Find Academic Supervisors

• Healthcare Informatics
• Artificial Intelligence
• Machine Learning
• +3 more

Maqbool Hussain is a Senior Lecturer in Computer Science at the College of Science and Engineering. His research focuses on healthcare informatics, artificial intelligence (AI) applications in medical decision-making, and the development of knowledge-based systems for aerospace and healthcare sectors. He has contributed significantly to areas such as digital twin frameworks for critical care workflows, clinical decision support systems, and semantic interoperability in healthcare. Research Interests Hussain’s work spans multiple disciplines, including: Healthcare Informatics and AI-driven medical solutions Knowledge Graphs and Large Language Models (LLMs) in healthcare and aerospace Digital twins for optimizing critical care and aerospace maintenance Data-driven clinical decision support systems Medical data security and interoperability standards Machine learning for text annotation and classification Recent Research Trends Recent publications highlight his focus on integrating advanced technologies like LLMs and digital twins into healthcare and aerospace contexts. His work addresses challenges in critical care workflow optimization, kidney disease diagnosis, and aerospace product maintenance through innovative knowledge graph approaches. He also explores the application of transformer-based models for text analysis and the secure exchange of medical data using biometric methods. Labs and Teams Hussain is affiliated with the College of Science and Engineering, contributing to interdisciplinary research teams focused on healthcare technology, aerospace systems, and AI applications. His collaborations span academic and industry partners, emphasizing practical solutions for healthcare informatics and aerospace engineering challenges.

Sandra ZillesView profile

Professor

• Computational Learning Theory
• Artificial Intelligence
• Machine Learning
• +3 more

Sandra Zilles is a Professor and Canada Research Chair (Tier 1) in Computational Learning Theory at the University of Regina's Department of Computer Science. She holds adjunct appointments at the University of Waterloo and collaborates with the Alberta Machine Intelligence Institute (Amii). Her research focuses on theoretical computer science and artificial intelligence, particularly interactive learning models, formal language theory, and heuristic search algorithms. Her research integrates computational learning theory, formal language theory, and discrete artificial intelligence structures. Key interests include: Machine teaching with limited data Learnability of pattern languages and automata Graph-theoretic approaches in AI Her work bridges theoretical frameworks with applications in medical imaging, bioinformatics, and game theory. Zilles has received numerous honors including: NSERC Canada Research Chair (Tier 1, 2022-2029) Royal Society of Canada College membership Best Paper Awards (KI 2012, ALT 2003, COLT 2002) She mentors over 50 students and postdocs through her research group. Current projects explore symbolic automata, collaborative learning, and geometric teaching models. Her lab maintains international collaborations with institutions in Germany, Canada, and the US.

Anika GroßView profile

Professor

• Database Systems
• Knowledge Graphs
• Ontologies
• +7 more

Prof. Anika Groß is a Professor for Database Systems and Programming at the Department of Computer Science and Languages at Anhalt University of Applied Sciences in Köthen, Germany. She holds a PhD from the University of Leipzig (2014) and has held roles as a PostDoc in strategic research at Daimler AG and as a research assistant at the Interdisciplinary Centre for Bioinformatics (IZBI). She is actively involved in academic governance, serving on the faculty council, board of examiners, and various sustainability and research data management committees. Her research focuses on database systems, knowledge graphs, and ontology engineering, with applications in environmental science, medicine, and material sciences. Education - PhD (Dr. rer. nat.) in Computer Science, University of Leipzig (2014) - Diploma in Bioinformatics, MLU Halle-Wittenberg (2007) - Postdoctoral research at University of Leipzig's Database Group (2008–2013) Research Interests Her work emphasizes temporal graphs, ontology evolution, and semantic data integration. She develops tools like Region Evolution eXplorer (REX) and contributes to projects such as AgriRestore (ecosystem restoration) and MeineWaldKI (AI-based forest monitoring). Her recent focus includes wearable device applications in healthcare and digital twins for lithium value chains. Recent Research Trends Recent publications highlight interdisciplinary applications of knowledge graphs in agriculture and environmental science, leveraging temporal analysis and machine learning for tasks like chemical transformation prediction and bladder monitoring. She also explores cross-lingual semantic annotations in medical forms and holistic clustering of linked data. Professional Roles - Executive Committee Member, GI Database Systems Group - Fellow, Institute for Technologies and Economics of Lithium (ITEL) - Co-Chair of multiple workshops including FGDB-Workshop @ LWDA 2018 and BigDS@BTW 2017 Labs & Collaborations She leads research teams in projects funded by DFG, European Union (EFRE), and ITEL, collaborating with organizations like Daimler AG and ANU. Her lab focuses on advancing database systems and interdisciplinary data science applications.

