Find Academic Supervisors

Xander WilckeView profile

Academic

• Machine Learning
• Data Mining
• Knowledge Representation
• +5 more

Xander Wilcke is a Research Associate at the Faculty of Science, VU University, with affiliations to the Artificial Intelligence Research Associate group and the Network Institute. His work focuses on machine learning applied to knowledge graphs, data mining, and multimodal data analysis. He holds a PhD in Machine Learning for Heterogeneous Knowledge Graphs from VU University (2022). Research Interests: Machine Learning on knowledge graphs, multimodal data integration, pattern discovery, and archaeological data analysis. His work emphasizes user-centric approaches and end-to-end learning frameworks for heterogeneous data. Recent projects include hypothesis creation support systems and timestamp vectorization techniques. Awards: Best-Paper Award Nominee at KDIR 2020 ICT.OPEN Best Poster Presentation Award (Runner-up, 2018) Advising & Grants: Contributed to the development of the kgbench dataset collection Recipient of grants supporting knowledge engineering and data mining research Active in academic collaborations, presenting at conferences like SEMANTICS 2024 and organizing workshops on social-historical knowledge graph analysis. Teaches courses on Knowledge Representation and Machine Learning for Graphs at VU University.

• Algorithms
• Computational Geometry
• Dynamic Networks
• +2 more

Bernard Chazelle is Professor of Computer Science at Princeton University. His research spans theoretical computer science, algorithms, computational geometry, and complex systems, with applications to biological modeling, social dynamics, and network science. Chazelle's work develops geometric and algorithmic approaches to complex phenomena, including disease spread modeling, consensus formation, and collective behavior. His recent publications focus on energy-based modeling, network-augmented epidemiological frameworks, and geometric representations of social trends. His research demonstrates consistent innovation in applying discrete mathematics to natural and social systems. Recent trends include network-based disease modeling, consensus dynamics in adaptive networks, and geometric analysis of social phenomena.

Wen HuaView profile

Research Fellow

• Data Science
• Machine Learning
• Data Mining
• +5 more

Dr. Wen Hua is an Honorary Senior Research Fellow at the University of Queensland's School of Electrical Engineering & Computer Science. His research focuses on Data Science, Machine Learning, and Knowledge Graphs, with significant contributions to privacy-preserving techniques, neural network architectures, and trajectory analysis. He has co-authored over 80 peer-reviewed publications, including influential works on entity linking, meta-learning, and spatiotemporal data privacy. His work bridges theoretical advancements with practical applications in areas like recommendation systems, sentiment analysis, and information resilience. Key research interests include: Machine Learning and Deep Learning Privacy-Preserving Data Publishing Knowledge Graph Construction Text Mining and Natural Language Processing Spatiotemporal Trajectory Analysis Information Retrieval Systems Recent publications highlight trends in: Neural architectures for meta-learning and transformer-based systems Efficient algorithms for relation extraction and entity disambiguation Temporal and spatial data anonymization techniques Integration of knowledge graphs with text corpus analysis

Qing WangView profile

Associate Professor

• Graph Machine Learning
• Data Management
• Data Analytics
• +3 more

Dr. Qing Wang is an Associate Professor at the Australian National University (ANU) since 2012, leading the Graph Research Lab and the Database Group within the School of Computing. Her research focuses on graph machine learning, data management, and algorithms for dynamic networks. With over a decade of industry experience in data analysis and management, she holds a PhD in Computer Science (Summa Cum Laude) from Christian-Albrechts-University Kiel, Germany. Education: PhD (Dr.rer.nat.) in Computer Science, Christian-Albrechts-University Kiel, Germany (2010) Master of Information Systems (First Class Honours), Massey University, New Zealand (2009) Master of Economics, Jinan University, China Bachelor of Engineering, South China University of Technology, China Research Interests: Dr. Wang explores cutting-edge techniques in graph machine learning, scalable algorithms for large-scale data, and knowledge tracing. Her work bridges theoretical foundations with practical applications, such as improving graph neural networks' expressivity and optimizing distance queries on dynamic road networks. She actively contributes to the development of tools for data integration and privacy-preserving analytics. Grants & Awards: ARC Discovery Project (2021–2023): 'Deep Learning for Graph Isomorphism' ARC Discovery Project (2016–2018): 'Creating the Social Genome' ANU Dean’s Award for Teaching Excellence (2015) Fellow of the Higher Education Academy (2015) Labs & Teams: She leads the Graph Research Lab , focusing on advancing graph algorithms and their applications in real-world systems. Her team collaborates on projects involving dynamic data integration, privacy-preserving techniques, and educational data analytics.

