I am a Teaching Fellow in Statistics at  University of Edinburgh. I support the running of statistics courses across undergraduate degrees and currently developing learning materials in statistics using R that can be used by wider social science communities.

My PhD research was concerned with  big data analysis in application to social science research questions.  In my thesis  I analysed data recorded by smart meters to study energy users behavioural patterns  as well as I address the challenges presented in these kind of complex time series data structures. The motivation behind my work was to explore whether there is a potential for big data to inform public policy and decision making in energy sector. I used computational statistics methods to answer these questions.

In the past I worked as  Teaching Fellow in Quantitative Methods at UCL School of Public Policy and as a Research Assistant in London School of Economics (LSE).

My research and teaching interests are spanned broadly across the area of quantitative methods in interdisciplinary settings:   social sciences, data science and energy research, I am keen to explore new forms of data and novel methods and technology that can be used to analyse it and  passionate about both development and application of statistical methods as well as promoting those to new generation of interdisciplinary researchers through active involvement in teaching and training activities.

I am currently looking for a job opportunity – either in industry or academic research setting, working with complex datasets 🙂



Scotland Data Science Meet Up, 24th of October


Future of Utilities: Smart Energy Conference, 17th of October – slides

R Ladies Edinburgh Workshop, 29th of May – slides


Blog Post for ‘Teaching Matters’ University of Edinburgh

Interdisciplinarity in the age of data: Teaching data analysis and statistics in the social sciences 

Seminar at UCL Energy Institute: 

UCL Energy Institute Seminar Series, 2018 Video

I also have a writer page where I post a bit of fiction.