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      Dr Kai Xu

      BEng, PhD
      ROLE: Associate Professor in Data Analytics
      SCHOOL: Science & Technology
      DEPARTMENT: Computer Science
      TELEPHONE: +44 (0)20 8411 5510
      EMAIL: K.Xu@mdx.ac.uk
      1. Biography &
        Qualifications
      2. Learning &
        Teaching Interests
      3. Research Outputs &
        Interests
      4. Funded Research &
        Knowledge Exchange Projects
      5. Engagement &
        Impact

      • Biography &
        Qualifications

        Dr Kai has 20 year experience in data science research and practice. He has a PhD in Computer Science focusing on query optimisation for large databases. Since then, he has been working on data science problems in a number of domains including intelligence analysis, cyber security, social media, autonomous driving, eScience, and bioinformatics. He believes in integrating human intelligence, such as expert knowledge, with machine intelligence, such as machine learning models, which leads to over 60 peer-reviewed publications and a few international awards. He also enjoys the practical side of data science, from contributing to open-source visualisation software to developing visual analysis software. He led and participated in over 10 data science projects, including short-term consulting projects to multi-year 100+-person international project with over £10 million budget. More details here: https://kaixu.me/

         


        Languages Spoken

        Chinese (Mandarin)

        English

      • Learning &
        Teaching Interests

        • MSc by Research in Visual Analytics (program leader)
        • MSc by Research in Data Science (program co-leader)
        • CSD2500 Web Development (module leader)
        • CSD3145 Web-based App Development (Module leader)
        • CSD4060 Visual Data Analysis (module leader)

         

      • Research Outputs &
        Interests

        Dr Kai Xu's research focuses on Visual Analytics, which integrates data visualisation with analytic techniques (such as Machine Learning and Natural Language Processing) to make sense of large and complex data (such as Big Data). By combining the strength of human cognition and computational analysis, this approach allows analysts to gain insight from Big Data faster and deeper than what was previously possible.

        Awards

        1. Project of the Year Award, Middlesex University, 2018
        2. Honourable mention for Effective Support for Analytic Sensemaking, IEEE Visual Analytics Science and Technology (VAST) Challenge 2014
        3. Middlesex University Contribution Pay Scheme reward (for outstanding performance) 2013 & 2014
        4. Subject Matter Expert’s Award, IEEE Visual Analytics Science and Technology (VAST) Challenge 2012
        5. Winner, International Graph Drawing Contest (History of the World Cup category) 2006
        6. Siemens Prize for Commercialisation Potential, University of Sydney Research Conversazione 2005

        Selected Publications

        1. J. Wenskovitch, M. Zhou, C. Collins, R. Chang, M. Dowling, A. Endert, and K. Xu, “Putting the “i” ininteraction: Interactive interfaces personalized to individuals,”IEEE Computer Graphics and Applications,vol. 40, no. 3, pp. 73–82, 2020.
        2. K. Xu, S. Salisu, P. H. Nguyen, R. Walker, B. L. W. Wong, A. Wagstaff, G. Phillips, and M. Biggs,“Timesets: Temporal sensemaking in intelligence analysis,”IEEE Computer Graphics and Applications,vol. 40, no. 3, pp. 83–93, 2020.
        3. J.-D. Fekete, T. J. Jankun-Kelly, M. Tory, and K. Xu, “Provenance analysis for sensemaking,”IEEEComputer Graphics and Applications, vol. 39, no. 6, pp. 27–29, Nov. 2019.
        4. P. H. Nguyen, K. Xu, A. Wheat, B. L. W. Wong, S. Attfield, and B. Fields, “SensePath: Understanding thesensemaking process through analytic provenance,”IEEE Transactions on Visualization and ComputerGraphics, vol. 22, no. 1, pp. 41–50, Jan. 2016.
        5. P. H. Nguyen, K. Xu, R. Walker, and B. W. Wong, “TimeSets: Timeline visualization with set relations,”Information Visualization, vol. 15, no. 3, pp. 253–269, 2016.
        6. K. Xu, S. Attfield, T. J. Jankun-Kelly, A. Wheat, P. H. Nguyen, and N. Selvarij, “Analytic provenancefor sensemaking: A research agenda”, IEEE Computer Graphics and Applications, 2015.
        7. T. J. Jankun-Kelly, T. Dwyer, D. Holten, C. Hurter, M. N ̈ollenburg, C. Weaver, and K. Xu, “Scalability considerations for multivariate graph visualization,” in Multivariate Network Visualization, ser. LectureNotes in Computer Science 8380, A. Kerren, H. C. Purchase, and M. O. Ward, Eds., Springer International Publishing, Jan. 1, 2014, pp. 207–235.
        8. R. Walker, A. Slingsby, J. Dykes, K. Xu, J. Wood, P. H. Nguyen, D. Stephens, B. L. W. Wong, and Y.Zheng, “An extensible framework for provenance in human terrain visual analytics,”IEEE Transactionson Visualization and Computer Graphics, vol. 19, no. 12, pp. 2139–2148, Dec. 2013.

