جستجوی استاد راهنمای دانشگاهی

Symeon Papavassiliouمشاهده پروفایل

استاد

• Computer Networks
• Wireless Networks
• Complex Networks
• +۷ مورد دیگر

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 Dieingمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

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
• +۳ مورد دیگر

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
• +۵ مورد دیگر

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
• +۵ مورد دیگر

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 Chinمشاهده پروفایل

استاد

• Information Systems
• Structural Equation Modeling
• Technology Acceptance
• +۵ مورد دیگر

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 Yuمشاهده پروفایل

استاد

• Microeconomics
• Mathematical Economics
• Game Theory
• +۴ مورد دیگر

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 Melamedمشاهده پروفایل

استادیار

• Computational Biology
• Cancer Genomics
• Data Science
• +۳ مورد دیگر

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
• +۴ مورد دیگر

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 Muiseمشاهده پروفایل

استادیار

• Artificial Intelligence
• Automated Planning
• Machine Learning
• +۶ مورد دیگر

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
• +۳ مورد دیگر

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.

Moharram Challengerمشاهده پروفایل

استادیار

• Cyber-physical Systems
• Multi-agent Systems
• Domain-specific Modeling Languages
• +۵ مورد دیگر

Moharram Challenger is a tenure-track Assistant Professor in the Department of Computer Science at the University of Antwerp's Faculty of Sciences. Previously, he served as an assistant professor at Ege University (2017-2018) and as a post-doctoral researcher at the University of Antwerp (2019-2020) working on Flanders Make projects PACo and DTDesign. His academic journey includes R&D leadership roles at UNIT IT Ltd. (2012-2016), post-doctoral research at Wageningen University (2016-2017), and tenure-track faculty positions at IAU-Shabestar University (2005-2009). His research spans Cyber-physical Systems , Multi-agent Systems , and Domain-specific Modeling Languages , with recent publications focusing on quantum machine learning, digital twinning, and IoT optimization. Key projects include ITEA ModelWriter, ITEA Assume, and Flanders Make initiatives. His work demonstrates strong integration of model-driven engineering with emerging technologies like quantum computing and reinforcement learning. Challenger actively contributes to the academic community as a member of IEEE and ACM . His publication record shows consistent output across top venues, with 2025 featuring significant work in quantum-enhanced learning and CPS security. Current research emphasizes practical applications in drone energy modeling, medical diagnostics, and industrial IoT systems. His advising activities focus on cyber-physical systems and agent-based modeling, supported by grants from TUBITAK and Flanders Innovation & Entrepreneurship. Key collaborations include European ITEA projects and partnerships with industrial entities through UNIT IT Ltd. Challenger maintains active development through GitHub repositories related to code refactoring, model-driven engineering, and legacy system modernization, reflecting his commitment to practical software engineering solutions.

Elena Montiel Ponsodaمشاهده پروفایل

دانشیار

• Natural Language Processing
• Semantic Web
• Ontology Engineering
• +۴ مورد دیگر

Elena Montiel Ponsoda is an Associate Professor in the Department of Applied Linguistics at the Faculty of Computer Science, Universidad Politécnica de Madrid (UPM), and a member of the Ontology Engineering Group since 2006. Her interdisciplinary expertise bridges linguistic sciences with computer science, focusing on multilingual aspects of knowledge representation. Education: PhD in Applied Linguistics (2011) from Universidad Politécnica de Madrid M.A. in Conference Interpreting and Translation (2000) from Universidad de Alicante B.A. in Technical Interpreting (2003) from Hochschule Magdeburg-Stendal, Germany Her research centers on the integration of linguistic resources with Semantic Web technologies, specializing in ontology localization, lexicalization, and multilingual terminology representation. She has pioneered approaches to connect translation studies with knowledge engineering, developing frameworks for linguistic linked data and semantic interoperability across languages. Recent publications reveal a strategic shift toward legal domain applications, with significant contributions to the LYNX project's knowledge graph for multilingual legal compliance. Her work demonstrates consistent innovation in representing terminological resources using OntoLex-lemon, with increasing focus on practical implementations in legal informatics and labor market analysis. As an active mentor, she has guided numerous graduate students through thesis research while participating in major international projects including NeOn, ICPS, Monnet, BabeLData, and LIDER. Her leadership extends to organizing key workshops on multilingual semantic web technologies and serving on program committees for top-tier conferences. Montiel Ponsoda maintains influential roles in W3C Community Groups (Ontolex, BMLOD, ld4lt), helping shape international standards for multilingual linked data and language technologies on the Web.

Javier Osorioمشاهده پروفایل

استادیار

• Political and Criminal Violence
• Comparative Politics
• Natural Language Processing
• +۳ مورد دیگر

Javier Osorio is an Assistant Professor at the University of Arizona's School of Government and Public Policy. His research focuses on the micro-foundations and dynamics of political and criminal violence in Latin America, employing quantitative methods such as natural language processing, GIS, and big data analytics. He has received awards from the UNODC and MPSA, and his work appears in leading journals like the Journal of Peace Research . Dr. Osorio holds a Ph.D. in Political Science from the University of Notre Dame (2013) and previously taught at John Jay College of Criminal Justice. He leads the Academy for Security Analysis, funded by USAID, and collaborates with the NSF and DoD’s Minerva Initiative. His research projects include supervised event coding for conflict analysis and randomized controlled trials in Central America. His key contributions span conflict language models (e.g., ConfliBERT), criminal violence classification, and multilingual event coding frameworks. He has advised interventions in El Salvador and Nicaragua, emphasizing data-driven approaches to security challenges.

Fred Popowichمشاهده پروفایل

استاد

• Natural Language Processing
• Artificial Intelligence
• Big Data
• +۴ مورد دیگر

Fred Popowich is a Professor in the School of Computing Science at Simon Fraser University (SFU), Canada. He holds adjunct positions at Dalhousie University's Faculty of Graduate Studies and is an Associate Member of SFU's Department of Linguistics and Cognitive Science Program. His academic career began post-PhD (Cognitive Science/Artificial Intelligence, University of Edinburgh, 1989) and has spanned over three decades at SFU. Education: PhD in Cognitive Science/Artificial Intelligence (University of Edinburgh, 1989); M.Sc. and B.Sc. in Computing Science (Simon Fraser University and University of Alberta, 1985/1982). Research focuses on natural language processing (NLP), machine translation, intelligent systems, and big data applications. He directs SFU’s Big Data Initiative and leads the Natural Language Laboratory, supervising MSc/PhD students in computing science. His work includes developing systems for smart homes, toxic language detection in social media, and energy grid analysis. Industry roles include co-founding Axonwave Software (as CTO/President) and contributing to technology commercialization. Current projects address EV charging impacts, personalized learning systems, and real-time load monitoring. Publications span machine translation, sentiment analysis, and NLP applications in education and energy systems. His work bridges theoretical computer science with practical applications in healthcare, smart cities, and education.