Find Academic Supervisors

• 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.

• Natural Language Processing (NLP)
• Machine Learning
• Artificial Intelligence
• +3 more

Emanuele La Malfa is a Researcher at the University of Oxford's Department of Computer Science, affiliated with Trinity College. His research focuses on robustness guarantees in Natural Language Processing (NLP) and model explainability, addressing critical challenges in AI fairness, adversarial robustness, and theoretical foundations. He is involved in the FUN2MODEL project and has contributed to understanding linguistic structure fragility in neural networks. Key research themes include evaluating LLM robustness against adversarial attacks, exploring ethical implications of model exploitation (e.g., jailbreaking), and analyzing dialect fairness in reasoning tasks. His work bridges theoretical computer science with practical applications in AI safety and model interpretability. Publications span topics like multi-agent system limitations in LLMs, fixed-point explainability frameworks, and formalizing continuity properties of language models. His research emphasizes scalable communication protocols for distributed AI systems and the logical capabilities of large models through representation learning. La Malfa's contributions highlight interdisciplinary efforts, combining mathematical rigor with real-world NLP challenges, positioning him at the forefront of modern AI reliability research.

Arash TermehchyView profile

Associate Professor

• Machine Learning
• Data Systems
• Human-Data Interaction
• +6 more

Arash Termehchy is an Associate Professor in the Department of Electrical Engineering and Computer Science at Oregon State University's College of Engineering. His research focuses on data-centric systems, including machine learning on structured/heterogeneous data, human-data interaction, and scalable/robust reasoning systems. He leads the IDEA Lab and teaches courses such as CS 549 (Scalable Reasoning & ML), CS 540 (Database Management Systems), and CS 440 (Database Management Systems). His work has been recognized with awards like the 2019 ACM SIGMOD Research Highlight Award and multiple Best Paper selections. Termehchy holds a Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign. His research spans data cleaning methods for ML, scalable relational learning, and systems for translational biomedicine (e.g., RTX-KG2). He explores how users' data focus evolves during visual analysis and develops adaptive visualization techniques like ShiftScope. His work emphasizes practical applications of AI in data systems, including entity augmentation, query generation, and model consistency through declarative constraints. Key contributions include Schema-independent relational learning (Castor), variational databases, and the Data Exploration Game framework. His research bridges theoretical foundations (e.g., logical scalability of learning algorithms) with real-world systems (e.g., Universal-DB for graph analytics). Current projects address challenges in handling incomplete data, scalable schema mappings, and human-in-the-loop data exploration. Education: Ph.D., Computer Science, University of Illinois at Urbana-Champaign Labs/Teams: IDEA Lab (Investigating Data-centric AI & Exploratory Analytics) Grants: NSF III Collaborative Research on Similarity Metrics (2014), Yahoo! Key Scientific Challenges (2011)

Abelardo Carlos Martínez LorenzoView profile

Research Fellow

• Natural Language Processing
• Cross-lingual Semantics
• Information Extraction
• +3 more

Abelardo Carlos Martínez Lorenzo is a postdoctoral researcher at Sapienza University of Rome working under Professor Roberto Navigli in the Natural Language Processing group. His research focuses on cross-lingual semantics and information extraction, with specialization in semantic parsing and language models. His educational background includes: Software Engineering degree from Universidad de Málaga, Spain Erasmus Mundus Joint Master Degree in Big Data Management and Analytics from Université Libre de Bruxelles (Belgium), Universitat Politècnica de Catalunya (Spain), and Technische Universität Berlin (Germany) Martínez Lorenzo's research centers on overcoming language barriers through advanced semantic representation frameworks. His work develops efficient multilingual parsing systems that address data scarcity in low-resource languages, leveraging transformer architectures and novel linearization techniques. Key contributions include the BabelNet Meaning Representation (BMR) formalism and optimization methods for Abstract Meaning Representation (AMR) parsing, enabling robust cross-lingual semantic analysis without language-specific constraints. His publication record (2022-2024) reveals a consistent trajectory in enhancing AMR parser efficiency and multilingual capabilities. Notable innovations include CLAP's 80% reduction in computational time, cross-lingual alignment through transformer cross-attention, and ensemble methods that maintain structural integrity while improving SMATCH scores. These works collectively advance accessible, high-performance semantic analysis across linguistic boundaries. His scientific recognition includes: Marie Skłodowska-Curie Fellowship as Early Stage Researcher in the Knograph project (Horizon 2020) Funded through the prestigious Marie Curie grant, Martínez Lorenzo conducts research within the Knograph project framework while mentoring junior researchers in the Sapienza NLP group. His work demonstrates significant grant impact through open-source releases (CLAP, LeakDistill) that democratize access to semantic analysis tools. He actively contributes to the Sapienza Natural Language Processing group led by Roberto Navigli, which specializes in knowledge-based multilingual NLP systems. The group maintains strong collaborations through European research initiatives including Horizon 2020 projects, with emphasis on creating interlingual semantic resources and scalable parsing frameworks.

