Find Academic Supervisors

• Data Science
• Machine Learning
• Natural Language Processing
• +7 more

Luca Cagliero is an Associate Professor (L.240) at the Department of Control and Computer Science (DAUIN) at Politecnico di Torino . He serves as Coordinator of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory and Scientific Advisor for the Partnership Agreement with TIERRA. His research spans Data Science , Machine Learning , and Natural Language Processing , with a focus on Financial Data Mining , Generalized Pattern Mining , and Legal AI . Scientific Branch: IINF-05/A - Information Processing Systems (Area 0009 - Industrial and Information Engineering) ERC Sectors: PE6_7 (Artificial Intelligence), PE6_9 (Human Computer Interaction), PE6_11 (Machine Learning), PE6_10 (Web and Information Systems) His scientific awards include the GiovedìScienza Award (2013), Working Capital PNI (2011), and Optime. Recognition of Merit in the Study (2008). He is a Fellow of ACM (2010-) and Effective Member of IEEE (2010-). As an Associate Editor for journals like Expert Systems with Applications and Machine Learning with Applications , he contributes to academic publishing. His conference roles include Program Committee memberships at ACM SIGMOD 2023, IEEE ICDM 2021, and ACM CIKM 2018. Luca supervises PhD students in areas such as AI-driven Cybersecurity , Conversational AI , and Neural Explainers , including Aurora Gensale, Giuseppe Gallipoli, and Irene Benedetto. His commercial research contracts involve projects like AI for Trend Analysis (Intesa Sanpaolo), Predictive Maintenance , and Legal Document Processing . Key research trends include Retrieval Augmented Generation for visually-rich documents, Bias Mitigation in speech models, and Shapley Value Estimation for model explainability. His work bridges Natural Language Processing with Cybersecurity in automotive systems.

Anthony RiosView profile

Assistant Professor

• Biomedical informatics
• Computational social science
• Natural language processing
• +1 more

Dr. Anthony Rios is an Assistant Professor in the Department of Information Systems and Cyber Security at The University of Texas at San Antonio (UTSA), affiliated with the Alvarez College of Business. He holds a Ph.D. in Computer Science from the University of Kentucky (2018) and a B.S. in Computer Science from Georgetown College (2011). His research focuses on biomedical informatics, computational social science, natural language processing (NLP), and machine learning, with applications in healthcare and social media analysis. Dr. Rios has published extensively in top venues such as EMNLP, NAACL, AMIA, and JAMIA, and has received awards including a distinguished poster finalist award at AMIA 2014 and a best paper finalist at ICHI 2015. His work includes developing robust NLP systems for electronic medical records (EMR) coding, combating bias in AI systems (e.g., dialect biases in privacy policies), and enhancing cybersecurity through SQL-based threat detection frameworks. He leads the UTSA-NLP team, contributing to projects like ArchEHR-QA and Chemotimelines. Current research explores large language model (LLM) validation, ethical AI, and scalable evaluation of data visualization systems. Education: Ph.D. in Computer Science, University of Kentucky, 2018 B.S. in Computer Science, Georgetown College, 2011 Awards: 2014: Distinguished Poster Finalist Award, AMIA 2015: Best Technical Paper Finalist, ICHI Labs/Teams: UTSA-NLP Lab, focused on NLP applications in healthcare and cybersecurity

Bevan KoopmanView profile

Associate Professor

• Information Retrieval
• Health Informatics
• Natural Language Processing
• +2 more

Dr. Bevan Koopman leads the Health Search team at CSIRO's Australian e-Health Research Centre and serves as Associate Professor in Language Technologies at University of Queensland. His research develops AI systems for clinical information retrieval, focusing on evidence-based medicine and health decision support. Current work examines large language models for medical question answering, automated clinical trial matching, and systematic review acceleration. Methodologies combine neural ranking, query refinement, and domain-specific knowledge integration. Publications demonstrate innovations in semantic search for medical records, Boolean query generation for systematic reviews, and conversational agents for agricultural health questions. Recent work evaluates ChatGPT for health information reliability. Teaching emphasizes quantitative skills development in biosciences. Funded projects include MathBench Australia for improving STEM education and automated diagnostic coding systems.

