Transcript Ralph Regula School of Computational Science

Steven I. Gordon
[email protected]
Computational Science
Education Programs
CASC Meeting
October 4,2012
Plan for the Morning
• Provide an overview of computational science related
programs
– Undergraduate programs in computational science
– Graduate programs in computational science
– Computer science programs focused on parallel and high
performance computing
– Professional development programs aimed at the current
workforce
• Panel discussion focused on problems and
prospects for developing and continuing these
programs
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Agenda
• Model Undergraduate Programs: Dr. Steven Gordon OSC
• Perspectives on Growing a Graduate Program in Computational
Science: Dr. Terry Moore, UTK
• Education & Training needs to fill the Missing Middle in Digital
Manufacturing: Dr. Ashok Krishnamurthy, OSC
• NSF/IEEE-TCPP Guidelines for an Undergraduate Core Curriculum:
Dr. Sushil Prasad, Georgia State
• TACC’s Comprehensive Scientific Computing Curriculum: Dr. Jay
Boisseau
• XSEDE Education Program & Formal Computational Science
Programs: Dr. Steven Gordon, OSC
• Panel discussion
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Overview
• Challenges to creating undergraduate programs in
computational science
• Minor program in computational science
• Associate degree program
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Challenges to Creating Programs in Ohio
• Computational science is interdisciplinary
– Faculty workloads fixed on disciplinary responsibilities
– Expertise at universities is spotty
– Major time commitments are required to negotiate a
new program
• No standards existed that defined the field
• Curriculum requirements for related fields leave
little room for new electives
• Change is hard
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Initial Focus in Ohio
• Call for faculty interest and participation
• Several meetings to discuss interests and possible
requirements
• Consensus that an undergraduate minor program
was a good place to start
• Joint application and award of NSF CI-Team
demonstration project
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Program Requirements
• Created a competency-based curriculum
– Provides detailed outlines of the background and skills
desired for students completing the program
– Bridged the differences across disciplines
– Allows for flexibility in implementation to fit the program
into multiple institutional situations and majors with
different backgrounds and focus areas
• Competencies can be a model for other programs
• http://www.rrscs.org/competencies
7
Education
Minor program overview
Competencies for Undergraduate
Minor
• Undergraduate minor program
– 6-8 courses
– Varies based on major
Simulation and Modeling
• Faculty defined competencies for
all students
Differential Equations and Discrete
Dynamical Systems
Programming and Algorithms
• Reviewed by business advisory
committee
Numerical Methods
• Program started in Autumn 2007
Parallel Programming
• Agreements to share students at
distance, instructional modules,
revenues, and teaching
responsibilities
Scientific Visualization
Optimization
One discipline specific course
Capstone Research/Internship
Experience
Discipline Oriented Courses
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Example Competencies Simulation and Modeling
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Explain the role of modeling in science and engineering
Analyze modeling and simulation in computational science
Create a conceptual model
Examine various mathematical representations of functions
Analyze issues in accuracy and precision
Understand discrete and difference-based computer models
Demonstrate computational programming utilizing a higher level
language or modeling tool (e.g. Maple, MATLABTM, Mathematica, other)
Assess computational models
Build event-based models
Complete a team-based, real-world model project
Demonstrate technical communication skills
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Detailed Descriptors
Explain the role of modeling in science and engineering
Descriptors:
Discuss the importance of modeling to science and engineering
Discuss the history and need for modeling
Discuss the cost effectiveness of modeling
Discuss the time-effect of modeling (e.g. the ability to predict the weather)
Define the terms associated with modeling to science and engineering
List questions that would check/validate model results
Describe future trends and issues in science and engineering
Identify specific industry related examples of modeling in engineering (e.g., Battelle; P&G,
material science, manufacturing, bioscience, etc.)
Discuss application across various industries (e.g., economics, health, etc.)
Example exercise
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Implementation
• Statewide collaboration agreement
– All students register through their home institution and pay local
tuition
– Transfer payment to universities hosting other students
– Registrars exchange information in background to get student
registered for remote courses and to transfer final grades
• Cross registration very modest
– Everyone voraciously guards their credit hours
– No tradition of cross-registration with other institutions
– Still a model with promise to allow shared use of scarce faculty
resources
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Associate Degree Program
• Results of an NSF Advanced Technology
Education Grant
• Program is an Associates in Science with a
concentration in Computational Science
• Goal to encourage students to complete a fouryear degree
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New Courses for the Curriculum
• Five new courses were designed for this new
program:
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Computational Science Methods
Modeling and Simulation
Introduction to Computational Biology
Introduction to Computational Chemistry
Introduction to Computational Physics
• These courses and all developed materials have
been shared among all schools participating in the
program
Education
Program Organization
• Also competency based
– http://www.rrscs.org/associate
• Participating institutions
– Central Ohio Technical College
– Sinclair Community College
– Stark State Technical College
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Summary
• The programs in Ohio can be used as the basis for
structuring other undergraduate programs
• Working through the XSEDE project, we are
assisting institutions with creating related
undergraduate and graduate programs
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Questions and Discussion
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