Alex Gavryushkin

Biography

Dr Alex Gavryushkin conducts research within the area of mathematical and computational genomics. In 2009 he completed a PhD in mathematics at Novosibirsk University (Russia). Alex’s subsequent research in theoretical computer science, focused on computational methods and online algorithms, was funded by a Government of Russia Doctor of Science Fellowship and a research grant from the University of Auckland. Alex used these prestigious awards to develop the mathematical and algorithmic fundamentals necessary for the online evolutionary analysis of large molecular sequence data, while at the University of Auckland. This work led Alex to Switzerland, where he took up a Research Fellow position in the Department of Biosystems Science and Engineering at ETH Zurich (2016). Alex will now return to found a research group working on data-scalable computational methods in biology and biomedicine at the University of Otago, the heart of New Zealand’s biomedical community.

Research summary

Evolutionary analysis of large molecular sequence data is widely employed throughout modern biology, medicine, and pharmacology. Thanks to next generation sequencing technologies, thousands of whole genome sequences are constantly produced at low cost. Today's computational methods and technologies are barely prepared to analyse the huge amount of data produced and answer basic biological questions in a statistically sound way. As a result many big data sets are analysed with simple and inappropriate models, or using approximations that may produce inaccurate inferences. The roots of the problem lie in our lack of understanding of fundamental mathematical principles that form the grounds for evolutionary analysis of data.

The pressing need to develop effective computational methods that can accurately analyse big data under appropriately complex evolutionary models constantly requires new algorithmic ideas as well as solutions of standing computational challenges. The ever-accelerating pace at which molecular sequence data is produced in the modern world makes it impractical to rerun all analyses every time new data arrives or existing data is refined. This motivates research on so-called "online" computational biology algorithms capable of integrating new data as it appears. Such algorithm enable data-scalable methods in evolutionary analyses of molecular data.

In this research programme Dr Alex Gavryushkin will develop mathematical, statistical, and computational machinery for evolutionary analysis of ‘omics’ data, with a specific focus of enabling such online algorithms. He and his research team will apply methods from modern branches of computational geometry, data science, and computer science to enhance statistical and computational performance of evolutionary approaches used in epidemiology, cancer research, ecology, and pharmacology. Specifically, they will develop efficient online methods and algorithms for several classes of inference problems that arise in evolutionary biology.