• Computer Vision
• Medical Image Analysis
• Machine Learning
• +4 more

Dr. Qianqian Zhang is an Associate Professor at China Agricultural University's College of Information and Electrical Engineering, specializing in computer vision, medical imaging, and IoT security. With over 140 publications spanning from 2007 to projected 2026, her research demonstrates consistent productivity across multiple domains of computer science and engineering. Her research interests focus on practical applications of machine learning in medical diagnostics, autonomous systems, and information security. She has developed innovative approaches in object detection (YOLO-FCE), medical image analysis for renal carcinoma diagnosis, and lightweight neural networks for image steganalysis. Her work bridges theoretical computer science with real-world healthcare and security applications. Analysis of her recent publications (2023-2026) reveals a strong trend toward interdisciplinary research combining computer vision with medical diagnostics, particularly in renal tumor analysis using CT imaging. She has also maintained significant contributions to IoT security through federated learning approaches and developed novel methods for motion sickness analysis using EEG and virtual reality. Featured in IEEE Access with 15 publications (2020) Published in Pattern Recognition, a premier computer vision journal Contributions to BMC Medical Imaging for clinical applications Multiple publications in IEEE Internet of Things Journal Dr. Zhang actively supervises graduate students including Peng Liu, Tiancheng Zhao, and Jiajia Liao, with whom she has co-authored numerous papers on vision-language models and multimodal learning. Her research group appears to focus on practical implementations of deep learning for real-world problems in healthcare, transportation, and security.

Justus RobertsonView profile

Assistant Professor

• Human-Centered Artificial Intelligence
• Computer Games
• Interactive Narrative

Justus Robertson is an Assistant Professor in the School of Interactive Games and Media at Rochester Institute of Technology (RIT). His research focuses on human-centered AI, computer games, and interactive narrative systems. He develops AI agents that generate dynamic stories using computational models of narrative theory and human cognition, creating immersive experiences such as adaptive training scenarios and entertainment narratives. His work aims to merge targeted story structures, simulated environments, and character behavior with human interaction, inspired by concepts like Star Trek's holodeck. Key projects include the General Mediation Engine (GME), a framework for interactive narratives, and BaseCase, a Unity-based demo showcasing GME applications. Robertson also explores esports analytics, player performance metrics, and data-driven audience engagement strategies through platforms like Dax. His GitHub repositories (e.g., GME, LitWiki) highlight contributions to game development frameworks and academic literature management tools. Collaborative projects like IC-CRIME demonstrate his interest in applied technology for crime scene analysis.

Natalie SchluterView profile

Associate Professor

• Theoretical Computer Science
• Machine Learning
• Natural Language Processing
• +3 more

Natalie Schluter is an Associate Professor at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU), and a Machine Learning Researcher at Apple's Machine Learning Research Organisation under Samy Bengio. She previously held roles including Head of Programme for the Data Science BSc at the IT University of Copenhagen, Senior Research Scientist at Google Brain/DeepMind, Lead Data Scientist at MobilePay, and postdoctoral researcher positions at the University of Copenhagen and Malmö University. Her education includes a PhD in Natural Language Processing from Dublin City University, an MSc in Mathematics from Trinity College Dublin, an MA in Linguistics (Semantics) from Université de Montréal, and a BA in French and Spanish from the University of British Columbia. Her research interests span Theoretical Computer Science (Graph Algorithms and Automata), Machine Learning, and their applications in Natural Language Processing and Data Science. Key areas include text summarization, parsing, cross-lingual NLP, and algorithmic fairness. She has contributed significantly to multilingual NLP datasets like MassiveSumm and DaNewsroom, and her work bridges theoretical foundations with practical NLP systems. Publications highlight innovations in neural network regularization, dependency parsing, and cross-lingual inference, with a focus on Danish and low-resource languages. Current advisees include PhD student David Sasu. She has advised numerous students and researchers, contributing to impactful NLP tools like the UniParse toolkit. Her career reflects a blend of academic and industry leadership, driving advancements in NLP theory and application while fostering education through pioneering programs like the Data Science BSc.

