Transcript Biological Sequence Data and Databases

Computational Science
Vision for the Future of Science:
Excellence through Interdisciplinary
Research and Education
Susan Keun-Hang Yang, PhD
Professor, Computational Science
Director, International Science Programs
Chapman University
Agenda
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Chapman’s Schmid College of Science
 Computational Science at Chapman
University
Classical Science
 Computational Science
 Data Science
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Chapman’s Schmid College of Science:
Rationale for Interdisciplinary Science Programs
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Science is today critical to most of societies’
problems
Understanding science at a level that will allow
general appreciation and assist someone to be a
better citizen, and more productive in her work, will
be crucial as society’s issues become more complex
Chapman is known for its individualized, competent
training in several fields: Interdisciplinary science
focused in computational science will be developed
as part of what Chapman is known for nationally
and internationally
We have a real opportunity to work with other
schools such as Business and Law in providing their
students understanding in the sciences and give
them an edge over their competitors
Build Computational Science Infrastructure at
Chapman University
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RS Lab (12 nodes)
S/W: ENVI, GIS, etc. (H. El-Askary, C. Kim, A. Prasad, M.
Kafatos CU; X. Liu, IGC)
 Modeling capabilities (C. Tremback, A. Prasad, TBD Computer
Engineer)
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CU Modeling Lab (10 node PC-cluster, 2 servers)
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RAMS (with G. Kallos, Univ. Athens, CU; C. Tremback, ATMET,
CU)
ICLAMS (with G. Kallos, Univ. Athens, CU; C. Tremblack,
ATMET, CU)
WRF (Community model)
Hydrology Modeling (S.K. Park, Ewha Womans Univ.)
CU Climate Scenarios
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IPCC Global Model Scenarios for future
Chapman Science Intends to be
Internationally Known in Key Science Areas
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Only SUNY Brockport offers undergraduate degrees in
computational science
Only Chicago, Michigan (and 3 more) have programs in
computational finance
Chapman will create strong ties with local industries and
universities for applied research projects
We will establish national leadership in several key areas such as:
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Undergraduate degrees in computational science
Computational biology and biotechnology
Applications of quantum theory
Computational mathematics (wavelets, statistics, etc.)
Applied research in hazards (earthquakes, forest fires, floods, droughts,
pollution, etc.)
Top notch undergraduate research
M.S. and Ph.D. in computational science
Explore feasibility of interdisciplinary programs such as computational finance,
film and computers, etc.
Chapman just recruited a top team of scientists to
its faculty, including:
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Yakir Aharonov: Internationally known Physicist, nominated
for Nobel Prize; received Wolf Prize, etc.
Foundations of quantum theory, weak measurements,
Aharonov-Bohm effect (quantum non-locality); discovered
more than 30 effects named after him; member of the
National Academy of Sciences, received numerous prizes
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Jeff Tollaksen: Aharonov’s collaborator and Chair
Department of Physics, Computational Science and
Engineering
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Susan Keun-Hang Yang: Director, International Science
Programs, renown expert in computational biology and
neuroscience, Computational biology, computational
neuroscience, biochemistry, bioinformatics, electrophysiology
Menas Kafatos
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Earth system science
Aerosols
Hazards and climate change
Computational science
Quasar redshifts and cosmological models
Active galactic nuclei
Black holes and general relativity
Fluid motions in curved spacetime
Quantum theory and Cosmology
Consciousness and quantum theory
Wealth of research experience and international reputation, administrator
(dean, director and principal investigator of large, muti-member projects);
founder of many innovative educational programs, including computational
science, global and environmental change, etc. Author of 12 books and
290 articles. Participates in international programs in Korea, Greece, Egypt,
World Meteorological Organization, member of Romanian Academy of
Sciences, member of National Academy, National Science Foundation and
NASA panels.
