Tutorial CANCELLED
If you have registered
for this course, a refund will be issued to you after the
meeting. If any questions about your refund, please contact
the MMSB at mmsb@ams.org. We apologize for any inconvenience
this may have caused you.
Sunday, January 4, 2009
9:00 a.m. to 4:30 p.m.
Washington Room 3, Marriott Wardman Hotel
Introduction and Description
This tutorial will be an introduction to both numerical and
statistical modeling for those who are not currently studying
computational science. It is especially designed for individuals
who are considering nonacademic employment.
The tutorial will be divided into two sessions. During the
morning session, Professor ChiWang Shu of the Division
of Applied Mathematics, Brown University, will present an
introduction to numerical modeling. In the afternoon, Professor
Wei Zhu, Department of Applied Mathematics and Statistics,
State University of New York at Stony Brook, will give an
introduction to statistical modeling.
Introduction to Numerical Modeling
January 4, 2009
9:00 a.m. to 12:00 noon
Professor ChiWang Shu
Division of Applied Mathematics
Brown University
In today's job market, Ph.D. students in mathematics might
improve their chances of getting employments if they consider
jobs outside the traditional academic job market. Students
who have expertise in numerical modeling are marketable to
many different types of nonacademic employers, including
government research labs, research labs of large companies
(such as the oil companies), and various computer software
companies (such as those who write software for medical science
and health industry or for financial markets). Even for students
whose major expertise is not in numerical modeling, some knowledge
of numerical modeling should enhance the chance to obtain
employment from many nonacademic employers.
This 3hour tutorial is intended for graduate students who
are not working in areas related to computational science
(numerical analysis, scientific computing, computational engineering,
etc.), but would be interested in an introduction to some
fundamental ideas for entry level numerical modeling.
The tutorial will start with a general description of numerical
modeling, and will explain the difference between a student
in a mathematics department majoring in computational science
and a student in computer science. While programming (in C
or another language) is a necessary skill for any student
in numerical modeling, it is the mathematical insight which
allows mathematicians to design and improve algorithms that
are stable, accurate and efficient for various applications.
The tutorial will then move to the description of a few selected
topics in numerical modeling, including the solutions of large
linear systems, the approximations of ordinary differential
equations, and finite difference, finite element, and spectral
methods for approximating partial differential equations.
While it is impossible to give an indepth coverage of so
many topics within three hours, we will emphasize the fundamental
concepts such as stability, accuracy and efficiency for these
algorithms.
The tutorial will end with a list of references which will
allow interested audience to follow up to gain more indepth
knowledge of the exciting area of numerical modeling.
The lecturer, Professor ChiWang Shu, has trained
over twenty Ph.D.s at Brown University who are now employed
both by academic and by nonacademic employers. His research
expertise is in scientific computing. In 2007 he was awarded
the SIAM/ACM Prize in Computational Science and Engineering
jointly by the Society for Industrial and Applied Mathematics,
the major society for applied and computational mathematicians,
and by the Association for Computing Machinery, the major
association for computer scientists.
Introduction to Statistical Modeling
January 4, 2009
1:30 p.m. to 4:30 p.m.
Professor Wei Zhu
Department of Applied Mathematics and Statistics
State University of New York at Stony Brook
Statistics is a branch of the mathematical sciences that
pertains to the collection and analysis of data. The goal
of statistical inference is to make a probabilistic statement
about the underlying population based on the given sample.
A statistical model is usually one or a set of stochastic
equations (often linear) linking the relevant population parameters
to the data observed. For example, one may wish to establish
a simple linear regression model predicting the height of
a son based on the height of his father. To estimate the regression
line, one can simply employ the least squares method developed
by Gauss and Legendre. So far, it is all mathematics. The
role of statistics is to make the subsequent statistical inference
(including confidence intervals and hypothesis tests), estimating,
for example, the range of the son's height with some certainty,
say 95% confidence, for a given paternal statue. An immediate
improvement upon such a model would be the addition of the
mother's height as another predictor. With two predictors
(often called regressors in statistics), one can now expand
the simple linear regression model into a multiple linear
regression model.
In this 3hour workshop intended for Joint Mathematics Meetings
attendees who wish to broaden their horizons (and job market
if pertinent) by learning more statistics, we will start by
introducing some popular probability problems from the Wall
Street job interviews. We will then focus on some basic and
yet most commonly used statistical models including the linear
regression model, the logistic regression model, and the autoregressive
time series model. We will conclude our workshop with an overview
of major branches of modern statistics  a list of reference
books corresponding to these subjects will also be provided
for the interested audience.
This workshop will be given by Professor Wei Zhu from
the State University of New York at Stony Book. Professor
Zhu has a B.S. in mathematics and a Ph.D. in Biostatistics.
For the past 10 years, she has applied statistics to a wide
spectrum of problems including brain imaging analysis, disease
pathway studies, climate modeling and optimal design of experiments.
She is an active educator whose former Ph.D. students are
currently working in academia, and the pharmaceutical, internet
and financial industries. She is the Data Core Director of
the Alzheimer's Disease Research Center at New York University.
She also collaborates closely with the Brookhaven National
Laboratory and the Cold Spring Harbor Laboratory.
Registration
The fee for this tutorial is US$25. It is not necessary to
register for the Joint Mathematics Meetings (JMM) in order
to participate in the tutorial. Registration for the tutorial
must be done separately. Please register by sending email
to the Mathematics Meetings Service Bureau (MMSB) at mmsb@ams.org
and include your name, address, phone number and method of
payment. If you would like to pay by check, please register
first with the MMSB and then send the check by mail to: Mathematics
Meetings Service Bureau, P.O. Box 6887, Providence, R.I.,
029406887. If you are already registered for the JMM, please
indicate this in your email so that this registration can
be added to your current record.
Information
If you have additional questions or concerns about the tutorial,
please contact Ellen Maycock at 18003214267, Ext. 4101
or ejm@ams.org.
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