group

PhD Students

• Paul Häusner Learning to solve conditionally convex optimization problems
• Ella J. Schmidtobreick Accelerating sparse linear algebra with graph neural networks
• Laura van Weesep Accelerating decision making in drug discovery with trustworthy foundation models
• Pritish Ranjan Joshi Simulation-based inference for metal plating dynamics
• Aleix Nieto Juscafresa Multimodal machine learning for precision medicine in breast cancer
• Tinh Thi Cao Graph neural networks for structured matrix problems

Postdoctoral Researchers

• Daniel Hernández Escobar Vector optimization for radiotherapy planning

Co-supervised PhD Students

• Ziwei Luo Diffusion models for image restoration
• Viktor Vanoppen Fundamental studies of metal plating processes for energy storage
• Sanna Jarl Active learning methods for autonomous exploration of thin film optoelectronic materials
• Adhithyan Kalaivanan Coupling diffusion and flow models with Bayesian inference
• Steven Wang Causal machine learning for precision medicine in breast cancer
• Erik Thiringer Multi-modal AI precision diagnostics for breast cancer

Alumni

PhD Students

• 2021–2026 (co-supervised) Jackie Yik, A Self-Driving Lab for Battery Electrolyte Design, subsequently Postdoctoral Researcher at Korea Advanced Institute of Science and Technology (KAIST)
• 2020–2025 (co-supervised) Dominik Fay, Machine Learning with Decentralized Data and Differential Privacy: New Methods for Training, Inference and Sampling, subsequently Senior Research Scientist at Elekta
• 2019–2024 (co-supervised) Niklas Gunnarsson, Motion Estimation from Temporally and Spatially Sparse Medical Image Sequences, subsequently Lead Research Scientist at Elekta

Postdoctoral Researchers

• 2024–2026, Liam Hamed Taghavian, subsequently Postdoctoral Researcher, Oxford University
• 2022–2024, Zheng Zhao, subsequently Assistant Professor, Linköping University
• 2021–2024, Sebastian Mair, subsequently Assistant Professor, Linköping University

Master’s Thesis Students

• Stina Brunzell, Inverse Design of Thin-Film Optical Characteristics with Guided Flow Matching, 2026
• Ella J. Schmidtobreick, Accelerating Active-set Solvers using Graph Neural Networks, 2025
• William Samuelsson, Accelerating Interior Point Methods using Graph Neural Networks, 2025
• Henri Doerks, Learning Distributed Optimization with Graph Neural Networks, 2024
• Aleix Nieto Juscafresa, Graph neural network-based preconditioners for optimizing GMRES algorithm, 2024
• Jinglin Gao, Self-supervised representation learning for Micro-CT images, 2024
• Jannes van Poppelen, Phase-field modeling using physics-informed neural networks, 2024
• Duc Huy Le, Exploration-Exploitation Trade-off Approaches in Multi-Armed Bandit, 2023
• Benjamin Bucknall, Promoting Exploration in Reinforcement Learning through Surprise-Based Intrinsic Motivation, 2022
• Dmitrijs Kass, Deep reinforcement learning for isocenter placement in Gamma Knife radiosurgery, 2022
• Simon Löw, Automatic Generation of Patient-specific Gamma Knife Treatment Plans for Vestibular Schwannoma Patients, 2020
• Dominik Fay, Membership Privacy in Neural Networks for Medical Image Segmentation, 2019
• Kenneth Lau, Representation Learning on Brain MR Images for Tumor Segmentation, 2018
• Dennis Sångberg, Automated Glioma Segmentation in MRI using Deep Convolutional Networks, 2015
• Johanna Skarpman Munter, Dose-Volume Histogram Prediction using Kernel Density Estimation, 2015
• Marcus Josefsson, Robust Optimization for Radiosurgery under the Static Dose Cloud Approximation, 2014
• Jenni Svensson, Multiobjective optimization in radiosurgery: How to approximate and navigate on the Pareto surface, 2014
• Lars Lowe Sjösund, Automatic Localization of Bounding Boxes for Subcortical Structures in MR Images Using Regression Forests, 2013