1 / 3

Robert Nowak ECE Dept., UW-Madison nowak@engr.wisc ece.wisc/~nowak

Robert Nowak ECE Dept., UW-Madison nowak@engr.wisc.edu www.ece.wisc.edu/~nowak. Research Interests : statistical signal processing, machine learning, imaging and network science, and applications in communications, bio/medical imaging, and in silico genomics. .

ronnie
Download Presentation

Robert Nowak ECE Dept., UW-Madison nowak@engr.wisc ece.wisc/~nowak

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Robert Nowak ECE Dept., UW-Madison nowak@engr.wisc.edu www.ece.wisc.edu/~nowak Research Interests: statistical signal processing, machine learning, imaging and network science, and applications in communications, bio/medical imaging, and in silico genomics.  Network Science, National Academies Press, 2006 The study of complex networked systems. Key Challenges : “Characterization of the dynamics and information flow in networked systems, modeling, analysis, and acquisition of experimental data for extremely large networks.” My take: In many large-scale problems we have limited prior knowledge, but a wealth of data.  How much can we learn from data? Adaptivity to unknown system behavior is key.

  2. Internet routing behavior/structure MAP Kinase Regulation Network Challenge 1: Inferring Networks from Experimental Data Network Tomography: Infer network behavior and structure from indirect and incomplete data • Challenges: • ill-posed problem • errors and noise • calibration

  3. Challenge 2: Detecting Weak Non-Local Signals Network Detection: Xi = data at each node Test: H0 : Xi ~ N(0,1) for all i vs. H1 : Xi ~ N(m,1), m > 0, at handful of nodes • Challenge: • m > 0 may be so small, that individual testing at each node is unreliable (e.g., biohazard or Internet virus detection) • plug-in schemes (e.g., the GLRT) are suboptimal in high dimensional settings • Data fusion (aggregation) can enhance detection capabilities, but typically requires strong prior knowledge Detection must be adaptive to unknown network behavior and/or structure

More Related