Graduate Student Seminar
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When: Every Friday, 4:00 pm
Where: Professor Lounge (PL), Fine Hall
This week (April 28):
Speaker: Jason Dominy
Subject: to be announced
Abstract: to be announced
Next week (May 5):
Speaker: no speaker
Subject: no speaker
Schedule for Spring 2005/2006:
Date Speaker Title of the talk Location Feb.17 Kolia Sadeghi Maximum entropy modeling for spike train data 214 Feb. 24 no speaker Mar. 3 Aleksander Donev Do Binary Hard Disks Exhibit an Ideal Glass Transition? PL Mar. 10 Filip Matejka Mathematical Models in Economics PL Mar. 17 spring break
Mar. 24 Mar. 31 Katy Bold Applying Multiscale Techniques to Evolving Networks 110 Apr. 7 Chenwei Zhu Random Measurements in Sparse Recovery PL Apr. 14 Easter
Apr. 21 Jana Gevertz Modeling the effects of vasculature evolution on early brain tumor growth 214 Apr. 28 Jason Dominy PL May 5 no speaker
Abstracts:
February 17, 2006. Kolia Sadeghi. Maximum entropy modeling for spike train data. I will give an intuitive account of maximum entropy modeling. This will be illustrated on a recent computational neuroscience paper (arXiv:q-bio.NC/0512013) dealing with a simple Ising model, and on some of my own work where features are learned.
March 3, 2006. Aleksander Donev. Do Binary Hard Disks Exhibit an Ideal Glass Transition? We demonstrate that there is no ideal glass transition in a binary hard-disk mixture by explicitly constructing an exponential number of jammed packings with densities spanning the spectrum from the accepted "amorphous" glassy state to the phase-separated crystal. Thus the configurational entropy cannot be zero for an ideal amorphous glass, presumed distinct from the crystal in numerous theoretical and numerical estimates in the literature. This objection parallels our previous critique of the idea that there is a most-dense random (close) packing for hard spheres [Torquato et al, Phys. Rev. Lett., 84, 2064 (2000)].
March 10, 2006. Filip Matejka. Mathematical Models in Economics. We will first review some fundamental concepts in economics, then turn from qualitative descriptions to mathematical formulations - optimal control problems mainly, and finally, we will talk about modelling economics on networks.
March 31, 2006. Katy Bold. Applying Multiscale Techniques to Evolving Networks. Multiscale problems arise in different disciplines and are a popular area of applied math. Work on multiscale problems has largely been for continuous processes that are described by ordinary or partial differential equations. The same techniques can be applied to discrete problems, such as the evolution of networks (graphs). Features of multiscale problems and basic techniques for analyzing these problems will be discussed. I will give simple examples of applying multiscale techniques to evolving networks.
April 7, 2006. Chenwei Zhu. Random Measurements in Sparse Recovery. Suppose one is given a small number of (possibly noisy) linear measurements of a signal. If the number of measurements is less than the number of degrees of freedom of the signal, then one of course cannot reconstruct the signal from the measurements in general. But if one makes the additional hypothesis that the signal is sparse, or at least compressible, then it does become possible to recover the signal accurately, stably, and quickly, --from random measurements. I will survey a number of recent theoretical developments of this area (related to optimization, LP and random matrix theory) and propose some new challenges.
April 21, 2006. Jana Gevertz. Modeling the effects of vasculature evolution on early brain tumor growth. The modeling of tumor growth is an active area of research in mathematical biology that dates back to the 1950s. Nearly all cancer models treat the growing tumor as an entity that is independent of its environment. However, it is well recognized that the evolution of a tumor mass intricately depends on feedback mechanisms occurring between the growing tumor and its environment. I will discuss a novel two-dimensional hybrid cellular automaton model that couples the remodeling of the host microvasculature with the evolution of the tumor mass. The model is consistent with qualitative features of brain tumor growth and can be used to probe the efficacy of a variety treatment strategies.
Any questions concerning the PACM Student Seminar?
Please contact this person:
| Name: Sergey Kryazhimskiy |  |
| E-mail: skryazhi@princeton.edu | |
| Location: 202 Eno Hall |
Last updated: April 24, 2006
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