# Teaching and supervision

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**

**Previous teaching**

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**

*A Neural Network Boosted Loss Reserving Method*; w. Willis Tower Watson.

*Forecasting sales during COVID-19 using time series models*; w. Klarna.

*Clinical dose feature extraction for prediction of dose mimicking parameters*; w. RaySearch.

*Neural network embedding of a GLM rate making model in insurance pricing*; w. If.

- 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.