Friday, April 15, 2016 - 10:30am - 11:30am
Hideo Mabuchi (Stanford University)
The evolution from classical to quantum information technology will be greatly facilitated by the formulation of incremental approaches to making the transition. This is a far greater challenge than might seem apparent, as today we know only of completely classical and fully quantum-mechanical models as practically sensible computing paradigms and the jump to fully-quantum hardware implementations lies well beyond our current technological capabilities. There is not even a convincing road map for getting there.
Thursday, January 22, 2009 - 7:00pm - 8:30pm
Robert Ghrist (University of Pennsylvania)
Sensor networks are poised to impact society in fundamental ways analogous to the impact of the networked personal computers. The rapid development of small-scale sensors coupled with wireless ad hoc networking capability foreshadows a day when our physical surroundings will wake up with sensory data, assuming it does not drown in the data first. In this lecture, Professor Ghrist will describe a recent calculus for sensor network data, whose origins lie in the century-old theory of algebraic topology.
Wednesday, September 5, 2012 - 2:00pm - 3:00pm
Edward Ott (University of Maryland)
We consider Boolean models of the dynamics of interacting genes. Stability is defined for a large Boolean network by imagining two system states that are initially close in the sense of Hamming distance and asking whether or not their evolutions lead to subsequent divergence or convergence.
Tuesday, February 28, 2012 - 10:15am - 11:00am
Eric Kolaczyk (Boston University)
The set of tools for thinking hard about sampling and measurement-level aspects of scientific studies is among the earliest areas of statistics to
Thursday, October 27, 2011 - 3:00pm - 4:00pm
Stuart Geman (Brown University), Matthew Harrison (Brown University)
The spiking dynamics of simultaneously recorded neurons from a small region of cortex reflect the local network structure of excitatory and inhibitory connections between observed neurons, as well as the time varying response of the neurons to their many unobserved and correlated inputs. Inference about the local network is easily contaminated by these unobserved nonstationary influences. We have been exploring conditional inference as an approach for statistically isolating local network dynamics from background nonstationarities.
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