eScience workshop • december 2008
rosalind reid
executive director
harvard initiative in innovative computing
can the next generation of
scientists become
“computational thinkers”?
Fact 1: Computation will be at the core of all
science within the next decade.
Fact 2: Today’s undergraduates are
tomorrow’s research scientists.
Fact 3: Computational thinking generally is
not integrated into undergraduate science
curricula at Harvard.
Is this a problem?
We asked the faculty. (At Harvard, always
ask the faculty.)
narrative responses to an
informal online survey* of
Harvard science faculty on
“computational thinking”
* conducted June 2008
modest hypothesis: computational thinking (as
defined by Jeannette M. Wing*) can be a
unifying theme for catalyzing curriculum
innovation to improve the preparation of
tomorrow’s scientists.
*of Carnegie Mellon University, now in charge of Computer and Information
Sciences and Engineering (CISE) at NSF
Wing’s examples of
computational thinking in
science*

“machine learning has revolutionized
statistics”

“algorithms and data structures,
computational abstractions and
methods will inform biology”
*Microsoft Faculty Summit, Hangzhou, China, October 31, 2005
notes on these data

quantitative data were collected, but the survey was
informal and unscientific

faculty from several departments took the time to offer
thoughtful comments

respondents self-labeled their fields of research

special thanks to Lynn Stein for her help, to Microsoft
Research for funding, and to Rob Lue for his continuing work
to organize conversation among science faculty on these
questions

and thanks to the EECS faculty for an open and supportive
discussion of co-teaching possibilities
q1: We are interested in how
computation has or has not transformed
research in your field.

theoretical mechanics/earth science: “People with
[deep, fundamental understanding of ... science and math]
are able to do marvelous things with modern computation.”

climate: “Computation is a matter of necessity... as realscale experiments are not possible.”

earth science: “Numerical solution of large-scale
problems...crystal structures, high-pressure phases.”

language/cognition: “More precise and rigorous
formulation and testing of theories... large-scale databases
can be analyzed for patterns of human behavior.”
q1: We are interested in how
computation has or has not transformed
research in your field.

cosmology(n=2): [Computation is] “the engine of progress
in our field.” “High-end computation has become both
necessary and critical for data analysis”

geophysics: “My group.... is doing science that other
groups can’t because we have embraced a computational
approach... computational geometry and [GUIs] enable us
to build and run more realistic models.”

materials/surfaces: “Computation makes it possible to
‘see’ molecular detail.”

astrophysics: “Totally transformed.”
q1: We are interested in how
computation has or has not transformed
research in your field.

paleobiology: “Forward modeling, simulation of complex
systems that cannot be addressed analytically, solutions to
NP-complete problems such as DNA sequence
alignment....”

nanotechnology: “Computerized data acquisition; data
analysis; graphic presentation... of data; simulations of
experiments; fundamental understanding of electrons inside
small structures....”

evolutionary biology: “analyses of large data sets.”

evolutionary developmental biology: “As more and
more genomic sequence data becomes available,
computational methods are necessary to deal with the
data.”
q1: We are interested in how
computation has or has not transformed
research in your field.

paleobiology: “Forward modeling, simulation of complex
systems that cannot be addressed analytically, solutions to
NP-complete problems such as DNA sequence
alignment....”

nanotechnology: “Computerized data acquisition; data
analysis; graphic presentation... of data; simulations of
experiments; fundamental understanding of electrons inside
small structures....”

evolutionary biology: “analyses of large data sets.”

evolutionary developmental biology: “As more and
more genomic sequence data becomes available,
computational methods are necessary to deal with the
data.”
q1: We are interested in how
computation has or has not transformed
research in your field.

exoplanets: “The discovery of exoplanets was enabled by
sensitive optical detectors and by the ability to undertake
massive modeling efforts... and identify best models over a
large parameter space.”
q2: What types of computational thinking
do you expect to become important to
scientific investigation in the coming decade?

theoretical mechanics/earth science: “...intelligent
algorithms and a deep understanding of aspects of the physics
that cannot be represented accurately due to limitations on
computer resources.”

climate: “...how to reduce and abstract a real-world problem
into a computationally solvable problem... and how to map the
results back to the real-world problem.”

earth science: “...numerical solution of problems, use of tools
such as MATLAB and Mathematica...”

language/cognition: “Intelligent searching and parsing of
language databases.”
q2: What types of computational thinking
do you expect to become important to
scientific investigation in the coming decade?

theoretical mechanics/earth science: “...intelligent
algorithms and a deep understanding of aspects of the physics
that cannot be represented accurately due to limitations on
computer resources.”

climate: “...how to reduce and abstract a real-world problem
into a computationally solvable problem... and how to map the
results back to the real-world problem.”

earth science: “...numerical solution of problems, use of
tools such as MATLAB and Mathematica...”

language/cognition: “Intelligent searching and parsing of
language databases.”
q2: What types of computational thinking
do you expect to become important to
scientific investigation in the coming decade?