• Data Science
• Artificial Intelligence
• Machine Learning
• +7 more

Mohsen Farid is a Senior Lecturer in Data Science at the College of Science and Engineering. His research spans interdisciplinary areas including artificial intelligence, machine learning, neurotechnology, and healthcare analytics. He has contributed to advancements in genomics data management, video authentication, and neurotechnological solutions for mental health conditions like PTSD. His work frequently integrates computational methods with real-world applications in healthcare and cybersecurity. Key research outputs include studies on storage-optimized genomics systems (2024), neurotechnological interventions for PTSD (2023), and deep learning analyses of histopathology images (2021). His earlier contributions include gait recognition systems (2018) and semantic mapping of legal arguments (2015). Farid’s publications reflect a strong focus on applied data science and its societal impact, particularly in healthcare, security, and biomedical engineering. While no specific awards or grants are explicitly mentioned, his extensive publication record highlights sustained contributions to interdisciplinary data-driven research. His articles often address challenges in data management, pattern recognition, and ethical applications of AI.

Laura Smith ChowdhuryView profile

Professor

• Applied Mathematics
• Crime Modeling
• Complex Networks
• +5 more

Laura Smith Chowdhury is a Professor in the Department of Mathematics at California State University, Fullerton (CSUF). She holds a PhD from the University of California, Los Angeles (UCLA), and MS and BS degrees from Western Washington University. Her research focuses on applied mathematics, crime modeling, complex networks, dynamical systems, image processing, and computational science. She has contributed to agent-based models for social dynamics, territorial gang patterns, and public health interventions. Notable works include stochastic models for political conflict forecasting and network partitioning algorithms. Her research spans diverse applications, including crime prevention strategies for resource-limited police departments, social media analysis for controversial topics, and migration patterns in population dynamics. She has developed methodologies like spectral clustering with epidemic diffusion and variational methods for density estimation. Her work integrates computational tools with real-world problems in policy, epidemiology, and urban planning. Prof. Chowdhury’s publications appear in journals like the Bulletin of Mathematical Biology, European Journal of Applied Mathematics, and ACM Transactions on Knowledge Discovery from Data. She co-authored a patent (US 8,938,115 B2) on data fusion mapping techniques. Her research often bridges mathematical theory with practical societal challenges, emphasizing interdisciplinary collaboration.

Achim RettingerView profile

Professor

• Machine Learning
• Knowledge Representation
• Natural Language Processing
• +4 more

Achim Rettinger is a full professor at Trier University, leading the research group krAil (Knowledge Representation Learning). He specializes in machine learning, natural language understanding, and human-centered AI. His work focuses on expressive knowledge representations and their applications in semantic technologies. Education: Studied Computer Science at Universität Koblenz (Germany), University of Georgia (USA), and University of Alberta (Canada). PhD in machine learning at TU Munich/Siemens AG, followed by habilitation at KIT (2016). Served as interim professor at Karlsruhe Institute of Technology (2018/19). Research interests include knowledge graphs, cross-lingual semantic annotation, and data-driven analysis in political and medical domains. Notable contributions include the X-LiSA framework and work on semantic web technologies. Awards include best paper and challenge awards at ISWC and ESWC conferences. Leadership roles include senior PC member at ISWC, track chair at ESWC, and membership in AI for Good Foundation. Active in EU projects, DFG grants, and large-scale collaborative initiatives. Key projects: BreXearch (cross-lingual Brexit analysis), xLiMe System (semantic search), and medical decision support systems for liver surgery. Collaborates with interdisciplinary teams on cognition-guided surgery and data integration. Publications span top venues like ISWC, NeurIPS, ICLR, and CVPR, with a focus on semantic web, machine learning, and applied AI solutions.

Huiping CaoView profile

Professor

• Databases
• Deep Learning
• Data Mining
• +7 more

Huiping Cao is a Professor in the Department of Computer Science at New Mexico State University (NMSU). She holds a Ph.D. from The University of Hong Kong (2007), and bachelor’s and master’s degrees from Renmin University of China (1999 and 2002). Her expertise spans databases, deep learning, data mining, time series analysis, and graph mining. Her research focuses on developing computational methodologies to extract knowledge from complex data such as sequences and graphs, with applications in smart grids, animal and range sciences, agriculture, and high-performance computing. Dr. Cao’s work emphasizes interdisciplinary applications, including real-time cattle monitoring using sensors (GPS and accelerometers) for disease detection, parturition prediction, and grazing behavior analysis. She has developed tools like GRAZETOOLS for livestock behavior analysis and contributed to precision ranching systems using LoRa-WAN sensors. Her methodological innovations include backbone index models for multi-cost road networks and federated learning frameworks to address data imbalance in distributed systems. Her research has addressed challenges such as uncertainty quantification in time series analysis, probabilistic modeling for segmentation tasks, and sensor error evaluation. She collaborates across disciplines, integrating machine learning with environmental and agricultural domains. Dr. Cao’s contributions also include advancements in graph-based influence discovery, semantically annotated observational data systems, and compression techniques for power grid monitoring data.

