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Pat Langley Computational Learning Laboratory Center for the Study of Language and Information Stanford University, Stanford, California

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Presentation on theme: "Pat Langley Computational Learning Laboratory Center for the Study of Language and Information Stanford University, Stanford, California"— Presentation transcript:

1 Pat Langley Computational Learning Laboratory Center for the Study of Language and Information Stanford University, Stanford, California http://cll.stanford.edu/~langley A Vision for Computational Science

2 A Day in the Life of a Future Scientist Professor Jones comes into her office on Tuesday morning. Her first action is to check the status of an experiment she submitted the night before. Her computerized assistant reports the results for culture growth over time under 20 different conditions, displaying each curve in relation to the current models predictions. The system highlights two conditions in which results diverged from those expected.

3 A Day in the Life of a Future Scientist Professor Jones comes into her office on Tuesday morning. Her first action is to check the status of an experiment she submitted the night before. Her computerized assistant reports the results for culture growth over time under 20 different conditions, displaying each curve in relation to the current models predictions. The system highlights two conditions in which results diverged from those expected. Dr. Jones asks the assistant if it has any explanations to propose for the two anomalies, and the system returns a list of ten alternatives, ranked by fits to the data and consistency with knowledge of the field. The researcher asks the computer aide if the literature contains other reports of either similar results or similar hypotheses. It recommends five papers in response, and she spends the next hour reading the two that seem most relevant.

4 A Day in the Life of a Future Scientist Professor Jones comes into her office on Tuesday morning. Her first action is to check the status of an experiment she submitted the night before. Her computerized assistant reports the results for culture growth over time under 20 different conditions, displaying each curve in relation to the current models predictions. The system highlights two conditions in which results diverged from those expected. Dr. Jones asks the assistant if it has any explanations to propose for the two anomalies, and the system returns a list of ten alternatives, ranked by fits to the data and consistency with knowledge of the field. The researcher asks the computer aide if the literature contains other reports of either similar results or similar hypotheses. It recommends five papers in response, and she spends the next hour reading the two that seem most relevant. After some thought, Dr. Jones tells her assistant to focus on the three model revisions she feels are most plausible and asks it to design a new experiment that will discriminate among them. The researcher makes a few changes to its proposed conditions and submits the design for robotic execution when the resources become available. She leaves the office in time to walk across campus to attend a weekly committee meeting.

5 The Computer Revolution communicate with each other communicate with each other learn new facts learn new facts listen to music listen to music make travel plans make travel plans shop and make purchases shop and make purchases bank and pay bills bank and pay bills prepare and give presentations prepare and give presentations In recent years, information technology has changed the way we: Computers support all of these activities by storing, retrieving, processing, and interchanging information in digital form.

6 The Scientific Revolution the universe the universe the Earth the Earth matter matter life life disease disease the mind the mind society society The much older scientific revolution has greatly increased our understanding of: Science supports such advances by collecting data systematically, stating clear theories/models, and relating them to each other.

7 Science and Computation This approach has already produced some impressive advances in a number of scientific fields. science computation computational science Clearly, the potential for combining these important revolutionary movements is greater than either in isolation.

8 Collection / Analysis of Sky Surveys

9 Simulation / Visualization of Fluid Dynamics

10 Mining Data from Earth-Observing Satellites

11 Analysis of Genetic Sequences

12 Clustering of Gene Expressions

13 Recording / Analysis of Brain Activity

14 Indexing / Retrieval of Scientific Papers

15 The Importance of Computational Science Universities... must make coordinated, fundamental, structural changes that affirm the integral role of computational science in addressing the 21 st centurys most important problems, which are predominantly multidisciplinary... and collaborative. These are each important contributions, but they only touch on the full potential of computational science. A recent report from the Presidents Information Technology Advisory Committee stated: We need a systematic vision for computational science, a broad research agenda, and clear plans for educating the next generation.

16 Facets of Computational Science

17 A Broader Definition the use of computational methods and metaphors the use of computational methods and metaphors to understand and support the scientific enterprise. to understand and support the scientific enterprise. In its broadest sense, we can define computational science as: This requires that we understand the ways in which: content disciplines pose computational problems; content disciplines pose computational problems; method disciplines offer computational solutions. method disciplines offer computational solutions. Computational science attempts to relate these two areas, just as science relates data to theory.

