I am currently supervising the following PhD students and postdocs:

Viktor Nilsson (PhD); started Aug. 2020.
Federica Milinanni (PhD); started Aug. 2020.
Guo-Jhen Wu (postdoc, w. Henrik Hult); Aug 2019--

I am also assistant supervisor of
Tianfang Zhang (industrial PhD student at Raysearch, main supervisor Jimmy Olsson); Feb 2020--

Previous students and postdocs:
Carl Ringqvist (PhD, second supervisor, main supervisor Henrik Hult); Aug 2015--June 2021.

For students at the B.Sc. and M.Sc. levels, I am always interested in supervising projects in the (broad) areas of probability and analysis; see my student projects page for more information.

Current and upcoming teaching

SF1922 - Probability Theory and Statistics for Engineering Physics (Spring 2022)

Previous teaching

The following are previous courses I have taught post graduate studies; unless otherwise stated the course was given at KTH.

2021/20222
SF2935 - Modern methods of statistical learning (Fall 2021)
Link to Canvas page: SF2935.
2020/2021
SF2935 - Modern methods of statistical learning (Fall 2020)
FSF3950 - Classical papers in applied mathematics (Spring 2021) (graduate course, w. Anders Szepessy)
2019/2020
SF2935 - Modern methods of statistical learning (Fall 2019)
SF2943 - Time series analysis (Spring 2020)

2018/2019
2DBN10 - Advanced calculus (Fall 2018; TU Eindhoven)
SF3961 - Statistical inference (Graduate course, joint with H. Hult; spring 2019)
SF2943 - Time series analysis (Spring 2019)

2017/2018
2WA30 - Analysis 1 (Fall 2017; TU Eindhoven)
2DBN00 - Linear Algebra (Spring 2018; TU Eindhoven)

2016/2017
SF2942 - Portfolio theory and risk management (Fall 2016)
SF2935 - Modern Methods of Statistical Learning Theory (Fall 2016, co-lecturer)
SF1901 - Introduction to probability and mathematical statistics (Eng. Physics) (Spring 2017)
SF2943 - Time series analysis (Spring 2017)

2015/2016
APMA 1710 - Information theory (Fall 2015; Brown University)

Student theses

During fall 2020 I am supervising the following students/projects:

  • Julia Li - A Neural Network Boosted Loss Reserving Method ; w. Willis Tower Watson.
  • Christina Ghawi - Forecasting sales during COVID-19 using time series models ; w. Klarna.
  • Anton Finnson - Clinical dose feature extraction for prediction of dose mimicking parameters ; w. RaySearch.
  • Axel Gustafsson and Jacob Hansén - Neural network embedding of a GLM rate making model in insurance pricing ; w. If.
    In the past I have supervised the following M.Sc. theses:

    • Agnes Hansson - Understanding people movement and detecting anomalies using probabilistic generative models ; w. Assa Abloy.
    • André Gerbaulet and Patrik Amethier - Sales Volume Forecasting of Ericsson Radio Units - A Statistical Learning Approach ; w. Ericsson.
    • Sofia Larsson — A Study of the Loss Landscape and Metastability in Graph Convolutional Neural Networks (KTH, 2020); w. Modulai.
    • Anton Karlsson and Torbjörn Sjöberg - Preserving Inter-variable Dependencies in Tabular Data generated by Generative Adversarial Networks (KTH, 2020); w. Swedbank.
    • Titing Cui - Short term traffic speed prediction on a large road network (KTH, 2019)
    • Kristofer Engman - Bidding models for bond market auctions (KTH, 2019); w. SEB.
    • Alva Engström and Filippa Frithz - Measuring the impact of strategic and tactic allocation for managed futures portfolios (KTH, 2019); w. Lynx.
    • Sean Belfrage and Adrian Ahmadi - Forecasting non-maturing liabilities (KTH, 2016); w. Carnegie Bank.

    At the bachelor's level I have supervised 8 theses in applied mathematics at KTH and one in stochatics at TU/e (joint with Remco van der Hofstad); see my CV for more details.

    Prospective students

    I am always interested in supervising student theses in the (broad) areas of probability and analysis; see my student projects page for more information. If you think you might want to write your thesis with me (and possibly some additional co-supervisor), feel free to contact me and we can have an informal chat about potential topics.