Campuses:

stochastic

Monday, December 3, 2018 - 9:00am - 10:00am
Kaveh Khodjasteh (Target Corporation)
Target is a unique retailer with a large and complex supply chain network supporting a diverse set of SKUs that it offers in store across the US and online. Making this network efficient entails solving multiple interconnected optimization problems using techniques in stochastic modeling and optimization, algorithm development and scaling, and artificial intelligence. This talk will give an overview of our approach as well as some examples in inventory management optimization, space planning and transportation, and software design using functional programming.
Wednesday, May 30, 2018 - 9:00am - 9:50am
Alla Borisyuk (The University of Utah)
Astrocytes are brain cells, as numerous as neurons, but are physiologically quite different. Astrocytes play an important role in neuronal function through their calcium signaling. In our collaborators' experimental data we see a large degree of variability in the calcium signals. In this project we will explore two major causes of this variability.
Wednesday, April 13, 2011 - 2:00pm - 3:00pm
Peter Kitanidis (Stanford University)
The subsurface is where most of the available freshwater is stored; in the United States, groundwater is the primary source of water for over 50 percent of Americans, and roughly 95 percent for those in rural areas. Cleaning up the surface from industrial and nuclear wastes is quite challenging. A major impediment in studying processes in the subsurface and in managing resources is that it is difficult to achieve accurate and reliable imaging, i.e., identification of properties, of geologic formations.
Thursday, October 21, 2010 - 9:30am - 10:30am
R. Tyrrell Rockafellar (University of Washington)
A fundamental difficulty in stochastic optimization is the fact that
decisions may not be able pin down the values of future costs, but
rather can only, within limits, shape their distributions as random variables.
An upper bound on a ramdom cost is often impossible, or too expensive, to
enforce with certainty, and so some compromise attitude must be taken to
the violations that might occur. Similarly, there is no instant
interpretation of what it might mean to minimize a random cost, apart
Friday, June 11, 2010 - 11:00am - 12:30pm
Rene Carmona (Princeton University)
No Abstract
Monday, February 8, 2016 - 2:25pm - 3:25pm
Daniel Hoehener (Massachusetts Institute of Technology)
In this talk I present a stochastic model for human driving behavior and a general (model-based) approach to design a so-called safety supervisor which can override the human driver if otherwise a collision would occur. The main property of our approach is that it provides formal guarantees for the correctness of the safety supervisor. I will illustrate the theory with two application examples.
Friday, November 20, 2015 - 9:45am - 10:00am
Abhyudai Singh (University of Delaware)
The inherent stochastic nature of biochemical processes can drive differences in gene expression between otherwise identical cells. While cell-to-cell variability in gene expression has received much attention, randomness in timing of events has been less studied. We investigate event timing at the single-cell level in a simple system, the lytic pathway of the bacterial virus phage lambda. Data reveals precision in timing: lysis occurs on average at 65 min with a standard deviation of 3.5 min.
Friday, October 26, 2012 - 10:15am - 11:05am
Lee DeVille (University of Illinois at Urbana-Champaign)
Dynamical systems defined on networks have applications
in many fields, including computational and theoretical neuroscience. In
particular, it is important to understand when networks exhibit synchronous or
other types of coherent collective behaviors. Other questions include whether
such coherent behavior is stable with respect to random perturbation, or what
the detailed structure of this behavior is as it evolves. We will examine several
models of networked dynamical systems and present a mixture of results that range
Monday, June 16, 2008 - 8:45am - 9:00am
No Abstract
Tuesday, May 13, 2008 - 1:45pm - 2:45pm
Darren Wilkinson (University of Newcastle upon Tyne)
This talk will provide an overview of computationally intensive
methods for conducting Bayesian inference for the rate constants of
stochastic kinetic intracellular reaction network models using
single-cell time course data. Inference for the true Markov jump
process is extremely challenging in realistic scenarios, so the true
model will be replaced by a diffusion approximation, known in this
context as the Chemical Langevin Equation (CLE). Inference for the CLE
is also challenging, but the development of effective algorithms is
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