[Jan 3, 2025] New blog post: My Year of Reading in 2024.
[Nov 18, 2024] New preprint: Multi-Stain Modelling of Histopathology Slides for Breast Cancer Prognosis Prediction [medRxiv] [project], work lead by Abhinav Sharma.
[Oct 25, 2024] New preprint: Automated Segmentation of Synchrotron-Scanned Fossils [bioRxiv] [project], work lead by Melanie During and Jordan Matelsky.
[Oct 10, 2024] New preprint: Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts [arXiv] [project].
[Oct 1, 2024] New preprint: Evaluating Deep Regression Models for WSI-Based Gene-Expression Prediction [arXiv] [project].
[Sep 16, 2024] New preprint: Taming Diffusion Models for Image Restoration: A Review [arXiv] [project], work lead by Ziwei Luo.
[Jul 3, 2024] Accepted paper: Evaluating Regression and Probabilistic Methods for ECG-Based Electrolyte Prediction, work lead together with Philipp Von Bachmann and Daniel Gedon, has been published in Scientific Reports.
[Apr 15, 2024] Accepted paper: Photo-Realistic Image Restoration in the Wild with Controlled Vision-Language Models has been accepted to CVPR Workshops 2024. This is follow-up work on our ICLR paper, once again lead by Ziwei Luo.
[Mar 21, 2024] Invited talk: I presented How Reliable is Your Regression Model’s Uncertainty Under Real-World Distribution Shifts? [slides] [video] at the RISE Learning Machines Seminars.
[Feb 12, 2024] I am now officially co-advising Ziwei Luo, PhD student at Uppsala University, together with Thomas Schön (main supervisor) and Jens Sjölund.
[Jan 29, 2024] New blog post: My Year of Reading in 2023.
[Jan 16, 2024] Accepted paper: Controlling Vision-Language Models for Multi-Task Image Restoration, work lead by the incredibly skilled Ziwei Luo, has been accepted to ICLR 2024.
[Dec 8, 2023] I have joined the group of Mattias Rantalainen at Karolinska Institutet as a postdoc to work on computational pathology.
[Nov 30, 2023] I have successfully defended my PhD thesis Towards Accurate and Reliable Deep Regression Models [pdf] [DiVA] [slides] [video].
[Oct 31, 2023] Invited talk: I presented How Reliable is Your Regression Model’s Uncertainty Under Real-World Distribution Shifts? [slides] at the DFKI Augmented Vision Workshop.
[Oct 2, 2023] New preprint: Controlling Vision-Language Models for Multi-Task Image Restoration [arXiv] [code] [project].
[Jun 28, 2023] Accepted paper: How Reliable is Your Regression Model’s Uncertainty Under Real-World Distribution Shifts? has been accepted to TMLR.
[Jun 22, 2023] New blog post: The How and Why of Reading 300 Papers in 5 Years.
[Apr 25, 2023] Accepted paper: Image Restoration with Mean-Reverting Stochastic Differential Equations has been accepted to ICML 2023.
[Apr 18, 2023] Accepted paper: Refusion: Enabling Large-Size Realistic Image Restoration with Latent-Space Diffusion Models has been accepted to CVPR Workshops 2023.
[Mar 16, 2023] Today I presented Some Advice for New (and Old?) PhD Students [slides] at our weekly division meeting.
[Feb 8, 2023] New preprint: How Reliable is Your Regression Model’s Uncertainty Under Real-World Distribution Shifts? [arXiv] [code] [project].
[Jan 27, 2023] New preprint: Image Restoration with Mean-Reverting Stochastic Differential Equations [arXiv] [code] [project].
[Dec 21, 2022] New preprint: ECG-Based Electrolyte Prediction: Evaluating Regression and Probabilistic Methods [arXiv] [code] [project].
[Feb 7, 2022] New blog post: Explaining machine learning and how it can be used to help doctors.
[Feb 3, 2022] Today I presented my half-time seminar Energy-Based Probabilistic Regression in Computer Vision [slides].
[Jan 18, 2022] Accepted paper: Learning Proposals for Practical Energy-Based Regression has been accepted to AISTATS 2022.
[Oct 22, 2021] New preprint: Learning Proposals for Practical Energy-Based Regression [arXiv] [code] [project].
[Sep 13, 2021] I will be a contingent worker at Facebook Reality Labs until mid-December (part-time internship extension).
[Sep 3, 2021] Accepted paper: Uncertainty-Aware Body Composition Analysis with Deep Regression Ensembles on UK Biobank MRI has been accepted for publication in Computerized Medical Imaging and Graphics.
[Jun 7, 2021] I will spend the summer as a research intern at Facebook Reality Labs (FRL Research Pittsburgh, remote due to COVID-19).
[Apr 17, 2021] Accepted paper: Accurate 3D Object Detection using Energy-Based Models has been accepted to CVPR Workshops 2021.
[Apr 10, 2021] Accepted paper: Deep Energy-Based NARX Models has been accepted to SYSID 2021.
[Jan 18, 2021] New preprint: Uncertainty-Aware Body Composition Analysis with Deep Regression Ensembles on UK Biobank MRI [arXiv] [code] [project].
[Dec 9, 2020] New preprint: Deep Energy-Based NARX Models [arXiv] [code] [project].
[Dec 8, 2020] New preprint: Accurate 3D Object Detection using Energy-Based Models [arXiv] [code] [video] [project].
[Oct 21, 2020] In November and December, I will visit the Computer Vision Lab at ETH Zürich (remote due to COVID-19).
[Jul 29, 2020] Accepted paper: How to Train Your Energy-Based Model for Regression has been accepted to BMVC 2020.
[Jul 3, 2020] Accepted paper: Energy-Based Models for Deep Probabilistic Regression has been accepted to ECCV 2020.
[May 5, 2020] New preprint: How to Train Your Energy-Based Model for Regression [arXiv] [code] [project].
[Apr 7, 2020] Accepted paper: Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision has been accepted to CVPR Workshops 2020.
[Oct 1, 2019] Accepted workshop paper: Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision has been accepted to the workshop on Bayesian Deep Learning at NeurIPS 2019.
[Sep 27, 2019] New preprint: Energy-Based Models for Deep Probabilistic Regression [arXiv] [code] [project].
[Jun 5, 2019] New preprint: Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision [arXiv] [code] [video] [project].
[May 18, 2019] I will present our extended abstract on ensembling as approximate Bayesian inference at SSDL19, June 10 [slides].
[Apr 25, 2019] I have posted the first part in a short series of blog posts on how to get started with PyTorch and deep learning.
[Nov 24, 2018] I was awarded the Tryggve Holm medal for “outstanding student achievements” during my time at Linköping University.
[Sep 28, 2018] New project: PyTorch implementation of DeepLabV3, see GitHub repository and Youtube video for further details.
[Sep 21, 2018] The code for 3D object detection used in my MSc thesis has been uploaded to GitHub.
[Sep 20, 2018] I have created a GitHub repository for posting summaries of interesting papers that I read.
[Sep 13, 2018] I have joined the group of Prof. Thomas Schön as a PhD student to work on uncertainty-aware deep learning.