Robert NowakECE Dept., UW-Madisonnowak@engr.wisc.eduwww.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.

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