R.S.K. ChandrasegaranView profile

Assistant Professor

• Computer-Supported Collaborative Design
• Visual Analytics
• Human-AI Interaction
• +3 more

R.S.K. (Senthil) Chandrasegaran is an Assistant Professor at the Faculty of Industrial Design Engineering (IDE), Delft University of Technology. His work focuses on integrating computer support tools to enhance collaborative design processes and applying visualization techniques to analyze designer interactions. He is affiliated with the Designing Intelligence Lab (DI_Lab) , where he investigates how visual analytics can model and improve human-AI collaboration in design. Education: Ph.D. in Mechanical Engineering, Purdue University Previous Positions: Postdoctoral Researcher at University of Maryland (HCI Lab) and University of California, Davis (VIDI Lab) Senthil's research interests center on: Visual analytics for understanding collaborative design behaviors AI agents in creative processes Linguistic methods to analyze design discourse Human-AI interaction frameworks His recent publications examine agent conversations, data humanism in design, and computational analysis of design language. The work emphasizes: AI-driven design support systems Visualization of complex design interactions Linguistic markers of creativity and collaboration He teaches courses related to design computing and has contributed to frameworks for integrating electronics into mechanical design education.

• Natural Language Processing
• Information Retrieval
• Computational Linguistics
• +3 more

Dr Emma Barker is a Research Fellow and Data Science & AI Community Manager at the University of Sheffield's School of Computer Science. Her work focuses on Natural Language Processing (NLP), computational linguistics, and data science applications. She has contributed to projects involving term extraction from web texts, analysis of online news reader comments, and development of spoken language corpora. Her research bridges theoretical NLP advancements with practical applications in information retrieval, social media analysis, and cross-lingual systems. Her publications emphasize techniques like graph-based clustering for news comment analysis, automated label generation for topic categorization, and corpus development for dialogue systems. She collaborates on interdisciplinary projects such as the SENSEI corpus and the Sheffield Numbers Spoken Language Corpus (SNuC). Barker also plays an active role in community-building through her Data Science & AI Community Manager role, fostering academic-industry partnerships and advancing AI adoption in real-world contexts. Key research themes include argumentation mining in multi-party conversations, cross-domain document classification, and bootstrapping methods for multilingual term extraction. Her work spans technical innovations in NLP tools integration, evaluation metrics for summarization systems, and graph-theoretic approaches to content analysis. Despite no listed awards, her contributions to open-source corpora and collaborative frameworks reflect her commitment to advancing computational linguistics and data science practices.

Michael GertzView profile

Professor

• Data Science
• Natural Language Processing (NLP)
• Machine Learning
• +3 more

Michael Gertz is a full professor at Heidelberg University, leading the Data Science Group within the Faculty of Mathematics and Computer Science. He holds a diploma from TU Dortmund University and a Dr. rer. nat. from Leibniz University Hannover (1996). Before joining Heidelberg in 2008, he was faculty at the University of California, Davis (1997–2008). His research spans Data Science, Natural Language Processing, Machine Learning, Complex Networks, and Legal Technology, with applications in law, medicine, economics, and political sciences. Academic roles include Dean of the Faculty of Mathematics and Computer Science (2014–2016), Managing Director of the Institute of Computer Science (2016–2023), and co-speaker of the Helmholtz Information & Data Science School for Health (since 2019). He has held editorial roles at ACM Transactions on Spatial Algorithms and Systems, ACM Journal of Data and Information Quality, and The VLDB Journal. Research focuses on interdisciplinary projects, such as legal text analysis, network models, and scientific data management. He has presented widely, including keynote speeches on AI in law and legal technology. His work emphasizes exploring quantities in text, temporal networks, and event detection in social media. Professional activities include program committee memberships for conferences like SIGIR, EMNLP, and CIKM, as well as reviewing for funding agencies like the Helmholtz Society and DFG. He is a member of ACM, IEEE Computer Society (inactive since 2012), and Gesellschaft für Informatik (GI). Key projects include developing frameworks like EVELIN, CQE, and QuantPlorer, and datasets such as EUR-Lex-Sum and Klexikon. His contributions bridge computational methods and domain-specific applications, fostering innovation in data-driven solutions across disciplines.