18 astronomy biology chemistry Earth science materials science medicine physics Content-Oriented Disciplines Computational sciences problems come from content fields: These disciplines stand to benefit from computational science, but they also provide data, theories, and other content. computational science

19 Method-Oriented Disciplines Computational sciences techniques come from method fields: These fields provide the underlying processes that computational science uses to aid research in the content disciplines. computational science computer science mathematics decision analysis engineering logic phil. of science statistics astronomy biology chemistry Earth science materials science medicine physics

20 Additional Content Disciplines The social sciences also have roles to play as content fields: anthropology economics education geography history psychology sociology computational science computer science mathematics decision analysis engineering logic phil. of science statistics These disciplines stand to benefit from computational support as much as the natural sciences.

21 Scientific Representations and Structures Computational science should study the full range of content that scientific fields must represent, including: Scientific disciplines would benefit from the ability to encode and store each of these structures in digital form. taxonomies / ontologies descriptive laws theories / models predictions/explanations experimental designs records / databases documents / images computational science

22 Scientific Processes and Mechanisms Computational science should also study the development and utilization of mechanisms for: Scientific disciplines would benefit from the ability to emulate these processes on computers. taxonomies / ontologies descriptive laws theories / models predictions/explanations experimental designs records / databases documents / images computational science predict / simulate explain phenomena evaluate hyps / models propose hyps/models devise instruments design experiments record / index results form taxonomies communicate results

23 Contributions from Computer Science Computational science should also draw upon key subfields of computer science: data structures / knowledge representation data structures / knowledge representation computer simulation computer simulation programming languages programming languages database / information retrieval database / information retrieval remote sensing / sensor networks remote sensing / sensor networks data mining / knowledge discovery data mining / knowledge discovery human-computer interaction human-computer interaction Each of these areas can support essential components of the scientific process.

24 Claims About Computational Science

25 Science as Computation Claim: Science can be viewed as an interconnected set of computational processes. According to this framework, we can understand science by: analyzing the tasks that arise in scientific research; analyzing the tasks that arise in scientific research; studying the behavior of historical and modern scientists; studying the behavior of historical and modern scientists; creating computational artifacts that address the same tasks. creating computational artifacts that address the same tasks. Two fields with this view – artificial intelligence and cognitive psychology – are especially relevant to computational science.

26 Science as Heuristic Search Claim: Science can be characterized as search through one or more problem spaces. According to this framework, we can understand science by: identifying knowledge states that arise in scientific research; identifying knowledge states that arise in scientific research; specifying operators that generate new knowledge states; specifying operators that generate new knowledge states; describing the organization of search through the spaces. describing the organization of search through the spaces. Again, research on heuristic search in humans and machines is highly relevant to this perspective on scientific activity.

27 Numeric and Symbolic Processing Claim: Qualitative/symbolic reasoning is just as crucial to science as quantitative/numeric reasoning. Many fields, like biology and psychology, rely mainly on qualitative models and explanations. Thus, a broadly based computational science must support: string and document processing string and document processing logical deduction and abduction logical deduction and abduction reasoning over causal models reasoning over causal models Fortunately, computers are more than number crunchers; they support general symbolic processing.

28 Computational Science and the Humanities Claim: The humanities have central roles to play in the pursuit of computational science as content disciplines. art history classical studies literature film / television linguistics music theater computational science computer science mathematics decision analysis engineering logic phil. of science statistics

29 Computational Science and the Humanities Claim: The humanities have central roles to play in the pursuit of computational science as method disciplines: logical reasoning and analysis logical reasoning and analysis textual composition and rhetoric textual composition and rhetoric visual design and composition visual design and composition Moreover, philosophy of science studies the nature of scientific knowledge and reasoning. Each has techniques that can inform the design of computational artifacts that support the scientific process.

30 Human-Computer Synergy Claim: Science is best achieved through a mixture of computer- controlled and human-controlled processes. Scientific research is a complex endeavor that we are unlikely to automate completely anytime soon; instead, we should: determine which tasks are most tractably automated; determine which tasks are most tractably automated; determine which tasks as best done by human scientists; determine which tasks as best done by human scientists; create environments that support their effective interaction. create environments that support their effective interaction. This makes another discipline – human-computer interaction – especially relevant to computational science.

31 Some Important Challenges Claim: Despite many successes in computational science, we need more research on methods that: revise existing models in response to anomalies; revise existing models in response to anomalies; construct models in knowledge-rich, data-lean fields; construct models in knowledge-rich, data-lean fields; visualize relations between data and models; visualize relations between data and models; support the incremental nature of science. support the incremental nature of science. Such techniques will provide better support for science as it is normally practiced by scientists.

32 Computational Science at Dartmouth Computational Science at Dartmouth

33 Computational Science at Dartmouth Dartmouth seems well suited for taking a lead in developing computational science as a distinct field: ongoing computational work in specific areas ongoing computational work in specific areas low hurdles for cross-departmental research low hurdles for cross-departmental research focus on high-quality liberal education focus on high-quality liberal education Neukom endowment to launch an institute Neukom endowment to launch an institute Most important, the field needs such leadership and Dartmouth is willing to serve in that role.