Laura TiptonView profile

Assistant Professor

• Microbiome Analysis
• Bacterial-Fungal Interactions
• Statistical Ecology
• +4 more

Laura Tipton is an Assistant Professor in the Department of Mathematics & Statistics at James Madison University (JMU), affiliated with the College of Science and Mathematics. Her research focuses on microbiome dynamics across diverse habitats such as human lungs, aerobiota, invertebrate guts, and fermented foods, emphasizing bacterial-fungal interactions and robust statistical methodologies. She integrates techniques from ecology, machine learning, and graph theory, with side projects in digital humanities, data visualization, and data feminism. Education: PhD in Computational Biology (2016), University of Pittsburgh/Carnegie Mellon University MS in Statistics (2011), George Washington University BA in Biostatistics (2007), University of Virginia Her research interests span microbiome analysis, microbial interactions, and interdisciplinary data science. Recent work includes studies on airborne fungal DNA dynamics, fungal contributions to neonatal lung diseases, and psychobiotic field developments. She emphasizes methodological innovation in microbiome studies and advocates for inclusive data practices. While no formal awards or grants are listed, her work reflects significant contributions to collaborative projects like the Global Spore Sampling Project and Pacific Innovations, Knowledge, and Opportunities (PIKO) Program. Laura’s advising focuses on mentoring students in interdisciplinary projects at the intersection of mathematics, biology, and computational science.

Symeon PapavassiliouView profile

Professor

• Computer Networks
• Wireless Networks
• Complex Networks
• +7 more

Symeon Papavassiliou is a Professor at the Department of Communications, Electronics and Information Systems, School of Electrical and Computer Engineering, National Technical University of Athens since 2004. Previously held positions include Associate Professor at New Jersey Institute of Technology (1996-2004) and senior researcher at AT&T Labs (1995-1999). He leads the Network Management and Optimal Design Laboratory and has served in various academic leadership roles, including Deputy Director since 2005. His research focuses on computer networks, wireless systems, and AI-driven network management with over 400 publications. Recognized with multiple best paper awards and NSF Career Award (2003). Education: B.A. Electrical Engineering, NTUA (1990) MSc & PhD Electrical Engineering, Polytechnic University, NY (1992/1996) Key Roles: Founder, New Jersey Center for Wireless Networks & Internet Security Member, EETT (National Telecommunications Commission) 2006-2009 Editorial Board Member, multiple journals Research Interests: Specializes in mobile/distributed systems optimization, wireless networks, complex systems, IoT, and AI applications in network management. Active in 6G architecture research, edge computing orchestration, and secure federated learning frameworks. Publications highlight innovations in network resource allocation, game theory models for positioning systems, and symbiotic computing continuum architectures. Recent work emphasizes resilience in critical infrastructure and smart grid optimization. Awards: Over 10 best paper awards from IEEE conferences, AT&T recognition, and Greek Excellence in Research Grant (2012). Grants: Funded by EU Framework Programs, NSF, ESA, and industry partners like Panasonic and Northrop Grumman. Leads interdisciplinary projects like HEROES (UAV-based emergency response) and NEPHELE (multi-cloud ecosystems). Active in digital twin development for cultural heritage preservation and SDG tracking via knowledge graphs.

