| Apr 05, 2026 |
Paper accepted in Medical Physics: A parallel algorithm for generating Pareto-optimal radiosurgery treatment plans. This is a result of our collaboration with Elekta.
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| Jan 22, 2026 |
Two papers accepted at L4DC 2026: Warm-starting active-set solvers using graph neural networks (led by Ella J. Schmidtobreick) and Learning to accelerate distributed ADMM using graph neural networks (led by Henri Doerks and Paul Häusner).
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| Nov 01, 2025 |
Received a VINNOVA planning grant for Automate Sweden, a cluster of excellence on robotics bringing together leading Swedish universities and industry partners under a shared Physical AI vision.
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| Mar 01, 2025 |
New paper in npj Computational Materials: Navigating chemical design spaces for metal-ion batteries via ML-guided phase-field simulations. Combines phase-field modelling with Bayesian optimization to study electrode/electrolyte interface reactions. Work led by Liam (Hamed) Taghavian.
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| Feb 01, 2025 |
New paper in Cell Reports Physical Science: Accelerating aqueous electrolyte design with automated full-cell battery experimentation and Bayesian optimization. Demonstrates a self-driving lab for high-throughput battery electrolyte screening. Work led by Jackie Yik.
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| Jan 15, 2025 |
Two review articles published in Philosophical Transactions of the Royal Society A: Conditional sampling within generative diffusion models and Taming diffusion models for image restoration: a review. Work led by Zheng Zhao and Ziwei Luo.
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| Dec 01, 2024 |
New paper at NeurIPS 2024: Entropy-regularized diffusion policy with Q-ensembles for offline reinforcement learning. Combines diffusion models with entropy regularization for offline RL. Work led by Ruoqi Zhang.
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| Sep 15, 2024 |
New paper at ICLR 2024: Controlling vision-language models for multi-task image restoration. DA-CLIP adapts pretrained vision-language models for unified image restoration. Work led by Ziwei Luo.
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| Sep 01, 2024 |
New paper in TMLR: Neural incomplete factorization: learning preconditioners for the conjugate gradient method. A GNN that learns sparse matrix factorizations to precondition iterative solvers. Work led by Paul Häusner.
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| Jun 15, 2024 |
Received a Swedish Research Council (VR) starting grant for the project Accelerating sparse linear algebra with graph neural networks.
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| Jun 01, 2024 |
Awarded the Göran Gustafsson prize for young researchers in natural sciences and technology.
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