        Publication Feed

        Putting the "I" in Interaction: interactive interfaces personalized to the individual

        Wenskovitch, John and Zhou, Michelle and Collins, Christopher and Chang, Remco and Dowling, Michelle and Endert, Alex and Xu, Kai (2020) Putting the "I" in Interaction: interactive interfaces personalized to the individual. IEEE Computer Graphics and Applications , 40 (3). pp. 73-82. ISSN 0272-1716

        TimeSets: temporal sensemaking in intelligence analysis

        Xu, Kai and Salisu, Saminu and Nguyen, Phong H. and Walker, Rick and Wong, B. L. William and Wagstaff, Adrian and Phillips, Graham and Biggs, Mike (2020) TimeSets: temporal sensemaking in intelligence analysis. IEEE Computer Graphics and Applications , 40 (3). pp. 83-93. ISSN 0272-1716

        Survey on the analysis of user interactions and visualization provenance

        Xu, Kai and Ottley, Alvitta and Walchshofer, Conny and Streit, Marc and Chang, Remco and Wenskovitch, John (2020) Survey on the analysis of user interactions and visualization provenance. Computer Graphics Forum , 39 (2). ISSN 0167-7055 (Accepted/In press)

        An approach to human-machine teaming in legal investigations using anchored narrative visualisation and machine learning

        Attfield, Simon and Fields, Bob and Windridge, David and Xu, Kai (2019) An approach to human-machine teaming in legal investigations using anchored narrative visualisation and machine learning. In: First International Workshop on AI and Intelligent Assistance for Legal Professionals in the Digital Workplace (LegalAIIA 2019)., 17 Jun 2019, Montreal, Canada.

        A novel approach to task abstraction to make better sense of provenance data

        Bors, Christian and Attfield, Simon and Battle, Leilani and Dowling, Michelle and Endert, Alex and Koch, Steffen and Kulyk, Olga and Laramee, Robert and Troy, Melanie and Wenskovitch, John (2019) A novel approach to task abstraction to make better sense of provenance data. In: Provenance and Logging for Sense Making (Dagstuhl Seminar 18462), 11-16 Nov 2018, Dagstuhl, Germany.

        View more publications

      • Funded Research &
        Knowledge Exchange Projects

        1. Principle investigator, £80,000, “Behaviour analytics for defence and security”, Defence Science andTechnology Laboratory (dstl), 2019
        2. Principle investigator, US$20,000, “Data Science – Combining Data Visualization with Machine Learning to Make Sense of Large Social Media Data”, Microsoft Azure Research Award, 2018-2019
        3. Co-investigator, £50,000, “The Connected Vehicles”, funded by Department for Transport and City of York Council,
        4. Co-investigator, €13,115,906, “VALCRI - Visual AnaLytics for sense-making in CRiminal Intelligence analysis”, EU FP7 project, 2014-2018 (http://valcri.org/)
        5. Principle investigator, £100,000, “TimeSets: Timeline visualisation for provenance-based big data sensemaking”, Defence Science and Technology Laboratory (dstl), 2016
        6. Co-investigator, £120,000, “Patterns of Life Visualisation Experimentation Pt. II”, funded by Defence Science and Technology Laboratory (dstl), 2014-2015
        7. Co-investigator, £195,000, “Multiple Source Information Assimilation to Support Decision Making”, Defence Science and Technology Laboratory (dstl), 2013-2015
        8. Principle investigator, £50,000, “Open-Source Big Data Insight”, Defence Science and Technology Laboratory (dstl), 2015
        9. Principle investigator, £20,000, “High-Dimensional Visualisation for Big Data”, funded by CGI Group (http://www.cgi-group.co.uk/), 2014-2015
        10. Co-investigator, £89,000, “Pattern of Life Visualisation Experimentation”, Defence Science and Technology Laboratory (dstl), 2014
        11. Co-investigator, £215,000, “Data Intensive Visual Analytics”, Engineering and Physical Sciences Research Council (ERSRC), 2012 ― 2013
        12. Co-investigator, £870,000, “UK Visual Analytics Consortium Joint Research Project”, Department of Homeland Security, US and Ministry of Defence, UK. 2011 ― 2013

      • Engagement &
        Impact

        1. PC Chair, Computer Graphics & Visual Computing Conference, 2020.
        2. Organiser of the Dagstuhl Seminar on “Provenance and Logging for Sense Making”, 2018.
        3. Member of the EPSRC Peer Review Associate College
        4. Program Committee member, The IEEE VGTC Eurographics Conference on Visualisation (Eurovis) 2015, 2017
        5. Co-chair, “Provenance for Sensemaking” Workshop at IEEE VIS 2014, November 2014, Paris, France.
        6. Co-chair, “International Workshop on Visualizations and User Interfaces for Knowledge Engineering and Linked Data Analytics” (VISUAL2014), November 2014, Linköping, Sweden.

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