UTD Computational Science Seminar (Spring 2014)
All seminars are held in FO 2.604 from 12 noon to 1 pm.
Research Associate, Mathematical Sciences, UTD
Finite-Temperature Dynamics of Matter-Wave Dark Solitons in Linear and Periodic Potentials
We study matter-wave dark solitons in atomic Bose-Einstein condensates at finite temperatures, under the effect of linear and periodic potentials. Our model, namely a dissipative Gross-Pitaevskii equation, is treated analytically by means of dark soliton perturbation theory, which results in a Newtonian equation of motion for the dark soliton center. For sufficiently small wavenumbers of the periodic potential and weak linear potentials, the results are found to be in good agreement with pertinent ones obtained via a Bogoliubov-de Gennes analysis and direct numerical simulations.
Associate Professor and Founding Director of the Center for Multi-scale Intelligent Integrated Interactive Sensing
Center for Space Science, Department of Physics, UTD
Multi-scale Multi-Platform Remote Sensing, Big Data and Machine Learning for Societal Benefit
We are at the dawn of a new era of discovery. Unprecedented amounts of data in multiple areas are becoming available and being archived. The data comes from a vast array of sources from Remote Sensing and Aerial vehicles, to wearable sensors, business analytics, news feeds, social media and everything in between. The ultimate goals of gathering these data sets are improved insights and objective data driven decisions. However, a very real obstacle to realizing this goal is the existence of data silos. Health care is a case in point. One set of silos is the massive amounts of Electronic Health Records (EHR) being collected in a variety of systems, not all of which are interoperable. Another set of silos contains the ever-increasing volumes of bioinformatics. Yet another set of silos is the environmental context. With environmental context coming from a wide variety of sources from socio-economic and demographic sources such as the census, to meteorological analyses, to remote sensing from multiple satellites, to environmental station networks, to aerial vehicles and the exciting new area of wearable sensors. A set of case studies with societal benefit will be presented.
Distinguished Professor of Mathematics,
This special event is sposnsored by the UTD/SMU SIAM Student Chapter
Good Choices for Great Careers
The choices that scientists make early in their careers will impact them for
a lifetime. I will use the experiences of scientists who have had great
careers to identify universal distinguishing traits of good career choices
that can guild decisions in education, choice of profession, and job
opportunities to increase your chances of having a great career with
long-term sustained accomplishments.
I ran a student internship program at Los Alamos National Laboratory for
over 20 years. Recently, I have been tracking the
careers past students and realized that the scientists with great careers
weren't necessarily the top students, and that some of the most brilliant
students now had some of the most oh-hum careers.
I will describe how the choices made by the scientists with great careers
were based on following their passion, building their talents into a
strength supporting their profession, and how they identified a supportive
engaging work environment. I will describe some simple guidelines that can
help guide your choices, in school and in picking the right job that can
lead to a rewarding career and more meaningful life.
The topic is important because, so far as I can tell, life is not a trial
run - we have one shot to get it right. The choices you are making right
now to planning your career will impact your for a lifetime.
Please join us for an engaging discussion on how to make the choices that
will lead to a great career.
Department of Mathematics,
Southern Methodist University
A treecode-accelerated boundary integral Poisson-Botlzmann solver: modeling, algorithm and application
The Poisson-Boltzmann model is an extended model of the classical Gauss's law involving additionally multiple dielectrics (thus interface problem), solvent effects (thus continuum model) and dissolved electrolytes (thus nonlinearity). In
this talk, I will introduce our recent progress in developing a numerical Poisson-Boltzmann solver with
tree-code algorithms (for efficiency) and boundary integral formulation (for accuracy).
Following that, I will briefly touch the attractive performance computing feature of this solver including
parallelization and GPUs. I will conclude the talk with a report for the potential and established application
of the Poisson-Botlzmann model/solver for computing quantities with biological significance such as
electrostatic solvation energy, electrostatic forces, pKa values, etc.
Department of Materials Science and Engineering,
The University of Texas at Dallas
Analysis of Imaging Mass Spectrometry Data
Imaging Secondary Ion Mass Spectrometry (SIMS) experiments yield large data sets that are challenging to analyze. We will discuss the quantitative extraction of chemical concentration profiles, component spectra, and other information from such data. This will be done in the general framework of maximum a posteriori (MAP) reconstruction against physically motivated models, rather than through statistical dimensionality-reduction techniques such as Principal Component Analysis. Numerical techniques used to solve the resulting problem include alternating-least-squares iteration and simulated annealing. Selected topics will be presented, including the application of Gibbs priors for image regularization, extension of current analyses to include nonlinearities, and extraction of topographic information from high-mass-resolution images.
Department of Electrical Engineering,
The University of Texas at Dallas
The Many Levels of Semiconductor Device Physics
I will present a very quick tour of the logical chain that is
presumed to exist between fundamental physics and active device
technology, showing how the mathematical structures change
with physical length scale, and illustrating the behavior at
each level with simple numerical solutions and simulations.
The macroscopic description consists of a set of partial
differential equations. The point to be made is that the
the conventional procedure (taking those equations to define
a problem in modern analysis and constructing numerical solutions
as dictated by asymptotic error analysis) produces a confidence
in the results which is entirely unwarranted. Discrete numerical
models, constructed by the simplest possible procedures directly
from the physical system, come much closer to the true
behavior of the device, because the discretization "errors" tend
to correct the deficiencies in the macroscopic equations.