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, Anmar Karmush, Mårten Wadenbäck, Ania Beatriz Rodríguez-Barrera, Johanna Rosen, Michael Felsberg, Jonas Björk, Benchmark Dataset for Catalysis on 2D MXenes (2026) 
https://arxiv.org/abs/2606.00794
Pavlo Melnyk, Cuong Le, Urs Waldmann, Per-Erik Forssén, Bastian Wandt, On the Role of Rotation Equivariance in Monocular 2D-to-3D Human Pose Lifting (2026) https://arxiv.org/abs/2601.13913
Pavlo Melnyk, Michael Felsberg, Kostas Daniilidis, 
E(n)- EQUIVARIANT SPHERICAL DECISION SURFACES, GEOMETRY-GROUNDED REPRESENTATION LEARNING AND GENERATIVE MODELING WORKSHOP, GRAM AT ICLR 2026, Proceedings of Machine Learning Research, pp. 416-432, JMLR-JOURNAL MACHINE LEARNING RESEARCH (2026)
Akshay Chandrasekhar, Pavlo Melnyk, Loss-Invariant Projections as Passive Probes of Learned Representations (2026) https://arxiv.org/abs/2610.06195
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
