About me
I am a doctoral student at the Department of Mathematical Sciences, Chalmers University of Technology and University of Gothenburg, with my advisor Rebecka Jörnsten.
Currently, I am working on data integration methods through joint matrix factorisation, with emphasis on flexible, scalable and interpretable methods. In addition, I started to work on clustering methods in a biological context. Previously, I worked with dynamical models of pharmacological process and parameter estimation in both frequentist and Bayesian settings.
Degrees
- [2018] Licentiate in Advanced Engineering Mathematics from Chalmers University of Technology with work performed at Fraunhofer Chalmers Research Centre for Industrial Mathematics
- Thesis: Modelling of drug-effect on time-varying biomarkers (pdf)
- [2016] Master of Science in Mathematics from Chalmers University of Technology with work performed at SAFER – Vehicle and Traffic Safety Centre at Chalmers
- Thesis: Investigation of under-reporting and the consistency of injury severity classifications in Swedish police crash data compared to hospital injury data based on the Swedish Traffic Accident Data Acquisition (STRADA) (pdf)
- [2013] Bachelor of Science in Mathematics with subsidiary subject Physics from University of Würzburg, Germany
- Thesis: Introduction to the theory of Bergman spaces with emphasis on their dual spaces
Positions
- [Since 2018] Doctoral Student at the University of Gothenburg
- [2016–2018] Project employee at Fraunhofer Chalmers Research Centre for Industrial Mathematics
- [2015] Data Analyst at SAFER – Vehicle and Traffic Safety Centre at Chalmers
- [2015–2016] Consultant at Chalmers teknologkonsulter
- [2010–2011] Software engineer for the Faculty of Physics and Astronomy, University of Würzburg
Teaching
I was responsible for the lectures and course coordination for
- [2021] MSA220/MVE441 Statistical Learning for Big Data at Chalmers/University of Gothenburg (about 130 students, fully digital course)
- [2020] MSA220/MVE440 Statistical Learning for Big Data at Chalmers/University of Gothenburg (about 120 students, fully digital course due to the Covid-19 pandemic)
- [2019] MSA220/MVE440 Statistical Learning for Big Data at Chalmers/University of Gothenburg (about 90 students, Slides)
and did a lot of teaching assistance (e.g. holding exercise classes, correcting submissions) over the years since 2011. Examples of subjects I was involved in are
Mathematical Statistics, Probability Theory, Linear Algebra, and Calculus.
I supervised the following students during their master thesis projects
- [2020] Selma Tabakovic, Co-clustering of tensor data using sparse tensor factorisation
- [2020, co-supervised with Rebecka Jörnsten] Oskar Liew and Per Edvardsson, Deep learning models for data integration and surrogate models for interpretable predictions with applications in integromics and recommender system
Extra-curricular activities
Co-founder and organiser for a workshop on Modelling in Biology and Medicine
- [2020] Workshop held digitally (30+ participants, see recording on YouTube)
- [2019] Workshop held at the Wallenberg Conference Centre in Gothenburg, Sweden (60+ participants)
Member of organising committee for
- [2019] a poster conference for doctoral students at the University of Gothenburg
R package maintainer
- [Since 2020] CMF package (CRAN or GitHub). Original authors: Arto Klami and Lauri Väre
- [Since 2022] mmpca package (CRAN or GitHub). Original author: Jonatan Kallus
Other activities
- [2020-2022] Chair of the PhD Student Council at the Department of Mathematical Sciences, Chalmers University of Technology and University of Gothenburg (GU)
- [2018–2021] Member of the PhD Student Council and Research and Education Committee at the Department of Mathematical Sciences, Chalmers and GU
Publications
Peer-reviewed articles
Gustafsson J, Held F, Robinson JL, Björnson E, Jörnsten R, Nielsen J (2020) Sources of variation in cell-type RNA-Seq profiles PLoS ONE 15(9):e0239495
Held F, Hoppe E, Cvijovic M, Jirstrand M, Gabrielsson J (2019) Challenge model of TNFα turnover at varying LPS and drug provocations. J Pharmacokinet Pharmacodyn 46:223–240
Held F, Ekstrand C, Cvijovic M, Gabrielsson J, Jirstrand M (2019) Modelling of oscillatory cortisol response in horses using a Bayesian population approach for evaluation of dexamethasone suppression test protocols. J Pharmacokinet Pharmacodyn 46:75–87
Loryan L, Hoppe E, Hansen K, Held F, Kless A, Linz K, Marossek V, Nolte B, Ratcliffe P, Saunders D, Terlinden R, Wegert A, Welbers A, Will O, Hammarlund-Udenaes M (2017) Quantitative Assessment of Drug Delivery to Tissues and Association with Phospholipidosis: A Case Study with Two Structurally Related Diamines in Development. Mol Pharmaceutics 14(12):4362–4373
Talks & Posters
Invited Talks
- [March 2019] Pharmacokinetic/pharmacodynamic modelling in a Bayesian hierarchical framework. Yearly meeting of the Swedish Society for Medical Statistics, Gothenburg, Sweden
- [July 2018] Bayesian hierarchical model of oscillatory cortisol response during drug intervention. Stan Workshop on Pharmacometrics, INSERM and University Paris Diderot, Paris, France
Research Presentations
- [November 2019] Scalable integration of sparse data sources. Workshop in Data Science, Halden, Norway
Posters
- [June 2019] A challenge model of TNFα turnover with LPS provocations and drug intervention. PAGE Meeting 2019, Stockholm, Sweden
- [May 2018] Bayesian hierarchical model of oscillatory cortisol response during drug intervention. PAGE Meeting 2018, Montreux, Switzerland