Skip to main content

Combinatorial Structures in Time Series and Images

Wednesday, May 15, 2019, 1:30 pm – 2:30 pm

ABSTRACT | A few years ago the visibility graph algorithm was proposed as a mapping between time series and graphs, opening the possibility to make graph-based signal processing as well as building a bridge between time series analysis and network science [L. Lacasa, et al. “From time series to complex networks: the visibility graph” PNAS 105 (2008)]. A decade later that single contribution has sparkled an intense research activity at the interface between time series analysis and networks, and strategies to extract graph-based features for the classification of complex signals have shown to be a fruitful avenue. Recently the original algorithm has been extended to characterize spatial data structures by mapping scalar fields of arbitrary dimension into graphs, thus enabling the possibility of constructing the visibility graphs of images, landscapes, large-scale spatially-extended surfaces and, in general, to analyze spatially extended data structures of any dimension as network structures. 

The range of potential applications of this combinatorial framework includes image processing in engineering, the description of surface growth in material science, soft matter or medicine and the characterization of potential energy surfaces in chemistry, disordered systems and high energy physics. 

BIO | Jacopo Iacovacci received his B.S. degree in Physics from University of Rome La Sapienza in 2010, and his M.S. degree in Biophysics in 2012 from the same university. In 2013 he joined the Complex Systems and Networks Group at Queen Mary University of London where he obtained his Ph.D. degree in Mathematics in 2017 under the supervision of Dr. L. Lacasa and Dr. G. Bianconi with the thesis 'Motif formation and emergence of mesoscopic structures in complex networks'. He is currently Postdoctoral Research Associate at Imperial College London and at The Francis Crick Institute and his research focuses on developing machine learning and network-based techniques for the analysis of omics data as well as on modelling metabolic interactions and cooperation in populations of yeast cells.