Monday, November 26

DE/APPLIED AND COMPUTATIONAL MATH SEMINAR
TIME: 3:35 – 4:30
ROOM: 309A Ayres Hall
SPEAKER: Miun Yoon, Chaun Li, and Andreas Aristotelous

TITLE: (Yoon): Gene Network Identification Using Singular Value Decomposition and Iterative Reweighted Least-Squares (Li): Representation of Functions and Operators in L2 Using Multiwavelet Basis (Aristotelous): Nitsche's Method for the Poisson Equation with non homogeneous Dirichlet Boundary condition

Tuesday, November 27

JUNIOR COLLOQUIUM
TIME: 3:30 (pizza) 3:40 (talk)
ROOM: 214 Ayres Hall
SPEAKER: Professor Thomas Papenbrock, UTK Physics and Astronomy

TITLE: Random Matrices and Chaos in Atomic Nuclei
ABSTRACT: We speak of chaos in quantum systems if the statistical properties of the eigenvalue spectrum coincide with predictions of random-matrix theory. Chaos is a typical feature of atomic nuclei and other self-bound Fermi systems. In this colloquium, I will introduce random matrix ensembles, their connection to chaotic systems, and talk about recent applications of random matrix theory in models of atomic nuclei.

ALGEBRA SEMINAR
TIME: 1:30 p.m.
ROOM: 309B Ayres Hall
SPEAKER: Ben Lynch

Wednesday, November 28

DOCTORAL DEFENSE
Time: 2:30 p.m.
Room: Dabney 575
Committee: Drs. Hallam (chair), Collins, Gross, Hinton
Speaker: Mr. Jeff Nichols

Dissertation Title: Parallel Simulation of Individual-Based, Physiologically-Structured Population and Predator-Prey Models.

ANALYSIS SEMINAR
TIME: 3:35 – 4:25 p.m.
ROOM: 209A Ayres Hall
SPEAKER: Professor Remus Nicoara

TITLE: Commuting Squares, Hadamard Matrices and Subfactors (continued)
ABSTRACT: Commuting squares arise naturally as invariants and construction data in Jones' theory of subfactors. We investigate some existence and finiteness results for commuting squares and their associated subfactors with emphasis on those arising from complex Hadamard matrices.

Thursday, November 29

COLLOQUIUM
TIME: 3:40 p.m.
ROOM: 214 Ayres Hall
SPEAKER: Professor Michael A. Gilchrist, EEB

TITLE: Combining models of protein translation and evolution to predict production rates using codon bias
ABSTRACT: Genes are often biased in their codon usage. The degree of bias displayed often changes with expression level and intra-genic position. Numerous indices, such as the codon adaptation index, have been developed to measure this bias. While the expression level of a gene and index values are correlated, the heuristic nature of these metrics limits their ability to explain this relationship. As an alternative approach this study integrates mechanistic models of cellular and population processes in a nested manner to develop a stochastic evolutionary model of a protein's production rate.  The model is illustrated using the S. cerevisiae genome and its predictive qualities are compared and contrasted to standard, index based approaches.