{"id":362,"date":"2023-02-27T14:13:14","date_gmt":"2023-02-27T14:13:14","guid":{"rendered":"https:\/\/lassonde.yorku.ca\/ical\/?page_id=362"},"modified":"2024-12-16T01:12:24","modified_gmt":"2024-12-16T01:12:24","slug":"manos-papagelis","status":"publish","type":"page","link":"https:\/\/lassonde.yorku.ca\/ical\/manos-papagelis\/","title":{"rendered":"Manos Papagelis"},"content":{"rendered":"\n
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Manos Papagelis<\/h3>\n\n\n\n

Associate Professor, EECS Department
General Member, IC@L
Lassonde School of Engineering, York University Member, Graduate Program in Electrical Engineering & Computer Science, York University Member\/PI, Data Mining Lab, EECS Department, York University
Member\/Co-PI, Dependable Internet of Things Applications (NSERC CREATE DITA) Member\/Co-PI, Center for Innovation in Computing @ Lassonde (IC@L)
Member\/Co-PI, Center for AI and Society (CAIS) Member\/Co-PI, The Mobility Innovation Center (MOVE) Member\/Co-PI, Connected Minds (CM)
Member, AI at York University (AI@YorkU)

Website <\/a>| Email<\/a><\/p>\n<\/div>\n\n\n\n

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2023 – 2024 Research Highlights<\/h3>\n\n\n\n

Data Mining (Graph Mining \/ Trajectory Data Mining \/ NLP \/ Machine Learning)<\/strong><\/h3>\n\n\n\n

Our research interests include data mining, graph mining, large-scale network analysis, information networks, big data systems and knowledge discovery from data. The emphasis of our research is on theoretical foundations, novel models and algorithms that can provide fast and accurate solutions to complex computational problems and can support data-driven decision making in domains as broad as health, transportation, communication, and engineering. The current research focus is on two areas:<\/p>\n\n\n\n

    \n
  • Trajectory Data Mining<\/em><\/strong>: The primary focus of this research is on discovery of network patterns and dynamics through mining trajectory data streams. This describes a special type of trajectory mining task that seeks to efficiently discover pair-wise relationships among moving objects over time. Mining trajectory data streams to find interesting network patterns is of increased research interest due to a broad range of useful applications, including analysis of transportation systems and location-based social networks.<\/li>\n\n\n\n
  • Network Representation Learning<\/em><\/strong>: Large-scale network mining and analysis is key to revealing the underlying dynamics of networks, not easily observable. The primary focus of this research is on designing efficient representation learning methods for dynamic networks. Representing networks into low-dimensional spaces occurs in an agnostic way (without domain-expertise) and has the potential to improve the performance of many data mining tasks that now need to operate in lower dimensions. Network mining can support a variety of applications in diverse disciplines and has the potential to impact different industries.<\/li>\n\n\n\n
  • Natural language Processing (NLP)<\/em><\/strong>: Our research work in this area focuses on sentiment analysis, which aims to extract subjective information about the polarity of a set of documents, such as online reviews. Our research focus on emotion detection that can inform affective tasks and includes the following.<\/li>\n<\/ul>\n\n\n\n

    Research Interests<\/h3>\n\n\n\n
      \n
    • Data Mining<\/li>\n\n\n\n
    • Graph Mining<\/li>\n\n\n\n
    • Machine Learning<\/li>\n\n\n\n
    • Big Data Analytics<\/li>\n\n\n\n
    • Mobility Analytics<\/li>\n\n\n\n
    • Knowledge Discovery<\/li>\n<\/ul>\n\n\n\n

      Awards<\/h3>\n\n\n\n
        \n
      • 31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2023) \u2013 Best Paper Runner Up Award<\/li>\n<\/ul>\n\n\n\n

        Selected Publications<\/strong><\/h3>\n\n\n\n
          \n
        • Survey on Disease Outbreak Detection Methods Utilizing Diverse Data Sources<\/strong>. G. Babanejaddehaki, G., A. An, & M. Papagelis. ACM Transactions on Computing for Healthcare (ACM HEALTH), 2023. Major revisions requested.<\/li>\n\n\n\n
        • PathletRL: Trajectory Pathlet Dictionary Construction Using Reinforcement Learning<\/strong>. G. Alix, & M. Papagelis. In Proceedings of the 31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. In Press (ACM SIGSPATIAL 2023). (Best paper runner up award).<\/li>\n\n\n\n
        • Point2Hex: Higher-Order Mobility Flow Data and Resources<\/strong>. A. Faraji, J. Li, G. Alix, M. Alsaeed, N. Yanin, A. Nadiri, & M. Papagelis. In Proceedings of the 31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. In Press (ACM SIGSPATIAL 2023).<\/li>\n\n\n\n
        • The Role of Preprocessing for Word Representation Learning in Affective Tasks<\/strong>. N. Babanejad, H. Davoudi, A. Agrawal, A. An, & M. Papagelis. IEEE Transactions on Affective Computing (IEEE TAC), 2023.<\/li>\n\n\n\n
        • Trajectory-User Linking Using Higher-Order Mobility Flow Representations<\/strong>. M. Alsaeed, A. Ameeta, & M. Papagelis. In Proceedings of the 24th IEEE International Conference on Mobile Data Management (IEEE MDM 2023), pp. 158-167.<\/li>\n<\/ul>\n<\/div>\n<\/div>\n\n\n\n
          <\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

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