Jake Cowton

If a website doesn't use bootstrap, can you truly call it a website?

About me

Machine Learning | Big Data | Development

and many other buzz words

Currently undertaking a PhD focusing on machine learning applied to time-series data at Newcastle University with several years of experince developing in Python and many other languages.


A Smart Calendar System Using Multiple Search Techniques

Calendars are essential for professionals working in industry, government, education and many other fields, whichplay a key role in the planning and scheduling of people’s day-today events. The majority of existing calendars only provide insightand reminders into what is happening during a certain period of time, but do not offer any actual scheduling functionality that canassist users in creating events to be optimal to their preferences. The burden is on the users to work out when their events should happen, and thus it would be very beneficial to develop a tool to organise personal time to be most efficient based on given tasks, preferences, and constraints, particularly for those people who have generally very busy calendars. This paper proposes a smart calendar system capable of optimising the timing of events to address the limitations of the existing calendar systems. It operates in a tiered format using three search algorithms, namely branch and bound, Hungarian and genetic algorithms, to solve different sized problems with different complexity and features, in an effort to generate a balanced solution between time consumption and optimisation satisfaction. Promising results have shown in the experimentation in personal event planning and scheduling.
Jake Cowton, Longzhi Yang (2015). The 15th UK Workshop on Computational Intelligence. IEEE

Open data and data analysis preservation services for LHC experiments

In this paper we present newly launched services for open data and for long-term preservation and reuse of high-energy-physics data analyses based on the digital library software Invenio. We track the ”data continuum” practices through several progressive data analysis phases up to the final publication. The aim is to capture for subsequent generations all digital assets and associated knowledge inherent in the data analysis process, and to make a subset available rapidly to the public. The ultimate goal of the analysis preservation platform is to capture enough information about the processing steps in order to facilitate reproduction of an analysis even many years after its initial publication, permitting to extend the impact of preserved analyses through future revalidation and recasting services. A related ”open data” service was launched for the benefit of the general public.
Jake Cowton, Sunje Dallmeier-Tiessen, Pamfilos Fokianos, L Rueda, P Herterich, J Kunčar, T Šimko, Tim Smith (2015). Open data and data analysis preservation services for LHC experiments. In Journal of Physics: Conference Series (Vol. 664, No. 3, p. 032030). IOP Publishing.