34 Creating a Dartmouth Community Computational science at Dartmouth requires a clear sense of community, which we can foster by: identifying faculty across campus with the potential and commitment to contribute to the new field; identifying faculty across campus with the potential and commitment to contribute to the new field; organizing talks by relevant faculty to advertise each others work and explore opportunities for collaboration; organizing talks by relevant faculty to advertise each others work and explore opportunities for collaboration; establishing a student organization with an emphasis on, and with activities in, computational science; establishing a student organization with an emphasis on, and with activities in, computational science; hosting regular social hours at which involved parties can meet and discuss common interests. hosting regular social hours at which involved parties can meet and discuss common interests. Such activities will improve awareness of computational science on campus and increase excitement about its potential.

35 Fostering Interdisciplinary Research Computational science at Dartmouth will need interdisciplinary collaborations, which we can encourage by: supporting postdoctoral fellows to work jointly with faculty from different departments; supporting postdoctoral fellows to work jointly with faculty from different departments; providing stipends to graduate students who work jointly with faculty from distinct departments; providing stipends to graduate students who work jointly with faculty from distinct departments; funding seed projects that involve collaboration among faculty across departments; funding seed projects that involve collaboration among faculty across departments; hiring new faculty with interdisciplinary records and with clear links to multiple departments. hiring new faculty with interdisciplinary records and with clear links to multiple departments. Such joint research efforts will make Dartmouth a role model for collaborative work in computational science.

36 Raising Funds for Computational Science Dartmouths efforts in computational science would benefit from additional funding, which we can assist by: organizing and submitting cross-departmental proposals for large grants from NIH, DOE, and NSF; organizing and submitting cross-departmental proposals for large grants from NIH, DOE, and NSF; pursuing gifts from, and joint projects with, companies that believe in computational science; pursuing gifts from, and joint projects with, companies that believe in computational science; playing an active role in government advisory boards to encourage long-term funding for the field; playing an active role in government advisory boards to encourage long-term funding for the field; working with elected representatives and agency officials to develop new funding programs. working with elected representatives and agency officials to develop new funding programs. Dartmouth can play a central role in such efforts to support the field of computational science.

37 Publicizing Dartmouths Role We can clarify Dartmouths efforts in computational science by: hosting a colloquium series that invites researchers from many fields to speak on campus; hosting a colloquium series that invites researchers from many fields to speak on campus; organizing and hosting an annual symposium on timely issues in computational science; organizing and hosting an annual symposium on timely issues in computational science; establishing a book series on computational science that includes volumes based on the symposia; establishing a book series on computational science that includes volumes based on the symposia; creating a Web site that reports news in computational science to the broader community; creating a Web site that reports news in computational science to the broader community; collecting on-line readings that define the field and illustrate key problems and approaches. collecting on-line readings that define the field and illustrate key problems and approaches. Combined with educational and research efforts, these activities will establish Dartmouth as a leader in computational science.

38 Fostering Interest in Computational Science Dartmouth should encourage interest in computational science among the campus community and the general public by: publishing accessible overviews of the movement in venues like Science, Nature, and CACM; publishing accessible overviews of the movement in venues like Science, Nature, and CACM; producing a documentary on computational science that covers the fields potential and challenges; producing a documentary on computational science that covers the fields potential and challenges; organizing a program to involve undergraduates in ongoing research on computational science; organizing a program to involve undergraduates in ongoing research on computational science; hosting evening talks by campus researchers in language understandable to a wide audience. hosting evening talks by campus researchers in language understandable to a wide audience. These activities will further establish Dartmouths commitment to computational science and its leadership in the area.

39 Possible Homes for Computational Science departments in the physical, life, or social sciences? departments in the physical, life, or social sciences? departments of computer science, mathematics, or statistics? departments of computer science, mathematics, or statistics? a school of of engineering or arts and sciences? a school of of engineering or arts and sciences? What academic unit would serve as the most appropriate home for computational science? Computational science is best supported at the campus-wide level, but coordinated with efforts on specific problems and approaches. A key challenge is to create a general institute of computational science that retains close ties to these more established units.

40 Some Relevant Dartmouth Units Dartmouth already has many interdisciplinary units relevant to computational science, including: Bioinformatics Shared Resource Bioinformatics Shared Resource Center for Biological and Biomedical Computing Center for Biological and Biomedical Computing Center for Cognitive Neuroscience Center for Cognitive Neuroscience Center for Integrated Space Weather Modeling Center for Integrated Space Weather Modeling Mathematical Social Sciences Mathematical Social Sciences MD/PhD Program in Computational Biology MD/PhD Program in Computational Biology Molecular Biology Core Facility Molecular Biology Core Facility Numerical Methods Laboratory Numerical Methods Laboratory These can support Dartmouths vision for computational science, but they must become active stakeholders.

