CV

Publications

• Extended target tracking utilizing machine-learning software–with applications to animal classification Magnus Malmström, Anton Kullberg, Isaac Skog, Daniel Axehill, Fredrik Gustafsson, preprint, 2023
• Fusion framework and multimodality for the Laplacian approximation of Bayesian neural networks Magnus Malmström, Isaac Skog, Daniel Axehill, Fredrik Gustafsson, preprint, 2023.
• Uncertainty quantification in neural network classifiers – a local linear approach Magnus Malmström, Isaac Skog, Daniel Axehill, Fredrik Gustafsson, preprint, 2023.
• On the validity of using the delta method for calculating the uncertainty of the predictions from an overparameterized model Magnus Malmström, Isaac Skog, Daniel Axehill, Fredrik Gustafsson, Proc. of 22nd IFAC World Congress, (IFAC), 2023
• Detection of outliers in classification by using quantified uncertainty in neural networks Magnus Malmström, Isaac Skog, Daniel Axehill, Fredrik Gustafsson, Proc. of {IEEE} 25th Int. Conf. on Inf. Fusion (FUSION), 2022
• A Novel and Fast Approach for Reconstructing {CASSI}-Raman Spectra using Generative Adversarial Networks Jacob Eek, David Gustafsson, Ludwig Hollmann, , Markus Nordberg, Isaac Skog, Magnus Malmström, 11th Int. Conf. on Image Proc. Theory, Tools and App. (IPTA), 2022
• Modeling of the tire-road friction using neural networks including quantification of the prediction uncertainty Magnus Malmström, Isaac Skog, Daniel Axehill, Fredrik Gustafsson, Proc. of {IEEE} 24th Int. Conf. on Inf. Fusion (FUSION), 2021
• Uncertainties in Neural Networks A System Identification Approach Magnus Malmström, Licentiate Thesis , 2021
• Asymptotic Prediction Error Variance for Feedforward Neural Networks Magnus Malmström, Isaac Skog, Daniel Axehill, Fredrik Gustafsson Proc. of 21st IFAC World Congress, (IFAC), 2020
• 5G Positioning - A Machine Learning Approach Magnus Malmström, Isaac Skog, Sara Modarres Razavi, Yuxin Zhao, Fredrik Gunnarsson, Proc. of {IEEE} 16th Workshop on Positioning Navig. Commun. , (WPNC) 2019
• 5G Positioning using Machine Learning Magnus Malmström, Master Thesis , 2018

Education

• B.S. in GitHub, GitHub University, 2012
• M.S. in Jekyll, GitHub University, 2014
• Ph.D in Version Control Theory, GitHub University, 2018 (expected)

Work experience

• Summer 2015: Research Assistant
  • Github University
  • Duties included: Tagging issues
  • Supervisor: Professor Git
• Fall 2015: Research Assistant
  • Github University
  • Duties included: Merging pull requests
  • Supervisor: Professor Hub

Skills

• Skill 1
• Skill 2
  • Sub-skill 2.1
  • Sub-skill 2.2
  • Sub-skill 2.3
• Skill 3

Publications

Paper Title Number 3

Talks

Talk 2 on Relevant Topic in Your Field

February 01, 2014

Talk at London School of Testing, London, UK

Conference Proceeding talk 3 on Relevant Topic in Your Field

March 01, 2014

Conference proceedings talk at Testing Institute of America 2014 Annual Conference, Los Angeles, CA

Licentiate presentation and defence

May 01, 2021

Talk at Linköping University, Linköping, Sweden

Licentiate intro

March 30, 2023

Tutorial at ,

Teaching

Teaching

Supervised Master’s Theses

Service and leadership

• Currently signed in to 43 different slack teams