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Hello

My name is Coilin and I was born here in Copenhagen and I'm data scientist at Banedanmark working with datamining, statistic cross-sectional analysis, business intelligence automation and machine learning.

My background

My education is applied mathematics and computer science at the Technical University of Denmark. My research interests are quite broad. A considerable part of my research has focussed on the optimal decisions based on uncertainties, e.g. optimised energy bending in the Möbius strip, uncertain investments in the financial sectors and myopic vs. life-history behavioural decision-making among pelagic fish. All my research work is based on my knowledge of programming.

I have graduated with a B.Sc. thesis in differential geometry and with a M.Sc. thesis in theoretical ecology, both from the Technical University of Denmark. This has given me a unique combination of extremely strong quantitative skills in both classical and modern mathematics. This enables me to use sophisticated mathematics to develop a complex software solution using to make realistic models which can solve the above mentioned problems.

My work at Banedanmark has focussed on the strategy, digital transformation, which means I am working a lot with the datastructures, dataminings and machine learnings. Among other things, I used a combination of SQL, Azure, R Studio to develope a relatively robust forecast model that can predict train punctuality for several years ahead. I used also Power BI to visualize all my results of business intelligence analysis, e.g. train punctuality on route map. On a daily basis, I am used to work with at least 5 years of relational/SQL data, filled about 2 millions rows per month, i.e. about 120 millions total. This means great demands on how my data computation should be designed, then it still can perform relatively quickly.

Why do I want to work with you?

I like to put my logical and analytical skills and systematic thinking into my mathemathical work. I greatly emphasise overview in my work. I like to work in agile processes. I appreciate working in a professionally creative and good datascience environment with an opportunity to achieve a professional development and to have fruitful dialogues with the scientific colleagues.

How did I get into my field of research and work?

I have always been interested in mathematics and programming since I was a child. At that time it was the only thing I wanted: to have a job with mathematics and programming. My field within "Mathematics and Technology" at Technical University of Denmark therefore perfectly reflects my choice because I was then able to use my education in a wide number of mathematical courses. A course I found especially interesting was "Introduction to mathematical biology". This led to my master project where I studied the marine ecology with an eye to the mathematical modelling of the fish migration and the mass growth of the fish population. My interest has since then broadened to include all mathematical modelling of the real world.

One of the goals I have set is to build a credible model which can forecast based on the deep learning methods - a bit like weather forecasting, but just with movements: a model which can predict (based on calculations) where the people/the animals will move in the course of, say, a week or what will happen to the animals/the people in the next 50 years, for example with calculations based on the fact that the global temperature will rise even more.

Interpreters

Since I have a hearing loss I use educated Sign Language interpreters during telephone calls, meetings, courses and conferences. They can translate directly between Danish Sign Language, English and Danish. I have the opportunity to apply to the job centre for a maximum of 20 hours of interpreting service per week if I have 37 working hours per week. That means that the job centre will bear the costs of this.