41 A Curriculum for Computational Science

42 Research and Education Enlightened research in computational science is not enough; we must also edcuate the next generation of scientists. These two central activities should travel hand in hand, with: research developing computational methods to support the aims of content-oriented scientists; research developing computational methods to support the aims of content-oriented scientists; education training students to use such computational methods. education training students to use such computational methods. Both efforts should be grounded in specific problems from the content-driven disciplines. However, they require very different background / prerequisites.

43 A Curriculum in Computational Science Dartmouth courses in computational science should provide their students with an understanding of: structures, processes, and practices that arise in science; structures, processes, and practices that arise in science; computational methods to encode these structures/processes; computational methods to encode these structures/processes; how such methods can support the scientific enterprise. how such methods can support the scientific enterprise. The curriculum should treat computational science as a field with its own intellectual issues but grounded in scientific applications. Students should acquire a broad view of science and the potential for computational support in each component activity.

44 Possible Courses on Computational Science A curriculum in computational science should include courses on: the scientific enterprise, including findings from the history, philosophy, and psychology of science; the scientific enterprise, including findings from the history, philosophy, and psychology of science; scientific formalisms that cover different frameworks and give practice at modeling in different disciplines; scientific formalisms that cover different frameworks and give practice at modeling in different disciplines; interactive modeling environments, including visualization methods, that build on HCI principles; interactive modeling environments, including visualization methods, that build on HCI principles; applications of computational linguistics, including information retrieval/extraction, summarization, and generation; applications of computational linguistics, including information retrieval/extraction, summarization, and generation; methods for analyzing data and constructing models, illustrated in a variety of disciplines; methods for analyzing data and constructing models, illustrated in a variety of disciplines; specialized methods for modeling and data analysis for fields like Earth science, psychology, and biology. specialized methods for modeling and data analysis for fields like Earth science, psychology, and biology.

45 Challenges in Computational Science Education science involves a wide range of structures and processes; science involves a wide range of structures and processes; different scientific disciplines have distinct characters; different scientific disciplines have distinct characters; students must understand both method and content areas; students must understand both method and content areas; they must master general principles and specific applications. they must master general principles and specific applications. following generalized core courses with specialized tracks; following generalized core courses with specialized tracks; organizing even general classes around hands-on projects; organizing even general classes around hands-on projects; using high-level software environments to lower entry barriers. using high-level software environments to lower entry barriers. The broad nature of the field raises a number of challenges: These should provide the curriculum in computational science with the right balance of generality and specificity. We can best address these pedagogical issues by:

46 Teaching Science via Computation Claim: Computational science is an excellent unifying theme for teaching scientific content. Students can gain knowledge about content disciplines by: developing computational models developing computational models analyzing data with computers analyzing data with computers simulating these models behavior simulating these models behavior comparing predictions to observations comparing predictions to observations These activities do not require substantial training in computer science or traditional programming languages. They can be achieved with high-level languages and interactive software environments.

47 Some Environments with Instructional Promise We can make computational science accessible to students by drawing on high-level software environments such as: STELLA and P ROMETHEUS let users specify, visualize, and simulate differential equation models (e.g., of ecosystems); STELLA and P ROMETHEUS let users specify, visualize, and simulate differential equation models (e.g., of ecosystems); H Y B ROW lets users specify, visualize, falsify, and revise qualitative causal models (e.g., of cell biology); H Y B ROW lets users specify, visualize, falsify, and revise qualitative causal models (e.g., of cell biology); ACT-R and I CARUS let users specify, simulate, and trace symbolic process models of human reasoning and learning. ACT-R and I CARUS let users specify, simulate, and trace symbolic process models of human reasoning and learning. We have used two of these environments in Stanford courses and hope to utilize the other in the future.

48 Closing Remarks

49 Summary change the ways that we carry out scientific research; change the ways that we carry out scientific research; increase the rate of scientific and technological progress; increase the rate of scientific and technological progress; improve education in the sciences and other disciplines. improve education in the sciences and other disciplines. Computational science, developed along appropriate lines, will: However, making this enterprise successful will require: a broad and inclusive vision for computational science; a broad and inclusive vision for computational science; a strong commitment to interdisciplinary research; a strong commitment to interdisciplinary research; an institution willing to play a leadership role. an institution willing to play a leadership role. Dartmouth can help transform this vision of into reality and aid computational science to develop its full potential.

50 End of Presentation


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