cosmology (n=2): “... ability to exploit large databases...
write, debug and run programs. Proficiency with a scripting
language. “The ability to conceptualize (and visualize) large
data sets.”

geophysics: “General procedural programming... models and
data visualization... ”

materials/surfaces: “ simulations of complex systems...
solving mathematically intractable problems....manipulating
datasets... capturing time-dependent phenomena.”

evo-devo biology: “the ability to compare many genomes at
once”
q2: What types of computational thinking
do you expect to become important to
scientific investigation in the coming decade?

cosmology (n=2): “... ability to exploit large databases...
write, debug and run programs. Proficiency with a scripting
language. “The ability to conceptualize (and visualize) large
data sets.”

geophysics: “General procedural programming... models and
data visualization... ”

materials/surfaces: “ simulations of complex systems...
solving mathematically intractable problems....manipulating
datasets... capturing time-dependent phenomena.”

evo-devo biology: “the ability to compare many genomes at
once”
q2: What types of computational thinking
do you expect to become important to
scientific investigation in the coming decade?

exoplanets: “novel analyses... of temporal variability surveys
and [categorization of] the variability in these large data sets.”

paleobiology: “...a wealth of more complex, easy-to-use
packages that can handle Bayesian analyses...”

nanotechnology: “..techniques...to handle many [processors]
at once.”

evo-devo biology: “...analyses of large data sets... smart
systems that bring together relevant data from disparate
sources.”
q3: What computational skills and abilities
would allow today’s undergraduates to
tackle tough problems in your field 10 or 20
years from now?

geophysics: “...general programming skills are the key
that allow tomorrow’s researchers to create their own tools...
and think differently.”

materials/surfaces: “...both applied math and skill in
numerical simulations and manipulations...”

astrophysics: “ability to use whatever programs are
standard [and] be able to modify them.”

cosmology “...what is becoming harder and harder is to
get students to understand the very basics of how
astronomical data is collected.”
q3: What computational skills and abilities
would allow today’s undergraduates to
tackle tough problems in your field 10 or 20
years from now?

geophysics: “...general programming skills are the key
that allow tomorrow’s researchers to create their own tools...
and think differently.”

materials/surfaces: “...both applied math and skill in
numerical simulations and manipulations...”

astrophysics: “ability to use whatever programs are
standard [and] be able to modify them.”

cosmology “...what is becoming harder and harder is to
get students to understand the very basics of how
astronomical data is collected.”
q3: What computational skills and abilities
would allow today’s undergraduates to
tackle tough problems in your field 10 or 20
years from now?

evo-devo biology: “statistical analysis, programming,
large-dataset management.”

paleobiology: “the big problems, the importance of first
principles”

nanostructures: “pattern recognition in the most general
sense”

evolutionary biology “... familiarity with... ‘informatics’
approaches”
miscellaneous comments

“Programming seems here to stay.” geophysics

“The larger problem is eliminating innumeracy among
Harvard undergrads. I routinely have students in my core
class that are marginally able, or unable, to deal with
quantitative material.” earth science

“The major problem with this [“computational thinking”]
approach is that it is concerned with teaching skills, rather
than building a CV for medical/professional school, and is
thus a slightly unusual vector for our undergraduates.”
materials/surfaces
miscellaneous comments

“You know, ironically, students are beginning to lose track
of the fundamentals that underlie the computational tools
they are using.” paleobiology

“These subjects [applied math and numerical simulation]
are difficult, and Harvard undergrads are not terrifically fond
of difficult subjects.” materials/surfaces

“Harvard... is the perfect place to pursue this type of
education.” geophysics
some conclusions

Computing challenges in the sciences will focus on large
data sets, but not just on large data sets.

The ability to bring data together from disparate sources
will be increasingly critical.

Some faculty fear that students using sophisticated tools
will lose touch with first principles or the understanding of
nature that comes from direct observation and
experimentation.

There is concern about levels of quantitative skill among
science students and cynicism about motivation.

Scale is seen as a growing challenge across the sciences,
and computational skill as necessary for meeting that
challenge.
experimentation at Harvard

research experiences provided by IIC (18 internships, 4
REUs in first 3 years)

physical sciences and life sciences now have integrated
first-year courses

first winter session: January 2010

new undergraduate laboratories will combine wet labs with
computer labs

IIC Director Efthimios Kaxiras convening interdisciplinary
faculty committee to launch co-teaching workshops

planned addition of science projects to CS 50/51; new
numerical methods courses in School of Engineering and
Applied Sciences (lack of departmental boundaries helps!)
what can “computational
thinking” not do for science?
replace observation; scientists must first
“take their dictation from Nature”
provide young scientists knowledge of
science’s laws, principles and method
what can “computational
thinking” do for science?
help conceptualize, manipulate and analyze
novel and large databases
lead to different formulations of theory
cleave observations/data/interactions/natural
systems into computable pieces; abstract
them; represent and model them; map
results back to the real world