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portfolio
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publications
Graph-based machine learning beyond stable materials and relaxed crystal structures
Published in Physical Review Materials, 2022
Evaluating the performance of a GNN for crystals in a high-throughput search setting, and proposing an ML-assisted workflow
Recommended citation: Ekström Kelvinius, F., Armiento, R., & Lindsten, F. (2022). Graph-based machine learning beyond stable materials and relaxed crystal structures. Physical Review Materials, 6(3), 033801.
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Autoregressive Diffusion Models with non-Uniform Generation Order
Published in ICML 2023 Workshop on Structured Probabilistic Inference & Generative Modeling, 2023
Investigating the generation order in autoregressive (diffusion) models for graph generation
Recommended citation: Kelvinius, F. E., & Lindsten, F. (2023). Autoregressive Diffusion Models with non-Uniform Generation Order. In ICML 2023 Workshop on Structured Probabilistic Inference & Generative Modeling.
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Accelerating Molecular Graph Neural Networks via Knowledge Distillation
Published in Thirty-Seventh Annual Conference on Neural Information Processing Systems (NeurIPS 2023), 2023
Using knowledge distillation for improving performance of molecular graph neural networks
Recommended citation: Ekström Kelvinius, F., Georgiev, D., Toshev, A., & Gasteiger, J. (2024). Accelerating molecular graph neural networks via knowledge distillation. Advances in Neural Information Processing Systems, 36.
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Discriminator Guidance for Autoregressive Diffusion Models
Published in The 28th International Conference on Artificial Intelligence and Statistics (AISTATS), 2024
Three versions of discriminator guidance for autoregressive (diffusion) models
Recommended citation: Ekström Kelvinius, F. & Lindsten, F.. (2024). Discriminator Guidance for Autoregressive Diffusion Models. Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 238:3403-3411 Available from https://proceedings.mlr.press/v238/ekstrom-kelvinius24a.html.
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Solving Linear-Gaussian Bayesian Inverse Problems with Decoupled Diffusion Sequential Monte Carlo
Published in arXiv, 2025
Sequential Monte Carlo method for solving linear-Gaussian inverse problems with diffusion priors
Recommended citation: Ekström Kelvinius, F., Zhao, Z., & Lindsten, F. (2025). Solving Linear-Gaussian Bayesian Inverse Problems with Decoupled Diffusion Sequential Monte Carlo. arXiv preprint arXiv:2502.06379.
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WyckoffDiff – A Generative Diffusion Model for Crystal Symmetry
Published in arXiv, 2025
Discrete diffusion model for generating materials as descriptions of their symmetry properties
Recommended citation: Ekström Kelvinius, F., Andersson, O. B., Parackal, A. S., Qian, D., Armiento, R., & Lindsten, F. (2025). WyckoffDiff-A Generative Diffusion Model for Crystal Symmetry. arXiv preprint arXiv:2502.06485.
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talks
Learning on Graphs and Geometry Meetup Uppsala
Published:
Participated in the Learning on Graphs and Geometry meetup in Uppsala and presented our NeurIPS 2023 paper Accelerating Molecular Graph Neural Networks via Knowledge Distillation
teaching
TDAB01 Probability and Statistics
Undergraduate course, Linköping University, Division of Statistics and Machine Learning, 2021–present
A first course in probability and statistics for students on the engineering program in software engineering.
TDDE01/732A99 Machine Learning
Undergraduate course, Linköping University, Division of Statistics and Machine Learning, 2020–2022
Master’s level course in Machine Learning (TDDE01/732A99). Assisted in lab exercises and corrected exams.
TDDE70 Deep Learning
Undergraduate course, Linköping University, Division of Statistics and Machine Learning, 2024–present
Was part of the development of the new course in deep learning, given at the master’s level for engineering students for the first time in spring 2024. I was particurly involved in the development of the labs, and responsible for developing a lab on graph neural networks.
Master’s thesis supervision
Undergraduate course, Linköping University, Division of Statistics and Machine Learning, 2021–present
As a PhD student, I have been supervising 17 students in their master’s thesis work on topics related to machine learning. The students have been mainly from the Master’s program in Statistics and Machine Learning, but also the engineering programs in Computer Science and Engineering, Applied Physics and Electrical Engineering, and Industrial Engineering and Management.