Thilo DieingView profile

Researcher

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

Thilo Dieing is a social data scientist at the Chair of Artificial Intelligence led by Prof. Dr. Heiner Stuckenschmidt at the University of Mannheim's Mannheim Center for Data Science. His research focuses on AI-supported study orientation, including developing chatbots and machine learning models to improve student advising. He contributes to projects such as NEST-BW, aiming to reduce dropout rates by creating personalized degree recommendations via generative AI chatbots, and the TransforMA project, which produced the multilingual recipe chatbot Foodle. His work emphasizes ethical AI use and explainable methods (XAI). Thilo holds a bachelor’s degree in political science and a master’s in data science from Mannheim. His technical expertise spans NLP, large language model optimization, and classification models. Future interests include integrating speech/image recognition and combining LLMs with knowledge graphs for advanced reasoning. His research underscores the critical role of data science in analyzing student data to create tailored educational pathways, ensuring chatbots like Foodle and the NEST-BW advisor deliver safe, effective guidance. Collaboration with student teams (e.g., a six-person TransforMA group) highlights his commitment to mentoring and practical application of AI in education.

• Artificial Intelligence
• Machine Learning
• Spatial Reasoning
• +3 more

Dr. Paulo Santos is a Senior Lecturer at Flinders University's College of Science and Engineering, specializing in Artificial Intelligence with a focus on explainable AI systems. He holds a PhD from Imperial College London (2003) and has over 20 years of research experience in spatial reasoning, machine learning, and robotics. His work bridges knowledge representation with deep learning to enhance transparency in AI decision-making. Dr. Santos has led research groups in Brazil, collaborated internationally, and secured funding from organizations like the British Council and EU. His expertise spans robotics, computer vision, and cognitive science. Notable achievements include the British Computer Science Machine Intelligence Prize (2004) and the Santander Prize for Science and Innovation (2006). Research interests include reinforcement learning for autonomous underwater vehicles (AUVs), scene graph generation in computer vision, and spatial reasoning for multi-robot systems. Recent work emphasizes sim-to-real transfer learning and fault recovery in underwater robotics.

• Logic
• Automated Reasoning
• Ontology Languages
• +5 more

Ulrike Sattler is a Professor in the Department of Computer Science at the University of Manchester, where she also serves as Deputy Head of Department and Senior Mentor. Her academic journey includes a PhD from RWTH Aachen University (1998) and a Habilitation from the University of Manchester (2003). She has held roles such as Reader and Senior Lecturer at Manchester, and previously worked as a Senior Researcher at TU Dresden and RWTH Aachen. Her research focuses on logic-based knowledge representation, automated reasoning, and Description Logics, with contributions to OWL ontology languages and standardization. Notable awards include the Friedrich Wilhelm Preis (1999) for her PhD thesis. She has co-supervised over 20 PhD students, including prominent figures like Birte Glimm and Matthew Horridge. Her service contributions include co-chairing conferences (KR 2010, IJCAR 2012), editorial roles in journals like JAR and JAIR, and leadership in the W3C OWL Working Group. She teaches courses on ontology engineering and semantic web technologies, emphasizing practical applications in molecular biology and knowledge graphs.

