can the next generation of scientists become “computational thinkers”?

can the next generation of scientists become “computational thinkers”? • eScience workshop • december 2008 • rosalind reid • executive director • harvard initiative in innovative computing

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 real-scale 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. • 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? • 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? • 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

can the next generation of scientists become “computational thinkers”?