School of Mathematical Sciences

Primoz Skraba

Primoz

Senior Lecturer in Applied and Computational Topology

Email: p.skraba@qmul.ac.uk
Room Number: Mathematical Sciences Building, Room: MB-122

Profile

Primoz Skraba is a Senior Lecturer in Applied and Computational Topology. His research is broadly related to data analysis with an emphasis on topological data analaysis . Generally, the problems he considers span both theory and applications. On the theory side, the areas of interest include stability and approximation of algebraic invariants, stochastic topology (the topology of random spaces), and algorithmic reseatch.  On the applications side, he focuses on combining topological ideas with machine learning, optimization, and  other statistical tools. Other applications areas of interest include visualization and geometry processing.

He received a PhD in Electrical Engineering from Stanford University in 2009 and has held positions at INRIA in France and the Jozef Stefan Institute, the University of Primorska, and the University of Nova Gorica in Slovenia, before joining Queen Mary University of London in 2018. He is also currently a Fellow at the Alan Turing Institute.

Research

Publications

A. Poulenard, P. Skraba, M. Ovsjanikov, Topological Function Optimization for Continuous Shape Matching, Computer Graphics Forum (Proceeding of the Symposium of Geometry Processing), 2018

O. Bobrowski, M. Kahle, and P. Skraba, Maximally Persistent Cycles in Random Geometric Complexes, vol. 27, no. 4, Annals of Applied Probability, 2017.

D. Govc, P. Skraba, An Approximate Nerve Theorem, Foundations of Computational Mathematics, 2017 

M. Kerber, D. Sheehy, P. Skraba. Persistent Homology and Nested Dissection. Proceedings of the 27th Annual ACM-SIAM Symposium on Discrete Algorithms. SIAM, 2016, Arlington, Virginia, USA.

F. Chazal, L. Guibas, S. Oudot, P. Skraba, Persistence-Based Clustering in Riemannian Manifolds, Journal of the ACM, vol. 60, no. 6, Article 10, 2013

P. Skraba, B. Wang, G. Chen, P. Rosen, 2D Vector Field Simplification based on Robustness, 7th Pacific Visualization Symposium, March 4-7, 2014, Yokohama, Japan. (Best Paper Award)