Evangelos KanoulasView profile

Professor

• Information Retrieval
• Machine Learning
• Natural Language Processing
• +3 more

Evangelos Kanoulas is a Professor at the Informatics Institute, University of Amsterdam. He holds a PhD in Computer Science from Northeastern University, Boston, USA (2010). His research focuses on information retrieval, machine learning, natural language processing, and conversational systems. He has made significant contributions to topics such as adversarial attacks in retrieval systems, knowledge-enhanced recommendation, and large language model applications in legal and product search domains. Education: PhD in Computer Science (Northeastern University, 2010). His work often bridges theory and practice, addressing challenges in robust retrieval, conversational AI, and multimodal systems. He has organized workshops such as ALTARS (Augmented Intelligence in Technology-Assisted Review Systems) and KEIR (Knowledge-Enhanced Information Retrieval), highlighting his role in advancing interdisciplinary research. Research interests include adversarial machine learning, conversational search, and AI-driven systems for legal and e-commerce domains. His recent work explores efficient training of large language models, robustness against typos, and generative models for query rewriting. His publications span top venues like SIGIR, ECIR, and ACL, with a focus on practical applications of NLP and IR. Despite no explicit mention of awards, his prolific publication record and leadership in organizing conferences reflect his academic impact.

Xiaojun ZengView profile

Professor

• Machine Learning
• Computational Intelligence
• Decision Support Systems
• +5 more

Prof. Xiaojun Zeng is a Professor of Machine Learning and Optimisation at the Department of Computer Science, University of Manchester. He holds a B.Sc. in Mathematics from Xiamen University and a Ph.D. in Computation from the University of Manchester. His research focuses on computational intelligence, machine learning, decision support systems, computational finance, health informatics, and data mining. He has published over 200 papers and contributed to projects like the EU-funded IoTRAIN initiative, aiming to modernize IoT education in Iran and Iraq. He has received prestigious awards, including the Microsoft European Retail Application Developer Award (2003) and the European IST Award (1999). His work spans academic leadership roles, including serving as an Associate Editor for IEEE Transactions on Fuzzy Systems and involvement in international conferences. Prof. Zeng’s research also addresses energy demand management and sustainable development goals, reflecting a commitment to interdisciplinary innovation. Education: B.Sc. in Mathematics, Xiamen University M.Sc. in Control Theory and Operations Research, Xiamen University Ph.D. in Computation, University of Manchester Research Interests: Computational intelligence, machine learning applications in healthcare and finance, decision support systems, and IoT integration in engineering education. His work bridges theoretical advancements with practical solutions in retail pricing, energy systems, and smart grids. Key Projects: IoTRAIN (2020–2023): EU project enhancing IoT education in Iran/Iraq. EPSRC and EU Framework projects on pricing, QoS, and cloud computing. Awards: Microsoft European Retail Application Developer Award (2003) European IST Award (1999) Grants & Advising: Supervised 40+ students and contributed to funded projects like the UK EPSRC pricing decision support initiative and EU H2020 Big Data in Finance. His research also impacts industrial collaborations in petroleum pricing and retail revenue management. Labs & Affiliations: Member of the Data Science Institute, Machine Learning and Optimisation group, and Christabel Pankhurst Institute at the University of Manchester.

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +6 more

Yulong Chen is a Researcher at the Department of Computer Science and Technology, University of Cambridge. He specializes in Natural Language Processing (NLP), focusing on areas such as large language models (LLMs), constituency parsing, cross-lingual summarization, and dialogue systems. His work bridges theoretical advancements with practical applications, including automated claim verification, multimodal intelligence, and ethical AI considerations. Chen contributes to both teaching and research: he teaches courses on Natural Language Processing at both undergraduate and postgraduate levels. His research emphasizes interdisciplinary approaches, combining machine learning, computational linguistics, and cognitive models. Key projects include developing benchmarks for higher-order theory of mind reasoning in LLMs and designing frameworks to uncover model weaknesses. His publications span foundational surveys (e.g., on LLM hallucinations and text-to-SQL systems) to applied work like MACSum for controllable summarization and Hi-TOM for evaluating social cognition in AI. He collaborates extensively on tools like EASE for efficient data annotation and UniSumm for few-shot summarization, reflecting his commitment to advancing both NLP infrastructure and model capabilities.

