Dr Mikkel Bue Lykkegaard
Job: Postdoctoral Research Fellow / Lead Data Scientist
Employer: University of Exeter / digiLab
PhD Thesis: Multilevel Delayed Acceptance MCMC with Applications to Hydrogeological Inverse Problems
PhD Supervisors: Prof Timothy Dodwell and Dr David Moxey
Mikkel currently works as a Postdoctoral Research Fellow at the University of Exeter and as Lead Data Scientist for digiLab.
Mikkel was a PhD student within the WISE Centre for Doctoral Training between 2018-2022 and was based at the University of Exeter. His research project was concerned with hydrogeological inversion – specifically using cutting-edge Markov Chain Monte Carlo (MCMC) techniques for groundwater flow parameter estimation and uncertainty quantification. He is particularly interested in the use of surrogate models in multi-level MCMC model hierarchies, and is currently exploring various Machine Learning techniques for ultra-fast approximation of model response.
Mikkel’s research has applications in both groundwater abstraction and remediation – improved estimates of groundwater flow patterns can improve decision support systems, allowing groundwater abstraction companies to make better sustainable yield estimates, and remediation companies to design taylor-made remediation campaigns.
Mikkel successfully defended his PhD thesis on “Multilevel Delayed Acceptance MCMC with Applications to Hydrogeological Inverse Problems” in May 2022.
Mikkel’s earlier academic background is a BEng degree in Arctic Technology (Civil and Environmental Engineering) from the Technical University of Denmark (DTU) and an MSc degree in Environmental Science from University of Aberdeen. During and after his undergraduate degree, Mikkel worked as a Construction Technician at Istak Hf on the simultaneous construction of five hydropower plants in a remote mountain valley in Norway. Mikkel has also worked as a Substitute Teacher, Bartender, Yoga-Teacher and Kindergarten Assistant in Denmark prior to starting his academic studies.
- Environmental (geo-)hydrology and hydroinformatics
- Uncertainty quantification and model sensitivity analysis
- Distributed environmental models and surrogate models
- Environmental fate and risk assessment of pollutants