Our research focuses on both computational biology and complex networks analysis.

A) Computational biology

We develop various algorithms on strings and graphs to improve and accelerate methods for mapping and de novo assembly of single genomes, metagenomes and transcriptomes. In addition we are particularly interested in genome phasing. We optimize our code utilizing SIMD (Single instruction multiple data) instructions on single core, and parallel programing on multi core and GPU architectures. In our work we use various machine learning, AI and signal processing methods to achieve maximal performance. We focus primarily, but not exclusively, on datasets produced using long read technologies developed by Oxford Nanopore Technologies (ONT) and Pacific Biosciences. The analysis of signal level information obtained from ONT sequences is of our particular interest. Using this information we plan to develop new methods for fast identification of microbes in metagenomics samples and modified nucleotides.

B) Complex network analysis

We focus our research on dynamics in complex networks. We study the question of inferring the source of a rumour or epidemic in a network. In addition we are interested in identifying exogenous and endogenous activity in social media. We investigate wisdom of the crowd approach to forecasting. We plan to use network for understanding microbial community dynamics at various scales and quantitative trait prediction from transcriptomic data.


  • 2015 University of Zagreb, FER - award for the paper "Identification of Patient Zero in Static and Temporal Networks " published in PRL

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