Daniel BuschekView profile

Professor

• Human-Computer Interaction (HCI)
• Artificial Intelligence
• Machine Learning
• +4 more

Professor Daniel Buschek leads the research group in Mobile Intelligent User Interfaces at the University of Bayreuth's Faculty of Mathematics, Physics and Computer Science. His work focuses on integrating Human-Computer Interaction (HCI) with Machine Learning/AI to design novel user interfaces that enhance the effectiveness, efficiency, and security of digital technology use. He explores both 'AI for better UIs' and 'better UIs for AI,' addressing challenges in interactive systems, generative models, and ethical considerations in human-AI collaboration. His research spans topics such as AI writing assistants, user perception of AI-generated content, and adaptive touch interfaces. Buschek's team includes prominent researchers like Florian Lehmann and Hai Dang, contributing to conferences like CHI and ACM DIS. He has published extensively on topics ranging from generative AI interaction design to behavioral biometrics and continuous authentication systems. Key contributions include frameworks for understanding intelligent systems, tools like SummaryLens and GestureMap, and studies on user behavior in AI-driven environments. Buschek's work emphasizes user-centric design, ethical AI integration, and the societal implications of emerging technologies.

• Artificial Intelligence
• Machine Learning
• Data Mining
• +3 more

Dr. Maryam Khanian Najafabadi is a Lecturer in the School of Computer Science at The University of Sydney. Her expertise spans Computational Intelligence, Artificial Intelligence, and Natural Language Processing, with a focus on applying machine learning and data mining techniques to solve real-world challenges in industry and academia. She holds a strong track record of research in recommendation systems, sentiment analysis, and biometric systems, and has led projects integrating AI methodologies across diverse sectors. Her research interests include Machine Learning, Data Mining, Recommendation Systems, and Natural Language Processing, with current projects emphasizing intelligent techniques for user-item interaction analysis and sentiment/emotion detection in text. She has published extensively in Tier 1 journals and conferences, with a focus on graph neural networks, hybrid recommendation models, and sarcasm detection frameworks. Dr. Najafabadi is committed to education, teaching courses in Machine Learning, Data Mining, Programming Languages, and Computer Science fundamentals. She emphasizes collaborative learning and practical skills development in her teaching. While no specific grants are detailed, her work demonstrates active engagement in both academic and applied research. Her professional activities include supervising undergraduate/postgraduate projects and leading industry-linked research initiatives. Her work has been recognized through awards and credentials (specific details unlisted here), reflecting her contributions to computational intelligence and interdisciplinary applications.

• Artificial Intelligence
• Natural Language Processing
• Linked Data
• +8 more

Professor Kai Eckert holds the position of Professor for Artificial Intelligence at the Department of Computer Science, Mannheim University of Applied Sciences since March 2023. His research focuses on Artificial Intelligence, Natural Language Processing, Linked Data, and their applications in Social Sciences, Information Science, and Digital Humanities. He has contributed to projects like JudaicaLink and Linked Open Citation Database, emphasizing data integration and provenance. Research Interests: His work spans AI-driven solutions for data management, semantic technologies, and domain-specific knowledge bases. Notable areas include RDF validation, metadata modeling (RDF-Star, Named Graphs), and cross-lingual summarization (X-SCITLDR). He explores ethical and technical aspects of data provenance in scholarly communication and library systems. Publications: Over 35+ peer-reviewed works since 2007. Key themes include semantic web applications, metadata provenance (e.g., Dublin Core extensions), and crowdsourced ontology development. Recent trends highlight AI in social networks (cyberbullying detection) and multilingual NLP systems. Grants & Collaboration: Active in EU-funded initiatives and international conferences (e.g., ISWC, ESWC). Developed tools like Semtinel for interactive indexing and ICE-Map for KOS evaluation. Collaborates with institutions like Stuttgart Media University and Springer.