Tuan LeView profile

Assistant Professor

• Data Mining
• Machine Learning
• Text Mining
• +3 more

Tuan Le is an Assistant Professor in the Department of Computer Science at New Mexico State University (NMSU), serving as Coordinator of the Master of Data Analytics Program. He holds a Ph.D. from the School of Information Systems at Singapore Management University (2017). His research focuses on data mining, machine learning, text mining, visualization, and graph mining, with applications in semantic analysis, anomaly detection, and interactive visualization techniques. Education: Ph.D., School of Information Systems, Singapore Management University (2017). Research Interests include advanced topic modeling, visualization of complex datasets, graph-based data analysis, and leveraging pre-trained language models for document structure learning. His work emphasizes practical applications in user preference modeling, multi-view anomaly detection, and semantic information retrieval. Notable awards include the 2024 Patricia Christmore Faculty Teaching Award nomination and the AAAI 2014 Honorable Mention for Outstanding Paper. He has also held postdoctoral roles at Carnegie Mellon University and visiting positions at Oakland University. Academic contributions include pioneering projects such as ContraVis (contrastive visual topic modeling) and SEMAFORE (semantic visualization with manifold regularization), reflecting his expertise in developing innovative tools for data analysis and visualization.

Vijayan SugumaranView profile

Professor

• Big Data Management
• Data Science
• Intelligent Agents
• +5 more

Vijayan Sugumaran is a Distinguished Professor of Management Information Systems at Oakland University's School of Business Administration. He chairs the Department of Decision and Information Sciences and co-directs the Institute for Data Science. He holds a Ph.D. in Information Technology from George Mason University (Virginia, USA). His research focuses on Big Data Analytics, Intelligent Agents, and Semantic Web technologies. He leads a $2M NSF grant for STEM-driven data science training and has authored over 350 peer-reviewed articles across journals like Information Systems Research and IEEE Transactions on Big Data . Teaching interests include Advanced Database Systems, Machine Learning, and Predictive Analytics. He has received awards such as the SAS Educator Award (2025), Janke Scholar (2023-2026), and Fulbright Specialist (2012). He serves as editor-in-chief of the International Journal of Intelligent Information Technologies and chairs conference tracks like the Intelligent Agent mini-track at AMCIS. His work bridges academia and industry through collaborations in data science education, smart city infrastructure, and cybersecurity. He advises on analytics programs and has developed frameworks for urban infrastructure resilience using Digital Twins and multi-agent systems. Current projects include blockchain-based participation systems and AI-driven cyberbullying detection.

• Data Mining
• Big data
• Fuzzy Clustering
• +10 more

HINDAWI Mohammed is a Researcher-Lecturer at CESI (France) specializing in Computer Science with a focus on Data Mining, Big Data, and Machine Learning. He holds a PhD in Computer Science from INSA Lyon (2013) and teaches at the engineering level. His current research emphasizes Dimensionality Reduction in semi-supervised learning and frugal/embedded AI applications. Education: PhD in Computer Science (2013), INSA Lyon MSc in Knowledge and Decision (2008), INSA Lyon BSc in Software Engineering (2005), Aleppo University Research interests include semi-supervised learning, feature selection, ensemble methods, and clustering techniques. He leads the Engineering and Numerical Tools research team and contributes to the Advanced Automation: AI and Data Science group. His work addresses practical applications like patient monitoring via machine learning and battery state estimation. Publications span over 15 peer-reviewed articles in journals like Knowledge and Information Systems (KAIS) and conferences such as IEEE MMSP and ACM CIKM. He supervises PhD students like Trésor Yao Koffi and collaborates on projects involving embedded systems and healthcare tech.

Miguel Vargas MartinView profile

Professor

• Computer Security
• Cybersecurity
• Artificial Intelligence
• +4 more

Dr. Miguel Vargas Martin is a Professor of Computer Science at Ontario Tech University's Faculty of Business and Information Technology. He holds a PhD from Carleton University, a Master's from CINVESTAV del IPN, and a Bachelor's from Universidad Autónoma de Aguascalientes. His research focuses on cybersecurity, authentication systems, machine learning, and human factors in security. Dr. Martin teaches graduate courses such as Cryptography and Secure Communications, and undergraduate courses including Machine Learning and Cryptography, Malware, and Network Security. Education: PhD in Computer Science, Carleton University (2003) Master of Applied Science in Electrical Engineering, CINVESTAV del IPN (1998) Bachelor of Science in Computer Science, Universidad Autónoma de Aguascalientes (1996) Research Interests: Dr. Martin's work addresses cutting-edge topics in computer security and artificial intelligence. His recent studies include improving password security through honeyword generation using machine learning, developing differential privacy visualizations for public understanding, and creating emotion recognition models for companion robots. His cybersecurity frameworks target edge computing and IoT environments, emphasizing practical defenses against modern threats. Key Contributions: Over 25 peer-reviewed articles since 2012 span topics from brain-computer interfaces for password memorability analysis to fusion-based optimization algorithms. His work bridges theoretical advancements with real-world applications in healthcare, transportation systems, and industrial IoT security.