• Multi-agent systems
• Artificial intelligence
• Machine learning
• +8 more

Ho-fung Leung is a Professor at the Department of Computer Science and Engineering, Faculty of Engineering, Chinese University of Hong Kong. With a prolific publication record spanning over three decades, his research has significantly contributed to the fields of artificial intelligence, multi-agent systems, and natural language processing. His educational background, though not explicitly stated in the provided text, likely includes advanced degrees in computer science or a related field, given his extensive research contributions and faculty position at a prestigious university. Professor Leung's research interests span multiple areas within artificial intelligence, with a particular focus on multi-agent systems, reinforcement learning, natural language processing, and human-computer interaction. His work often explores the intersection of theoretical foundations and practical applications, developing novel algorithms and frameworks that address real-world challenges in AI systems. He has made significant contributions to constraint satisfaction problems, trust and reputation systems in multi-agent environments, and more recently to deep learning applications in NLP and human activity recognition. His recent publications demonstrate a strong trend toward applying advanced machine learning techniques to complex problems in natural language understanding, knowledge representation, and human activity recognition. Many of his papers focus on improving the efficiency, robustness, and interpretability of AI systems through innovative architectural designs and learning paradigms. Key research themes include few-shot learning, knowledge-enhanced models, and theoretical analysis of reinforcement learning dynamics. Professor Leung has received recognition for his work through numerous publications in top-tier conferences and journals, though specific awards are not detailed in the provided information. He has supervised numerous students throughout his career, with many of his publications featuring junior researchers in first-author positions. His research group appears to focus on cutting-edge problems in AI, with current projects spanning reinforcement learning theory, knowledge graph applications, and multimodal learning systems. Collaborators include researchers from across CUHK and international institutions. Professor Leung is actively involved in multiple research projects, with recent work focusing on human activity recognition using wearable sensors, knowledge-enhanced language models, and theoretical aspects of reinforcement learning. His research continues to evolve while maintaining strong connections to foundational AI principles, demonstrating remarkable adaptability in a rapidly changing field.

• Veterinary Medicine
• Toxicology
• Bioinformatics
• +4 more

Jiazhang Cai is a Professor in the Department of Precision One Health at the College of Veterinary Medicine, Ohio University. His research focuses on interdisciplinary areas including veterinary toxicology, bioinformatics, microbiome analysis, and machine learning applications in veterinary medicine. Key research interests include: Canine toxicology and emergency care protocols Biomarker discovery for neurodegenerative and environmental health disorders Development of spatial transcriptomics and ensemble algorithms in genomics Machine learning applications for bioinformatics and animal behavior analysis Recent studies explore topics like hypernatremia management in dogs, neuromuscular blockade effects in veterinary critical care, and multi-center clinical trials. His work bridges computational methods with clinical veterinary practice. Cai's publications span 2023–2025, demonstrating a focus on precision health approaches across animal and environmental systems. His research has implications for advancing diagnostic tools, therapeutic strategies, and understanding host-microbiome interactions.

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +3 more

Qian WANG is a Lecturer at the Faculty of Law of Université de Montréal, contributing to both teaching and interdisciplinary research. Her work bridges legal education with advanced computational methods, though her recent publications primarily focus on artificial intelligence and computer science. Research interests span a broad spectrum including artificial intelligence, machine learning, natural language processing, and computer vision. Her work often intersects with systems-level challenges such as operating system control and cognitive modeling, demonstrating a unique interdisciplinary approach. Publications reflect a trajectory from foundational contributions in graph theory and saliency detection (2020-2021) to cutting-edge advancements in large language models and reasoning frameworks (2023-2025). Key themes include improving AI capabilities through dynamic reasoning mechanisms and exploring the ethical dimensions of autonomous systems. No academic awards or grants are explicitly noted in the provided materials, though her prolific publication record indicates active research engagement. She currently holds no listed advisees or doctoral supervision roles. Labs or collaborative teams associated with her research are not detailed in the available information.

