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Networks Across Disciplines: Theory and Applications
Edo M Airoldi · Anna Goldenberg · Jure Leskovec · Quaid Morris

Sat Dec 11 07:30 AM -- 06:30 PM (PST) @ Westin: Nordic
Event URL: http://morrislab.med.utoronto.ca/~anna/networksnips2010/ »

Networks are used across a wide variety of disciplines to describe interactions between entities --- in sociology these are relations between people, such as friendships (Facebook); in biology --- physical interactions between genes; and many others: the Internet, sensor networks, transport networks, ecological networks just to name a few. Computer scientists, physicists and mathematicians search for mechanisms and models that could explain observed networks and analyze their properties. The research into theoretical underpinnings of networks is very heterogeneous and the breadth of existing and possible applications is vast. Yet, many of such works are only known within their specific areas. Many books and articles are written on the subject making it hard to tease out the important unsolved questions especially as richer data becomes available. These issues call for collaborative environment, where scientists from a wide variety of fields could exchange their ideas: theoreticians could learn about new questions in network applications, whereas applied researchers could learn about potential new solutions for their problems. Researchers in different disciplines approach network modeling from complementary angles. For example, in physics, scientists create generative models with the fewest number of parameters and are able to study average behavior of very large networks, whereas in statistics and social science, the focus is often on richer models and smaller networks. Continuous information exchange between these groups can facilitate faster progress in the field of network modelling and analysis.

Author Information

Edo M Airoldi (Harvard University)
Anna Goldenberg (SickKids/University of Toronto)

Dr Goldenberg is a Senior Scientist in Genetics and Genome Biology program at SickKids Research Institute, recently appointed as the first Varma Family Chair in Biomedical Informatics and Artificial Intelligence. She is also an Associate Professor in the Department of Computer Science at the University of Toronto, faculty member and an Associate Research Director, Health at Vector Institute and a fellow at the Canadian Institute for Advanced Research (CIFAR), Child and Brain Development group. Dr Goldenberg trained in machine learning at Carnegie Mellon University, with a post-doctoral focus in computational biology and medicine. The current focus of her lab is on developing machine learning methods that capture heterogeneity and identify disease mechanisms in complex human diseases as well as developing risk prediction and early warning clinical systems. Dr Goldenberg is a recipient of the Early Researcher Award from the Ministry of Research and Innovation. She is strongly committed to creating responsible AI to benefit patients across a variety of conditions.

Jure Leskovec (Stanford University and Pinterest)
Quaid Morris (University of Toronto)

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