Hesham el-Askary
Earth system science
Aerosols
Pollution
Remote sensing
Dimitar Ouzounov
Earthquakes Fires
Predicted within days of its occurrence the
devastating earthquake that rocked southern China
in May 2008
Eyal Amitai
Hydrology , Precipitation, Floods in Orange County
Principal collaborator on The Global Precipitation Measurement,
joint U.S.-Japanese mission
Ramesh Singh
Remote Sensing, Aerosols, Pollution
Proposed Structure of New Graduate
Science Degree Programs
M.S. Hazards and Global Environmental Change
– Core, 13 Credits
– Electives, 18 Credits; or
– Electives, 15 Credits; Thesis, 3 Credits
M.S. Computational Science
– Computational Biology/Biotechnology Track
– Computational Mathematics Track
– Modeling and Data in Earth System Science
Ph.D. Computational Science
– Core: Scientific Computing; Scientific Databases; Visualization; Numerical Techniques
– Science Tracks; and Tracks with other Colleges
– Doctoral Thesis
Chronicle of Higher Education
(September 4, 2009)
Public Health
Health
Informatics
Computationa
l Science
Service
Science
Sustainability
Chronicle of Higher Education: “5 College Majors on the Rise”
School of Health Sciences
School of Computational
Sciences
School of Environmental
Sciences/Global Change
Theoretical, Laboratory and
Computational Science
Theory
Experiment
Computing
Two Classical Phases of Science
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Theoretical Science (e.g. the
formulation of the general theory of
relativity; brain dynamics)
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Experimental/Laboratory Science (e.g.
the development of experiments to
confirm or deny general relativity
theory: deflection of light during a
total solar eclipse; fMRI)
The Third Phase of Science:
Computational Science
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When a fully formed theory does not exist
(e.g. we do not fully understand what
causes cancer)
When a theory exists but cannot be applied
because of its complexity (e.g. we fully
understand the theory of hurricanes, but the
physics of turbulence is too complex)
When experiments are impossible or too
cumbersome (e.g. experiments with blood
flow in humans, etc.)
Computational Science
Computational science is a fairly new
discipline which is defined as:
“Computational science (or scientific
computing) is the field of study concerned
with constructing mathematical models and
numerical solution techniques and using
computers to analyze and solve scientific,
social scientific and engineering problems.
In practical use, it is typically the
application of computer simulation and
other forms of computation to problems in
various scientific disciplines.”
Computational Science : Frontier Approaches
for Applied Science & Engineering
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Computational Science
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Distinct from computer science (the mathematical study
of computation, computers and information processing).
Scientific computing is to gain understanding through
the analysis of mathematical models implemented on
computers.
Scientists and engineers develop computer programs,
application software, that model systems being studied
and run these programs with various sets of input
parameters. Typically, models require massive
calculations (usually floating-point) and are executed on
supercomputers or distributed computing platforms.
Numerical analysis is an important underpinning for
computational science.
Programming languages commonly used for the more
mathematical aspects of scientific computing
applications include Fortran, MATLAB, SciLab, GNU
Octave, COMSOL Multiphysics, and PDL. The more
computationally-intensive aspects of scientific
computing often utilize some variation of C or Fortran.
Computational Science
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Computational science application programs
often model real-world changing conditions,
such as weather, air flow around a plane,
automobile body distortions in a crash, the
motion of stars in a galaxy, an explosive device,
etc. Such programs might create a 'logical mesh'
in computer memory where each item
corresponds to an area in space and contains
information about that space relevant to the
model. For example in weather models, each
item might be a square kilometer; with land
elevation, current wind direction, humidity,
temperature, pressure, etc. The program would
calculate the likely next state based on the
current state, in simulated time steps, solving
equations that describe how the system
operates; and then repeat the process to
calculate the next state”.
Computational Science
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Therefore, computational science involves both
the computing methodology and the scientific
applications. Applied mathematics, numerical
analysis as well as computational methods are
all part of scientific computing or computational
science. Established areas of computational
science include applications in computational
fluid dynamics, atmospheric science, seismology,
chemistry, global ocean/climate modeling,
environmental studies, physics, with emerging
applications in bioinformatics, neuroscience,
economics, etc. The list continues to grow.
Computational Science
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Many experiments and investigations that
have traditionally been performed in a
laboratory, a wind tunnel, or the field can
now be performed using computers. While
some studies, such as global climate change,
involve time scales that preclude the use of
realistic physical experiments.
Data Science:
Data Mining and Massive Data Sets
Massive data sets are expanding
exponentially across several fields
 “On demand” access is expected by
diverse user communities K-12,
undergraduate and graduate students,
policy makers, the general public
 Data access and analysis are essential
 Data mining looks promising to
reduce the volume and complexity of
available data
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Data Science: The Challenge of
Exploding Information
Data
Volume
Data
Transfer
Rate
Number of
Scientists &
Engineers
Relevance of New Team to California
Aerosols
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Floods
Wildfires
Droughts
Earthquakes
Biotechnology
Bioinformatics
General computational tools
Mathematical tools
Data mining,
Engineering
computing
Classical Phases of Science
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Theoretical Science
Experimental/Laboratory
Computational science