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/
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.
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 (3). pp. 757-783. ISSN 0167-7055
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
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
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.
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.