Dr Layik Hama
- Position: Research Fellow
- Areas of expertise: Data analytics and visualisation; geospatial data analysis; reproducible research.
- Email: L.Hama@leeds.ac.uk
- Location: Room 11.71 LIDA, Level 11, Worsley Building
- Website: Twitter | LinkedIn | Googlescholar | ORCID
Research Fellow at Leeds Institute for Data Analytics (LIDA). I joined LIDA in 2018 and since then I have been working with geospatial data. I have been working on developing geospatial data science tools and the latest work has been on Turing Geovisualization Engine (TGVE). The TGVE is a web-based, open source, interactive visual analytics tool for geospatial data analysis. The TGVE showcases visualisation methods to explore outcomes of other Turing/UoL research projects.
My research interests focus on computational methods in data science in general. More recently (2022) I have been teaching at the School of Computing. So far, the two modules I have assisted teaching are Programming for Data Science and Data Science for Business modules for MSc students.
- Developing scalable data science tools
- Data analysis and visualisation
I am interested data analytics and visualization. I have also been interested in software abstraction and the power of simplicity of using programming languages. That is why we are also working on developing tools to be used by data scientists. My interests also stretch to health data and Electronic Health Records, too.<h4>Research projects</h4> <p>Any research projects I'm currently working on will be listed below. Our list of all <a href="https://environment.leeds.ac.uk/dir/research-projects">research projects</a> allows you to view and search the full list of projects in the faculty.</p>
- PhD, 3D Geological Data Visualisation Techniques in field education. University of Leeds.
For academic year 2022-2023, teaching Data Science for Business and Programming for Data Science MSc modules.
For academic year 2018-2019 I was supervising one MSc student in Data Science and Analytics, looking at "contributory factors" of crashes within STATS19 dataset.