• Recommender Systems
• Context-aware Recommendation
• Explainable AI
• +5 more

Markus Zanker is a Professor in the Department of Knowledge Engineering within the Faculty of Computer Science at Free University of Bozen-Bolzano, Italy. With an extensive publication record spanning over two decades, he has established himself as a leading expert in recommender systems research, with particular expertise in context-aware, group, and knowledge-based recommendation approaches. His work bridges theoretical foundations with practical applications across diverse domains including tourism, healthcare, and e-commerce. Zanker's research interests center on advancing the theoretical underpinnings of recommender systems while addressing practical challenges in real-world deployments. His recent work has focused on causal decision-making frameworks for recommendation, intent-aware systems, and the integration of generative AI in group recommendation scenarios. He has made significant contributions to explainable recommendation systems, medical recommendation applications, and tourism recommendation systems, demonstrating both theoretical rigor and practical impact. His recent publication portfolio reveals a strong trend toward addressing fundamental challenges in recommendation science, including the growing emphasis on causal reasoning to move beyond correlation-based approaches, the integration of generative AI capabilities for more sophisticated group decision support, and the development of frameworks for ethical and socially beneficial recommendation systems. His work increasingly bridges the gap between traditional recommendation algorithms and emerging AI paradigms while maintaining focus on real-world applicability. Zanker is actively involved in the academic community as a workshop organizer, having co-chaired the Knowledge-aware and Conversational Recommender Systems (KaRS) workshop and the Recommenders in Tourism (RecTour) workshop for multiple consecutive years at major conferences including RecSys. His research has been supported through collaborations with numerous international researchers and institutions, focusing on both theoretical advances and practical implementations of recommendation technology across multiple application domains.

Wynne ChinView profile

Professor

• Information Systems
• Structural Equation Modeling
• Technology Acceptance
• +5 more

Wynne Chin is a Professor of Decision & Information Sciences at the C.T. Bauer College of Business, University of Houston. He holds a Ph.D. from the University of Michigan, MBA from the University of Michigan, and degrees from Northwestern University and UC Berkeley. His research focuses on IT adoption, structural equation modeling (SEM), and predictive analytics. He developed the PLS-Graph software, widely used in PLS-based SEM, and has contributed to cross-cultural analysis of IT sourcing and workplace assessment. Education: Ph.D. (Computers & Information Systems, U. Michigan), M.S. (Chemical Engineering, Northwestern), MBA (U. Michigan), B.S. (Biophysics, UC Berkeley). Research interests include IT decision-making, end-user satisfaction, group process measures, and methodological innovations in SEM. He has been recognized globally, ranking 3rd in first-authored articles in MISQ/ISR. Awards include the AIS Fellow, Esther Farfel Award, and AIS Technology Legacy Award. His work bridges IS and statistics, influencing both academic and applied fields. His articles emphasize technology acceptance models, causal modeling, and PLS techniques. Grants and advising contributions are significant but not explicitly detailed here. He has held visiting roles at institutions like the Australian School of Business and is a leader in the PLS research community.

Haomiao YuView profile

Professor

• Microeconomics
• Mathematical Economics
• Game Theory
• +4 more

Haomiao Yu is a Professor in the Department of Economics at Toronto Metropolitan University, where he serves as Continuing Education Academic Coordinator and Coordinator of the Economics and Management Science Major. His research focuses on microeconomics, mathematical economics, and game theory, with a particular emphasis on large games, equilibrium theory, and strategic interactions under asymmetric information. His work explores foundational topics such as information structures, rationalizability, and the equivalence between individualized and distributionalized games. Recent publications address Pareto refinements, social norms in game theory, and equilibrium existence in non-atomic games. Selected publications highlight his contributions to understanding strategic uncertainty, equilibrium distributions, and the closed-graph property of Nash equilibrium correspondences. His teaching fields align with his research expertise, including microeconomics, game theory, and industrial organization. Contact: haomiao@torontomu.ca | Personal Website | Curriculum Vitae

Rachel MelamedView profile

Assistant Professor

• Computational Biology
• Cancer Genomics
• Data Science
• +3 more

Rachel Melamed is an Assistant Professor of Biology at the University of Massachusetts Lowell, affiliated with the Kennedy College of Sciences and the Center of Biomedical and Health Research in Data Sciences (CHORDS). Her research focuses on linking health and molecular data to understand disease mechanisms and identify interventions for conditions like cancer. She employs computational methods from computer science and epidemiology, integrating diverse datasets including health records, biobanks, and drug experiments. Education: BA in Computer Science (Brown University), PhD in Biomedical Informatics (Columbia University), Postdoctoral training in Biomedical Data Science (University of Chicago). Research Interests: Disease biology & genomics, data science, epidemiology, causal inference. Projects include studying drug impacts on cancer pathways, genetic overlaps between diseases, and leveraging big data for personalized medicine. She leads a lab with graduate and undergraduate researchers exploring computational biology, bioinformatics, and pharmacology. Lab Team: Supervises students specializing in computational biology, statistics, and machine learning. Current members include Anugrah Vaishnav, Jianfeng Ke, Panagiotis Lalagkas, and others. Past researchers include Aylinh Eng and Bishoy Sargius. Key Focus Areas: Cancer etiology, drug repurposing, genetic risk stratification, and translational bioinformatics. Her work bridges computational methods with clinical and biological datasets to uncover actionable insights in disease prevention and treatment.

