Menu

UCL Department of Geography

Home

Description Photo Here

Personal tools
Log in
This is SunRain Plone Theme
UCL Home  /  Geography  /  People  /  Research Students  /  Markus Löning

Markus Löning

PhD Student at UCL and Enrichment Student at The Alan Turing Institute

 

Email: markus.loning.17@ucl.ac.uk

GitHub: https://github.com/mloning

LinkedIn: https://www.linkedin.com/in/mloning/

 

Biography


Markus is a PhD Student at UCL and an Enrichment Student at The Alan Turing Institute. He is supervised by Prof Paul Longley, Dr James Cheshire and Dr Franz Király. Prior to his PhD, he worked as a data science intern at BMW for six month. Before that, he graduated at the top of his class from his Master’s degree in Philosophy & Economics at Bayreuth University (Germany). Since the start of his Master’s studies he has been deeply interested in empirical research and statistical methods. He completed extra online courses on machine learning, attended a data science summer school at the LSE, studied at the Universidad Complutense de Madrid during an exchange semester, and took additional modules from his university’s computer science department. His dissertation research on empirical policy analysis led to a presentation at an academic conference and peer-reviewed journal publication in conjunction with his supervisors. He also holds a first-class BA Hons in Philosophy, Politics & Economics from Lancaster University.

 

Research


Markus is interested in supervised learning with time-series/panel data, i.e. observations on multiple independent individuals (e.g. customers, patients or machines) collected repeatedly over time. His goals are to:

  1. Create a practical, consistent and statistically solid workflow for modelling and evaluating supervised learning strategies with time-series/panel data,
  2. Design and implement an open-source Python toolbox that allows to put the workflow into practice,
  3. Develop probabilistic supervised learning methods based on point process models for panel data containing sequences of events with exact timestamps rather than regular time-series,
  4. Apply workflow to real-world datasets through ongoing industry collaborations, including the loyalty-card data from a UK high-street retailer (via the Consumer Data Research Centre), the fitness-training data from a supplier of cloud-connected gym equipment, and the biochemical process data from a pharmaceutical company.

He is inspired by software development projects like scikit-learn, a popular machine learning toolbox in Python which not only makes supervised learning methods widely available but also easily understandable through an intuitive and consistent API design.