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Wofford College's Emphasis in Computational Science


Textbook

Introduction to Computational Science:
Modeling and Simulation for the Sciences
(Second Edition)

by
Angela B. Shiflet and George W. Shiflet
© 2014 by Princeton University Press
ISBN: 978-0-691-16071-9


Recognition

Angela B. Shiflet

Recognition in Computational Science
The Carnegie Foundation for the Advancement of Teaching and the Council for
Advancement and Support of Education 2009 South Carolina Professor of the Year


Krell Institute Undergraduate Computational Engineering and Sciences (UCES) 2006 Award Winner


Additional Computational Science Modules

Educational Modules on High Performance Computing Bioinformatics Algorithms

with NCSI Blue Waters Intern

Aligning Sequences--Sequentially and Concurrently

by
Angela B. Shiflet1, George W. Shiflet1, Daniel S. Couch1,
Pietro Hiram Guzzi2, Mario Cannataro2
1 Wofford College, Spartanburg, SC, USA
2 University “Magna Græcia” of Catanzaro, Catanzaro, Italy
Copyright © 2016

Using Gene Ontology Databases—Sequentially and Concurrently

by
Angela B. Shiflet1, George W. Shiflet1, Daniel S. Couch1,
Pietro Hiram Guzzi2, Mario Cannataro2
1 Wofford College, Spartanburg, SC, USA
2 University “Magna Græcia” of Catanzaro, Catanzaro, Italy
Copyright © 2016

What Are the Chances?—Hidden Markov Models

by
Angela B. Shiflet1, George W. Shiflet1, Dmitriy A. Kaplun1,
Pietro Hiram Guzzi2, Mario Cannataro2
1 Wofford College, Spartanburg, SC, USA
2 University “Magna Græcia” of Catanzaro, Catanzaro, Italy

The module is a chapter in Mathematical and Computational Biology, ed. Hannah Callender Highlander, Carrie Diaz Eaton, and Alex Capaldi, Birkhauser, 2019. The book is a volume in the series Foundations for Undergraduate Research in Mathematics.

Abstract: Hidden Markov Models (HMMs) can be used to solve a variety of problems from facial recognition and language translation to animal movement characterization and genes discovery. With such a problem, we have a sequence of observations that we are not certain is correct—we are not sure our observations accurately reveal the corresponding sequence of actual states, which are hidden—but we do know some important probabilities that will help us. In this chapter, we will develop the probability theory and algorithms for two types of problems that HMMs can solve—calculate the probability that a particular sequence of observations occurs and determine the most likely corresponding sequence of hidden states. The chapter will end with a collection of research projects appropriate for undergraduates.

Instructors can obtain implementations of the sequential algorithm in C and the parallel algorithms in C with MPI from https://ics.wofford-ecs.org/ or Angela Shiflet.

NCSI Undergraduate Petascale Education Modules

by
Angela B. Shiflet and George W. Shiflet
Copyright © 2009 - 2012

Biofilms: United They Stand, Divided They Colonize

Getting the “Edge” on the Next Flu Pandemic: We Should'a “Node” Better

Living Links: Applications of Matrix Operations to Population Studies

Time after Time: Age- and Stage-Structured Models

Modeling an “Able” Invader - the “Cane” Toad

Probable Cause: Modeling with Markov Chains

SIGCSE 2007 Nifty Assignment

by
Angela B. Shiflet
Copyright © 2007

Spreading of Fire

Data and Visualization Site Modules


Special Thanks


Drs. Shiflet

Copyright © 2002 - 2019, Dr. Angela B. Shiflet
All rights reserved

The initial work was supported by National Science Foundation grant DUE-0087979.
All opinions, findings, conclusions, and recommendations in this work are those of the author and do not necessarily reflect the views of the National Science Foundation.