### Fall Meeting - Anschutz Medical Center
### Archive of messages
### Job opportunities

Fall Meeting Friday, October 26th, 2007 1 pm - 4 pm.


A final reminder, the Fall Chapter will be held at the Anshutz Medical
Center (formerly
Fitzsimons Hospital)   next Friday, October 26th from 1 to 4 pm.  This is
the first time we have held a meeting at this location.  There are several
reasons for holding the meeting at this location.

The talks described below cover a variety of topics both in and outside of
the health sciences field.  As always, we hope that these talks will appeal
to both those involved in the field as well as those outside. A tentative
agenda follows.

Starting at 1pm,

Laura Saba - University of Colorado Denver and Health Science Center
Calvin Croy - University of Colorado at Denver and Health Sciences Center
Manuel Lladser - University of Colorado - Boulder

Break with refreshments ~ 20 minutes

Dennis Lezotte -  University of Colorado Health Sciences Center
Derek Sondregger - Colorado State University

With the exception of Denny Lezotte's talk which is 45 minutes, the talks
will be approximately 25 minutes.  We will conclude at approximately
4pm.  With the exception of Manuel Lladser's talke, abstracts for most
of
the talks were included in the last note.

Manuel Lladser, Department of Applied Mathematics,  University of
Colorado at Boulder;

TITLE: Sufficient Markovian embeddings of non-Markovian random
sequences;

 ABSTRACT: Let A be a finite set and X a sequence of A-valued random
variables. We characterize the smallest state space size needed to analyze
the frequency statistics of a regular pattern of a k-th order Markovian
sequence X. The exponential growth of the state space as function of k
motivates to consider embeddings that take into account the actual
probabilistic parameters of the Markovian model of X.
Surprisingly, this can be done even for non-Markovian sequences and
non-regular patterns: for any transformation Q over finite length
sequences, we show there exists a unique coarsest refinement R of Q in a
certain class of transformations such that R(X_1), R(X_1,X_2),
R(X_1,X_2,X_3), etc is Markovian. (By coarsest refinement we mean that
R(u)=R(v) implies Q(u)=Q(v) and that R is a deterministic function of any
other refinement of Q that leads to a Markov process.) A toy
example of a non-Markovian sequence of 0's and 1's is analyzed
thoroughly and Discrete as well as Gaussian asymptotic distributions are
established for the number of occurrences of different regular patterns in
X_1, ..., X_n.

*** Directions

Directions and Parking Information can be found at
http://www.uchsc.edu/fitzsimons/maps .

The new center is located approximately at Colfax and Peoria Street - in
Aurora.  On the Anschutz Medical Campus, there are daily cash customer
parking lots
for UCDHSC Patients and Visitors:

Ignacio Lot (511) - located in back of the Administration Building (Building
500) on East 19th Avenue.  Neon orange signs posted on campus will also
direct you to the UCDHSC visitor parking lot. There are two pay and display
machines that are available to render payment.  These machines will accept
bill denominations up to $20.00 and coins. The flat $4 rate has been change
to the following:
   1 hour or less - $1
   1 – 3 hours - $2
   Over 3 hours - $4
After 5pm and weekends - $1

Lot 504 - located on the west side of Uvalda Ct. between 17th Place and 19th
Avenue is a metered lot for short term visitors. The rate is $1 per hour.
This lot was opened to replace the parking meters that used to be along 17th
Place in front of Building 500.


###  Note archives

Just a reminder, you can find an archive of messages at

http://mailman.ucar.edu/pipermail/cowystats/

### Job Opportunity

Fair Isaac Analytic Science, Westminster, CO
apply online at fairisaac.com or direct questions to tressafowler@fairisaac.com

Scientist II

Using state of the art tools, design and develop innovative data-driven
predictive and decision models based on neural networks, machine
learning, data mining, statistical modeling, pattern recognition, and
artificial intelligence, to support variety of business decisions in
the collections and recovery industry.

The main responsibilities include:
• Analyzing and understanding large amounts of historical data to
determine suitability for modeling
• In-depth data understanding and exploratory analyses
• Pattern identification and feature extraction and selection
• Analytic model design and development using different types of tools
and modeling techniques
• Analyzing model performance and preparing model reports for
communication with internal and external clients
• Participating in pre-sales process and providing post implementation support

Required Experience :  Minimum Qualifications
• BS in Mathematics, Statistics, Operations Research, or related
major. Min GPA 3.5
• Work experience in risk analysis, computer science or a related
technical field.
• Proficiency in programming skills.
• Strong problem solving and analytic ability
• Fluency in English.
• Excellent oral and written communication skills.
Preferred Qualifications:
• Domain knowledge in risk analysis.
• 2+ Years experience in Financial Industry
• Masters Degree in related field (Math, Computer Science, MBA)
• Working knowledge of C++ or Java, Python, UNIX and SQL Server


Scientist I

Using state of the art tools; design and develop innovative data-driven
predictive and decision models based on neural networks, machine
learning, data mining, statistical modeling, pattern recognition, and
artificial intelligence, to support variety of business decisions in
the collections and recovery industry.

The main responsibilities include:
• Analyzing and understanding large amounts of historical data to
determine suitability for modeling
• In-depth data understanding and exploratory analyses
• Pattern identification and feature extraction and selection
• Analytic model design and development using different types of tools
and modeling techniques
• Analyzing model performance and preparing model reports for
communication with internal and external clients
• Participating in pre-sales process and providing post implementation support

Required Experience :  Minimum Qualifications
• BS in Mathematics, Computer Science or a related technical field.
Minimum GPA of 3.5
• Familiar one or more of the following programming languages and/or
platforms (C++, Java, Python, UNIX, SQL Server, Windows)
• Strong problem solving and analytic ability
• Fluency in English
• Excellent oral and written communication skills.
Preferred Qualifications:
• Familiarity with Risk Analysis
• Coursework in Statistics and/or Business