CV Johan Larsson

14 November 2024

 Lund, Sweden

Professional Experience

2024–present

Thiele Postdoctoral Researcher, Department of Mathematical Sciences, Copenhagen University

2018–2024

PhD Student, Department of Statistics, Lund University

Education

2018–2024

PhD in statistics, Department of Statistics, Lund University

Title: “Optimization and Algorithms in Sparse Regression”

Supervisors: Jonas Wallin and Malgorzata Bogdan

2016–2018

Bachelor’s degree in statistics, Department of Statistics, Lund University

Title: “eulerr: Area-Propotional Euler Diagrams with Ellipses”

Supervisor: Peter Gustafsson

Publications

Conference and Journal Articles

Larsson, J., Klopfenstein, Q., Massias, M., & Wallin, J. (2023). Coordinate descent for SLOPE. In F. Ruiz, J. Dy, & J.-W. van de Meent (Eds.), Proceedings of the 26th international conference on artificial intelligence and statistics (Vol. 206, pp. 4802–4821). PMLR.
Larsson, J., & Wallin, J. (2022). The Hessian screening rule. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, & A. Oh (Eds.), Advances in neural information processing systems 35 (Vol. 35, pp. 15823–15835). Curran Associates, Inc.
Moreau, T., Massias, M., Gramfort, A., Ablin, P., Bannier, P.-A., Charlier, B., Dagréou, M., Tour, T. D. la, Durif, G., Dantas, C. F., Klopfenstein, Q., Larsson, J., Lai, E., Lefort, T., Malézieux, B., Moufad, B., Nguyen, B. T., Rakotomamonjy, A., Ramzi, Z., … Vaiter, S. (2022). Benchopt: Reproducible, efficient and collaborative optimization benchmarks. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, & A. Oh (Eds.), Advances in neural information processing systems 35 (Vol. 35, pp. 25404–25421). Curran Associates, Inc.
Larsson, J. (2021). Look-ahead screening rules for the lasso. In A. Makridis, F. S. Milienos, P. Papastamoulis, C. Parpoula, & A. Rakitzis (Eds.), 22nd European young statisticians meeting - proceedings (pp. 61–65). Panteion university of social and political sciences.
Larsson, J., Bogdan, M., & Wallin, J. (2020). The strong screening rule for SLOPE. In H. Larochelle, M. Ranzato, R. Hadsell, M.-F. Balcan, & H.-T. Lin (Eds.), Advances in neural information processing systems 33 (Vol. 33, pp. 14592–14603). Curran Associates, Inc.
Larsson, J., & Gustafsson, P. (2018). A case study in fitting area-proportional Euler diagrams with ellipses using eulerr. Proceedings of International Workshop on Set Visualization and Reasoning, 2116, 84–91.

Theses

Larsson, J. (2024). Optimization and algorithms in sparse regression: Screening rules, coordinate descent, and normalization [PhD thesis, Department of Statistics, Lund University].
Larsson, J. (2018). Eulerr: Area-proportional Euler diagrams with ellipses [Bachelor’s thesis, Department of Statistics, Lund University].

Invited Talks

Nov 13, 2024

The Choice of Normalization Directly Affects Feature Selection in Regularized Regression

Danish Statistical Society (DSTS) Fall Meeting

Apr 10, 2024

Normalization for class-imbalanced binary features in regularized regression

Uppsala Statistics Seminar Series

Oct 27, 2021

Designing a Distance-Based Course in Data Visualization: Lessons Learned

Cramér Society Fall Meeting

Nov 16, 2021

The Hessian Screening Rule

Seminar at the Faculty of Pure and Applied Mathematics, Wrocław University of Science and Technology

Sep 21, 2021

The Hessian Screening Rule and Adaptive Lasso Paths

Statistics Seminar at the Department of Mathematical Statistics, Chalmers/Gothenburg University

Sep 9, 2021

Look-Ahead Screening Rules for the Lasso

EYSM 2021

May 8, 2020

The Strong Screening Rule for SLOPE

Statistical Learning Seminar

Jun 18, 2018

A Case Study in Fitting Area-Proportional Euler Diagrams with Ellipses Using eulerr

SetVR 2018

Teaching

Course Development

2020–2023

Data Visualization, Department of Statistics, Lund University

Instructor

2020–2023

Data Visualization, Department of Statistics, Lund University

2019–2021

Statistics: Basic Course, Department of Statistics, Lund University

Teaching Assistant

2021

Introduction to R Programming, Department of Statistics, Lund University.

2019–2021

Statistics for Fire Engineers, Department of Statistics, Lund University.

2019–2020

Data Mining and Visualization, Department of Statistics, Lund University.

2019

Artificial Intelligence and Deep Learning, Department of Statistics, Lund University.

Supervision

2023

Bachelor’s Thesis, Department of Statistics, Lund University.

Student: Elias Gullberg

Title: Weather Factors and E. coli Concentration in Barnviken, Malmö – A Linear Mixed Model Approach

2023

Bachelor’s Thesis, Department of Statistics, Lund University.

Students: Marcus Stolz, Jonathan Christopher

Title: Exploring the Impact of Pseudo and Quasi Random Number Generators on Monte Carlo Integration of the Multivariate Normal Distribution

2022

Master’s Thesis, Data Analysis and Business Analytics, Lund University

Students: Gustavo Lemos Borba, Stella Sofia Sologaistoa Betancourt

Title: Interactive Network Visualization of Insurance Portfolios

2019–2020

Google Summer of Code

Students: Qincheng Liu (2019), Akarsh Goyal (2020)

Referee Service

2024

ICML, Computo, AISTATS, Annals of Statistics

2023

Journal of Computational and Graphical Statistics

2022

NeurIPS, ICML

2021

NeurIPS

Comittee Service

2020–2022

Statistical Learning Seminar Series, Main Organizer

https://statistical-learning-seminars.github.io/

Software

SLOPE

Generalized linear models penalized with the sorted L1 norm

Author and maintainer

R package (https://CRAN.R-project.org/package=SLOPE)

benchopt

Reproducible, efficient and collaborative optimization benchmarks

Contributor

Python package (https://benchopt.github.io)

eulerr

Area-propotional Euler diagrams with ellipses

Author and maintainer

R package (https://CRAN.R-project.org/package=eulerr)

Contracted Work

2021

WHO Antimicrobial Resistance Dashboard

Contracted work to develop and deploy a visualization dashboard for WHO’s Antimicrobial Resistance team in Europe.