Speaker:

István Kovács, Assistant Professor, Department of Physics and Astronomy, Northwestern University

Title:

How can we learn from noisy, incomplete, or even biased network data?

Abstract:

Network theory is a powerful tool to describe and study complex systems, and there has been tremendous progress in mapping large networks in all areas of science, leading to a growing library of complex network datasets. Yet, inherent limitations of the measurements lead to errors, biases and missing data. Therefore, as in any other quantitative field, it would be of paramount importance to characterize the uncertainty of our maps. Yet, unlike a simple error bar for a single valued quantity, the uncertainty of a network structure itself is expected to have a complex, network structure, requiring novel methodologies. Focusing on biological networks, we show how such detailed information can help us to solve key problems, such as link prediction, noise reduction or functional annotation. I will close by highlighting ongoing research directions and some surprising connections to modern physics. To conclude, putting error bars on our network maps is not a nuisance but an essential ingredient in addressing long standing problems in the field.

Speaker Bio:

István Kovács is Assistant Professor in the Department of Physics and Astronomy at Northwestern University. Previously he was a postdoctoral fellow in the Network Science Institute at Northeastern University, a visiting researcher in the Center for Cancer Systems Biology at the Dana-Farber Cancer Institute and at University of Toronto, as well as at the Department of Network and Data Science of the Central European University. He received a PhD in Physics from Eötvös Loránd University in Hungary, working at the Wigner Research Centre for Physics, during which he spent time at Semmelweis University and University of Saarbrücken, Germany. His group develops novel methodologies to predict the emerging structural and functional patterns in problems ranging from systems biology to quantum physics, in close collaboration with experimental groups.

Webinar:

Webinar link: https://northwestern.zoom.us/s/93326446732
Passcode: nico

About the Speaker Series:

Wednesdays@NICO is a vibrant weekly seminar series focusing broadly on the topics of complex systems and data science. It brings together attendees ranging from graduate students to senior faculty who span all of the schools across Northwestern, from applied math to sociology to biology and every discipline in-between.

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