Research Output per year
2015 PhD NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore
2011 BEng Aerospace Engineering (First Class Honours), Nanyang Technological University, Singapore
Professional Employment History:
2017 - present - Abertay University, Lecturer in Data Science and Macmillan Professional
2015 - 2016 - Airbus Group Innovations, United Kingdom, Advanced Laminar Flow Modelling and Control Consultant
2010 - Institute of Microelectronics, Agency for Science, Technology and Research (A*STAR), Singapore, Intern
I am an aerospace engineer-scientist by training. After completing a PhD at the National University of Singapore, I worked for the multinational aerospace company Airbus in the area of computational fluid dynamics, developing advanced technologies for laminar-turbulent transition control, including a collaboration with the joint European Union-Russia BUTERFLI project. I recently made a career pivot towards data science and machine learning, leading development of the computational models at Abertay University that allow optimisation and predictive analytics of cancer patient service provision in light of future scenarios. This digital tool development is a partnership with Macmillan Cancer Support and the Digital Health and Care Institute, who facilitate a co-design process in dialogue with the cancer community in the United Kingdom.
Co-principal investigator on grants totalling £227,000 from funders including Macmillan Cancer Support and the Digital Health & Care Institute.
MAT 501 Applied Mathematics and Artificial Intelligence - Leading delivery of a Masters-level module introducing students to Artficial Intelligence and Mathematics with special emphasis on computer graphics applications.
GRS 501 Research Methods - Equipping postgraduate students with the skills in research methods and statistics needed to conduct original investigations in new fields.
My research interests lie in designing and applying artificial intelligence and machine learning methods to new problem domains. This approach was first successfully performed on wavepackets in fluid dynamics, producing results that agree well with classical theories even though the algorithms are blind to the underlying physics, while simultaneously suggesting improvements to existing models of wave interactions in the boundary layer flow. In parallel with these cutting-edge data analysis tools, I also developed the computational models that generate the raw data for subsequent analytics applications. During my PhD, this involved direct numerical simulations (DNS) of the notoriously challenging partial differential equations known as the Navier-Stokes equations. At Airbus, these computations took on a more practical slant due to the needs of industry, and involved accelerated solution methods such as the Reynolds-Averaged Navier Stokes (RANS) and Parabolized Stability Equations (PSE). Computational fluid dynamics models of advanced laminar-turbulent transition control systems were developed, involving plasma, heat and roughness arrays.
Key projects included collaboration with a team of eleven industrial, academic and governmental organizations in the European Union Seventh Framework Project BUTERFLI (BUffet and Transition delay control investigated with European-Russian cooperation for improved FLIght performance), cooperation with a consortium of nine companies and universities in the Innovate UK national project ALFET (Advanced Laminar Flow Enabling Technologies), and participation as an Airbus industry representative at DiPART (Distributed Partnership in Research & Technology) to network with the UK flight physics community.
Now, my computational modelling and data analytics research is directed towards improving the quality of service provision for cancer patients, working in close collaboration with the Macmillan Cancer Support charity, and the Digital Health and Care Institute, which is part of the Scottish Funding Council's Innovation Centre Programme. This is facilitated by access to detailed datasets tracking the journey of cancer patients from a social care perspective. Computational algorithms are developed for service optimisation and predictive modelling using this evidence base, and interactive visualization software is co-developed with computer games technology to provide the end user with a rich and immersive interaction experience with the model and data. Furthermore, deep neural networks are trained to use actual client demographic and holistic needs assessment (HNA) data to make intelligent referrals to relevant agencies such as hospices or government departments.
NGS Scholarship 2011 - 2015
• Full scholarship for four years of inter-disciplinary PhD study at the National University of Singapore’s Graduate School for Integrative Sciences and Engineering (NGS).
Best Teaching Award (Nomination), National University of Singapore, 2013
• Tutored undergraduates in Fluid Mechanics (Module code ME2134) for Semester 1 Academic Year 2013/14.
• Attained a student feedback score of 4.223 out of five, above the Mechanical Engineering Department average of 3.968 and the Faculty of Engineering average of 3.976.
• Conducted three classes per week, one hour each, for a total of 30 teaching hours and class size of 151 undergraduate students.
CN Yang Scholars’ Book Prize Award 2011
• Graduated with outstanding performance in the scholars’ cohort.
Dean’s List Student 2010, 2011
• Top student in Nanyang Technological University for Aerospace Engineering Year 3, 2010 and Year 4, 2011.
Nanyang Scholarship 2007 - 2011
• Full scholarship for four years of undergraduate study at Nanyang Technological University (NTU).
CN Yang Scholars Program 2007 - 2011
• Inter-disciplinary research program for top NTU students passionate about science and technology.
• Given guaranteed on-campus hostel accommodation for all four years of undergraduate study.
Combined effects of amplitude, frequency and bandwidth on wavepackets in laminar turbulent transitionKang, K. L. & Yeo, K. S., 30 Jan 2020, In : Computers and Fluids. 197, 15 p., 104358.
Research output: Contribution to journal › Article
Research output: Contribution to journal › Special issue
Research output: Contribution to conference › Abstract
Research output: Non-textual form › Software
Unsupervised machine learning of integrated health and social care data from the Macmillan Improving the Cancer Journey service in GlasgowKang, K. L., Greer, M., Bown, J., Preston, J., Mabelis, J., Hepburn, L-A., Fisher, M., Falconer, R. E., McDermott, S. & Deed, S., 8 Nov 2018, In : British Journal of Cancer. 119, 1 p., 4.
Research output: Contribution to journal › Meeting Abstract