Gabor BELLAView profile

Associate Professor

• Multilingual Computational Systems
• Healthcare Data Interoperability
• Linguistic Diversity Modeling
• +4 more

Gabor BELLA is an Associate Professor (maître de conférences) in Data Science at IMT Atlantique's Data Science department in Brest, France. He holds a PhD in Computer Science and focuses on computational systems addressing linguistic diversity and interlingual communication challenges. His research emphasizes healthcare data interoperability and bias mitigation in AI language technologies. Key research areas include multilingual lexical databases, cross-lingual semantic alignment, and ethical considerations in AI. He leads the Universal Knowledge Core (UKC), a large multilingual lexicon emphasizing linguistic diversity, and coordinates the SCROLL NLP platform for semantic text analysis in structured data. Academic roles include Programme Chair at CONTEXT 2019 conference and serving as Associate Editor for Springer Nature Computer Science. Awards include a 2025 LoResLM Best Reviewer Award and 2024 ECAI Outstanding PC Member Award. Notable projects: InteropEHRate (health data interoperability), Diversicon Framework (pluggable lexical resources), and collaborations with organizations like NHS Scotland. His work bridges theoretical linguistics with applied NLP, particularly in minority language preservation and healthcare data integration. Recent publications address linguistic bias in AI, metonymy universality, and cross-lingual medical data standards. Supervises PhD students like Hadi Khalilia and collaborates widely across Europe and the Global South to advance equitable language technologies.

• Computer Science
• Artificial Intelligence
• Knowledge Graphs
• +3 more

Hao Wang is a researcher affiliated with Zhengzhou University in the School of Electrical and Information Engineering . His work spans interdisciplinary domains, primarily focusing on Computer Science , Artificial Intelligence , and Knowledge Graphs .

• Machine Learning
• Computer Vision
• Artificial Intelligence
• +3 more

Wei Jia is a Professor at the School of Computer and Information, Hefei University of Technology, China. Their research focuses on artificial intelligence, machine learning, computer vision, and robotics, with contributions to knowledge graphs, biometric systems, and autonomous systems. They have co-authored over 130+ publications in top-tier journals and conferences, including venues like IEEE Transactions, CVPR, and AAAI. Research interests span deep learning techniques, graph neural networks, and optimization for large-scale systems. Notable work includes entity extraction frameworks, safety analysis in engineering systems, and swarm control algorithms for unmanned vehicles. Contributions also extend to data management systems, such as the TierBase key-value store and the OVERLORD data loader for foundation models. Publications highlight interdisciplinary applications in cybersecurity, robotics, and biomedical imaging. Their work often bridges theoretical advancements with practical implementations, addressing challenges in both software and hardware systems. No specific awards or grants are listed in the provided text.

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +5 more

Xiaodan Zhu is an Associate Professor at Queen's University's Department of Electrical and Computer Engineering, affiliated with the Vector Institute for Artificial Intelligence and Ingenuity Labs Research Institute. He leads the Text Analytics and Machine Learning Lab (TAML), focusing on NLP, machine learning, and AI applications in areas like sentiment analysis, legal text, and healthcare. His Ph.D. (2010, University of Toronto) and M.Eng. (2000, Tsinghua University) form the foundation of his research career. Research Interests: Natural Language Processing (NLP), Machine Learning, AI ethics, legal AI solutions, and healthcare informatics. Notable contributions include the Tree-LSTM architecture, adversarial attacks on model calibration, and counterfactual recognition systems. His work has been published in top venues like ACL, NAACL, and EMNLP. Awards: Adam Kilgarriff *SEM Best Paper Award (2015/2024), JP Morgan Faculty Research Award (2022/2023), NSERC Discovery Accelerator Supplement. Key roles include Co-Editor-in-Chief for ACL Rolling Review, Associate Editor for IEEE/ACM TASLP, and organizing SemEval tasks. Professional Affiliations: Cross-joint appointment at the Scotiabank Centre for Customer Analytics. Supervised over 20 students, including recipients of Vector Institute Scholarships and NSERC awards. Active in legal AI initiatives with Queen's Law School and financial text analysis collaborations with industry partners.