• Artificial Intelligence
• Data Science
• Knowledge Graphs
• +4 more

Yang Lei is an academic affiliated with the University of Melbourne's Department of Computing and Information Systems. Their research focuses on knowledge graphs, FAIR data principles, large language models (LLMs), and ontology engineering. They contribute to initiatives like the Open Research Knowledge Graph (ORKG) and NFDI4DataScience, emphasizing reproducibility, scholarly metadata, and systematic literature reviews. Recent work includes applications of LLMs for abstract summarization, leaderboard extraction, and entity recognition in scholarly documents. Yang also explores challenges in FAIR Digital Objects, machine-actionable workflows, and interoperability in research data management.

Christian MuiseView profile

Assistant Professor

• Artificial Intelligence
• Automated Planning
• Machine Learning
• +6 more

Christian Muise is an Assistant Professor at Queen's University's School of Computing, part of the Faculty of Arts and Science. He holds a PhD (2014) in Artificial Intelligence from the University of Toronto, where he was advised by Sheila McIlraith and J. Christopher Beck. His research focuses on automated planning under uncertainty, combining planning with learning for applications like goal-oriented dialogue systems and multi-agent coordination. He previously held postdoctoral roles at the University of Melbourne's Agentlab and MIT's CSAIL, and was a Research Staff Member at the MIT-IBM Watson AI Lab. Education: PhD in Artificial Intelligence, University of Toronto (2014) MSc in Computer Science, University of Toronto (2009) BSc in Computer Science, Carleton University (2007) Research Interests: His work bridges automated planning and machine learning, emphasizing robustness in uncertain environments. Key areas include non-deterministic planning, model acquisition with large language models (LLMs), and human-aware planning. He explores applications in healthcare (e.g., treatment response prediction), robotics (autonomous navigation), and dialogue systems for safety-critical domains. Current projects include developing explainable planning systems and mitigating bias in AI decision-making. Awards: Scotiabank Scholar (Scotiabank Centre for Customer Analytics) Advising & Labs: Leads the Mu Lab, supervising PhD and Master's students in topics like model acquisition, dialogue systems, and planning bias. Active in open-source tools (e.g., L2P, MACQ library) to democratize planning research. Collaborates on projects like PRP Rebooted and FixMyPlan to advance FOND planning and LLM integration. Labs/Teams: Mu Lab at Queen’s University, focusing on planning under uncertainty, AI safety, and neuro-symbolic systems.

• High-Performance Computing
• Distributed Systems
• Graph Algorithms
• +3 more

Tahsin Reza is an Assistant Professor at the University of Waterloo, affiliated with the Faculty as a full-time member. His research focuses on high-performance computing, distributed systems, and large-scale graph processing. His work emphasizes algorithmic optimization for irregular parallelism, distributed approximation algorithms, and efficient handling of massive graphs with billions of edges. Key research interests include developing frameworks like YGM for HPC, HyGN for NUMA architectures, and tools such as PruneJuice for graph pruning. His contributions span graph algorithms for Steiner trees, temporal graphs, and metadata-driven pattern matching. He has extensively explored GPU and hybrid CPU-GPU systems to accelerate graph processing tasks in domains like InSAR data analysis and VANET tracking. No scientific awards or grants are explicitly mentioned in the provided materials. His work has been published in top venues, consistently addressing challenges in scalability, efficiency, and real-world applicability of graph-based solutions.