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.
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
Internet routing behavior/structure
MAP Kinase Regulation Network
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