• Systems Biology
• Semantic Web
• Knowledge Management
• +5 more

Martin Kuiper is a Professor in Systems Biology at the Norwegian University of Science and Technology (NTNU). He transitioned from industry to academia in 2008, previously working at VIB Institute in Gent, Belgium. His research focuses on integrating biological knowledge using ontologies, semantic web technologies, and logical modeling. The Kuiper lab develops tools like BioGateway and CausalBuilder, and contributes to initiatives such as DrugLogics and GRECO/GREEKC. Education: M.Sc. (1982) and PhD (1987) in Biology from the University of Groningen, followed by postdoctoral work at Ohio State University and Utrecht University. Career highlights include roles in industry (KeyGene NV, GenScope, Aventis CropScience) before joining academia. Research interests emphasize knowledge management, bioinformatics tools, and systems biology approaches to drug development and gene regulation. Key projects include modeling drug resistance in colon cancer (COLOSYS) and advancing causal interaction standards (MI2CAST). Publications span bioinformatics, computational biology, and systems medicine, with contributions to journals like iScience , Nucleic Acids Research , and Bioinformatics . Collaborations involve interdisciplinary teams from medicine and humanities at NTNU. Lab activities include developing ontologies for gene regulation and drug target identification, with applications in cancer therapy and systems medicine. Active in open science initiatives like the Gene Regulation Knowledge Commons.

• Computer Science
• Data Mining
• Database Systems
• +7 more

Yi Chen is a Professor at the MT School of Management and Director of the Center for Big Data at New Jersey Institute of Technology (NJIT). His research focuses on database systems, information retrieval, XML processing, privacy/security, and healthcare informatics. He has led numerous NSF-funded projects, including studies on privacy interventions in online publishing ecosystems and meta-information analysis in semi-structured data. Key contributions include work on counter-ad-blocking strategies, GDPR compliance frameworks, and machine learning models for healthcare prediction. He has published over 100 articles in top venues and holds an h-index of 24 with 1,947 citations. Notable awards include recognition in the Japanese Economic Review. Research interests combine computational methods with real-world applications, particularly in privacy-preserving systems, medical data analytics, and workflow optimization. Recent projects explore the impact of transparency frameworks in digital advertising and biomarker extraction using sentiment analysis techniques. His work bridges theoretical advancements with practical solutions for industry challenges. Grants: 11 federal grants including NSF projects on workflow mining, ad-blocking dynamics, and student research initiatives. Lab: Center for Big Data at NJIT, focusing on large-scale data challenges. Publications span key areas like ad viewability prediction, XML search engines (XSeek), and healthcare forum analysis. Collaborations include work on privacy frameworks and optimizing ad auction mechanisms.

Jean VaillancourtView profile

Academic

• Probabilities
• Stochastic Equations
• Random Games
• +2 more

Jean Vaillancourt is an Affiliated Professor at the Department of Decision Sciences, HEC Montréal. He holds a PhD in Mathematics from Carleton University. His research focuses on stochastic processes, probability theory, and data mining applications in social media analysis. Key areas include martingale theory, random walks in random environments, and social network dynamics. He has contributed to methodologies for estimating multi-modal histograms and predicting network restructuring following node removals. His work integrates mathematical rigor with computational techniques, appearing in venues like Stochastic Analysis and Applications and Neural, Parallel and Scientific Computations . Active on platforms like Academia.edu and ResearchGate, his research bridges theoretical probability with practical applications in data science and network analysis. Education: PhD in Mathematics, Carleton University Research Interests: Probabilities, stochastic equations, social media analysis, data mining, and network science. His work emphasizes statistical methods for pattern extraction, centrality-based network prediction, and measure-valued processes. Articles Overview: Recent publications explore martingale convergence theories, random walk periodicity detection, and cascading removals in social networks. These studies highlight his dual focus on foundational probability theory and applied data-driven analysis. Grants & Advising: Supervision activities span the last five years, though specific grants or advisees aren't detailed here. His work on road database automation and edge detection showcases interdisciplinary impact. Labs & Teams: Collaborates across disciplines, integrating mathematical modeling with computational tools. Active in HEC's research ecosystem, contributing to formal concept analysis and SAR imagery processing initiatives.