Tao WangView profile

Research Fellow

• Natural Language Processing
• Health Data Science
• Multimodal Learning
• +3 more

Dr. Tao Wang is a Research Fellow at King’s College London (KCL), affiliated with the Institute of Psychiatry, Psychology & Neuroscience (IoPPN) and the Department of Biostatistics & Health Informatics. He holds honorary research positions at leading UK hospitals and serves on Meta/Facebook’s Global Safety Policy Advisory Board on Mental Health and King’s AI Institute. His research focuses on advancing Natural Language Processing (NLP), Network Science, Multimodal Data Modeling, and AI applications in healthcare, public health, sociology, and law. He completed his PhD at the University of Southampton (2015–2018) and has contributed to over 45 publications, securing prestigious awards like the Alan Turing Fellowship and the HSJ Partnership Award for Best Mental Health Partnership. Education: PhD in Health Informatics, University of Southampton (2015–2018), supported by the UK ESRC Research Exchange at the University of Western Australia Research Interests: Natural Language Processing (NLP) for healthcare data Knowledge Graphs and Causal Inference in health analytics Clinical Decision Support Systems Multimodal Learning and AI ethics Key Projects: VIEWER: a visual analytics framework for mental healthcare Clinical Decision Support Systems using CogStack Pain analysis via knowledge graph embeddings Awards: Chair’s Prize at NIHR Maudsley BRC conference (2019) Early Career Research Award (2022) HSJ Best Mental Health Partnership Award (2022) HETT Best Data Innovation Award (2022) Labs & Teams: Precision Health Informatics Data Lab (IoPPN), King’s AI Institute.

Jian QuView profile

Professor

• Knowledge Graphs
• Question Answering
• Natural Language Processing
• +2 more

Professor Jian Qu is affiliated with the University of Hamburg as a Petra Kappert Fellow at the Centre for the Study of Manuscript Cultures (CSMC). His research focuses on computational approaches to manuscript analysis, particularly knowledge graph question answering, ontology learning, and text-graph representations. He has made significant contributions to natural language processing techniques applied to scholarly data and biomedical text analysis. Qu's research bridges artificial intelligence with cultural heritage preservation, developing methods to extract and structure knowledge from historical documents. His recent work involves hybrid prompt learning for knowledge graph generalization and benchmarking question answering systems across diverse datasets. The interdisciplinary nature of his work spans computational linguistics, semantic web technologies, and digital humanities. His publications demonstrate consistent focus on improving knowledge extraction and question answering systems, with recent emphasis on generalization across datasets and domains. Methodological innovations include attention-based neural networks for biomedical classification and unsupervised approaches for ontology construction from textual data.

Hakan EmekciView profile

Assistant Professor

• Artificial Intelligence
• Data Science
• Machine Learning
• +5 more

Hakan Emekci is an Assistant Professor at TED University's Graduate School of Applied Data Science. He teaches courses such as Statistical Learning, Data Analytics, and Information Retrieval in the Applied Data Science Master Program. His research focuses on AI applications in healthcare, education technology, econometrics, and data-driven methodologies. Emekci has published works on specialized language models for medical contexts, RAG systems optimization, and the integration of AI in educational frameworks. His recent studies emphasize domain-specific AI solutions and their practical implementations across diverse sectors. Emekci's academic contributions include analyzing central bank policies' impact on financial markets and developing computational frameworks for data mining. He actively teaches advanced courses at both undergraduate and graduate levels, including Data Structures and Algorithms. His work bridges theoretical data science concepts with real-world applications in healthcare, education, and finance. Notable research areas include: Healthcare informatics & AI-driven diagnostic systems Educational technology innovations Machine learning model optimization Financial econometric analysis Domain-specific large language models His publications from 2025 highlight advancements in multi-agent medical frameworks and comparative studies of retrieval-augmented generation techniques. Earlier work (2017-2018) established foundational research in econometric policy analysis and R-based data mining methodologies.

Thomas BonaldView profile

Professor

• Data Analysis
• Machine Learning
• Graph Theory
• +4 more

Thomas Bonald is a Professor at Telecom Paris, part of the Institut Polytechnique de Paris, and heads the DIG (Data, Intelligence and Graphs) team within the LTCI laboratory. His research focuses on data analysis, machine learning, graph theory, knowledge bases, and natural language processing. He leads projects like the YAGO knowledge base and develops open-source tools such as scikit-network and torch-kge for graph analysis and knowledge graph embedding. Key achievements include advancing link prediction, knowledge base refinement using LLMs, and contributions to fair resource allocation in networks. His work integrates theoretical insights with practical applications, including enhancing adaptive streaming performance in mobile networks. Scientific awards include recognition of his former students Céline Comte (Telecom Paris award) and Mathieu Feuillet (Gilles Kahn award). Bonald advises numerous PhD students and has supervised over 20 doctoral candidates. His research spans algorithmic fairness, network science, and large-scale data integration. Labs/Teams: DIG team at LTCI, contributing to projects like YAGO and NetSet datasets. His teaching includes probability, statistics, graph learning, and reinforcement learning, with lecture notes on topics like PageRank and spectral embedding.