Title: Sparse Approximations for Time-Series Networks
Abstract: In many real-world networks, such as the human brain, online social networks, and gene regulatory networks, different agents/entities interact with others over time. This talk will discuss methods to infer and approximate the network topology, that is to identify which agents are interacting with which other agents, without specific distributional assumptions.
Bio: Chris Quinn is an Assistant Professor in the Department of Computer Science. He received his PhD from the department of Electrical and Computer Engineering at UIUC. He started at Purdue University in Industrial Engineering and recently joined Iowa State. He is broadly interested in machine learning, information theory, and network science with applications in neuroscience, social networks, and biomedical science.
After the presentation, there will be a short time for discussion and questions afterwards. Please feel free to bring your lunch!