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Robert Nowak ECE Dept., UW-Madison [email protected] 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. . - PowerPoint PPT Presentation
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Robert NowakECE Dept., [email protected]/~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.
Challenge 1: Inferring Networks from Experimental Data
Network Tomography: Infer network behavior and structure from indirect and incomplete data
MAP Kinase Regulation NetworkInternet routing behavior/structure
Challenges: • ill-posed problem• errors and noise• calibration
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(,1), > 0, at handful of nodes
Challenge: • > 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