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Dr Layik Hama

Dr Layik Hama

Profile

Research Fellow currently based at School of Computing, previously at Leeds Institute for Data Analytics (LIDA). I “came back” to school of Computing in 2022 and since Febryary 2023 I have been working on a NIHR funded project codenamed “DynAIRx”. When I came back to School of Computing in October 2022, the two modules I assisted teaching were Programming for Data Science and Data Science for Business modules for MSc students.

I joined LIDA in 2018 and was working with geospatial data, working on developing geospatial data science tools and the latest work was Turing Geovisualization Engine (TGVE) published in EuroVIS. 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 broader computational methods in data science in general with current focus being on data visualization.

Responsibilities

  • Developing scalable data science tools
  • Data analysis and visualisation

Research interests

I am interested data analytics and visualization. I have also been interested in software abstraction and the power of simplicity of using programming languages. My current focus is on health data and Electronic Health Records visualization with immediate (2023-2025) being on Structured Medication Reviews as part of the DynAIRx project.

<h4>Research projects</h4> <p>Any research projects I'm currently working on will be listed below. Our list of all <a href="https://eps.leeds.ac.uk/dir/research-projects">research projects</a> allows you to view and search the full list of projects in the faculty.</p>

Qualifications

  • PhD, 3D Geological Data Visualisation Techniques in field education. University of Leeds.

Student education

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.