Postdoc
I am a postdoctoral researcher in the division for Computer Vision and Learning Systems (CVL). My research focuses on geometry, equivariant modelling, and applications of AI for Science. Here at CVL, I have earned my PhD in Electrical Engineering with a specialisation in Computer Vision, where I worked on Geometric Deep Learning under the supervision of Michael Felsberg and with funding from WASP.
For more details about my work, please visit pavlomelnyk.com.
Pavlo Melnyk, Cuong Le, Urs Waldmann, Per-Erik Forssén, Bastian Wandt, On the Role of Rotation Equivariance in Monocular 3D Human Pose Estimation (2026) 
https://arxiv.org/abs/2601.13913
Pavlo Melnyk, 
Spherical NeurO(n)s for Geometric Deep Learning, Linköping Studies in Science and Technology. Dissertations 2393, Linköping University Electronic Press, Linköping (2024) 
https://doi.org/10.3384/9789180756808
Pavlo Melnyk, Andreas Robinson, Michael Felsberg, Mårten Wadenbäck, TetraSphere: A Neural Descriptor for O(3)-Invariant Point Cloud Analysis, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). June 2024, IEEE Conference on Computer Vision and Pattern Recognition, pp. 5620-5630, IEEE Computer Society (2024) https://doi.org/10.1109/CVPR52733.2024.00537 https://arxiv.org/abs/2211.14456
Pavlo Melnyk, Michael Felsberg, Mårten Wadenbäck, Andreas Robinson, Cuong Le, On Learning Deep O(n)-Equivariant Hyperspheres, Proceedings of the 41st International Conference on Machine Learning, Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix (eds.), Proceedings of Machine Learning Research, pp. 35324-35339, PMLR (2024) https://arxiv.org/abs/2305.15613
Pavlo Melnyk, Michael Felsberg, Mårten Wadenbäck, Steerable 3D Spherical Neurons, Proceedings of the 39th International Conference on Machine Learning, Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvari, Gang Niu, Sivan Sabato (eds.), Proceedings of Machine Learning Research, pp. 15330-15339, PMLR (2022) https://arxiv.org/abs/2106.13863
