Pawel Pralat, Ryerson University
Assistant Director of Industry Liaison at The Fields Institute for Research in Mathematical Sciences
Professor
Toronto, Ontario
pralat@torontomu.ca
Office:
(416) 979-5000 ext. 7421
Expert In
Bio/Research
In the big data era, data is considered as the new fossil fuel. Every human-technology interaction, or sensor network, generates new data points that can be viewed, based on the type of interaction, as a self-organizing network. In these networks (for example, the Facebook on-line social network)...
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Bio/Research
In the big data era, data is considered as the new fossil fuel. Every human-technology interaction, or sensor network, generates new data points that can be viewed, based on the type of interaction, as a self-organizing network. In these networks (for example, the Facebook on-line social network) nodes not only contain some useful information (such as user's profile, photos, tags) but are also internally connected to other nodes (relations based on friendship, similar user's behaviour, age, geographic location). Such networks are large-scale, self-organizing, decentralized, and evolve dynamically over time. Understanding the principles driving the organization and behaviour of complex networks as well as algorithms based on these networks is crucial for a broad range of fields, including information and social sciences, economics, biology, and neuroscience.
Pawel's main research interests lies in graph theory with applications to real-world self-organizing networks such as the web graph, social networks, or terrorism networks. He is interested in both modelling and searching of complex networks with emphasis on connections to Big Data research questions. Both topics have experienced tremendous growth in the last few years, with an increasing number of applications in other areas of mathematics and computer science.
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Pawel's main research interests lies in graph theory with applications to real-world self-organizing networks such as the web graph, social networks, or terrorism networks. He is interested in both modelling and searching of complex networks with emphasis on connections to Big Data research questions. Both topics have experienced tremendous growth in the last few years, with an increasing number of applications in other areas of mathematics and computer science.
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