data practices and rectify colonial research legacies. Recent publications (2020-2025) reveal consistent innovation at the ethics-technology nexus, particularly in race/ethnicity annotation frameworks, community-informed social driver extraction, and Tribal data governance protocols. His work demonstrates how technical NLP advancements must be inseparable from decolonial research partnerships. Dr. Bear Don’t Walk currently leads U01-funded research on HIV patient social drivers and mobile-based hallucination studies while teaching BIME 585. He explicitly welcomes new students committed to community-engaged, justice-oriented informatics and maintains active collaborations with Tribal health organizations through formalized research agreements.