Computer & Information Science and Engineering at NSF

1. Computer & Information Science and Engineering at NSF Michael Pazzani Division Director, CISE/Information and Intelligent System NSF

2. Overview • NSF • CISE • IDM • Statistics on NSF/CISE/IDM… • CISE activities in NSF wide competitions • Plans for 2005 and Beyond • Program Deadlines • Transitioning Information Technology Research • Information Integration • Privacy • Homeland Security • Cybertrust • How you can help NSF • Submit quality proposals. Innovative but achievable. • Let us know about your progress on grants • Be a reviewer • Join NSF

3. NSF Mission National Science Foundation Act of 1950 (Public Law 810507): • To promote the progress of science; • to advance the national health, prosperity, and welfare; • to secure the national defense; • and for other purposes.

4. Office of the Director Directorate for Biological Sciences Directorate for Geosciences Directorate for Computer and Information Sciences and Engineering Directorate for Mathematical and Physical Sciences Directorate for Education and Human Resources Directorate for Social, Behavioral And Economic Sciences Directorate for Engineering Research in the NSF Organization

5. CISE Organization: 2004Peter Freeman, AD

6. What’s new in the reorganization • New divisions and programs • Clusters: Larger solicitations with teams of program directors working together • Emphasis Areas: cross-divisional activities and priority areas • Cybertrust • Broadening participation • Science of design • Information integration

7. Information and Intelligent SystemsMichael Pazzani, DD • increasing the capabilities of human beings and machines to create, discover and reason with knowledge • advance the ability to represent, collect, store, organize, locate, visualize and communicate information • research on how empirical data leads to discovery in the sciences and engineering

8. IIS: Systems in Context • Supports research and education on the interaction between information, computation and communication systems and users, organizations, government agencies and the environment • Human-Computer Interaction: • Universal Access • Digital Society and Technologies • Robotics • Digital Government

9. IIS: Data, Inference & Understanding • basic research with the goal of creating general-purpose systems for representing, storing, accessing and drawing inferences from data, information and knowledge. • Artificial Intelligence and Cognitive Science • Computer Vision • Human Language and Communication • Information and Data Management • Digital Libraries

10. IIS: Science and Engineering Informatics • Collaborative Research in Computational Neuroscience • Science & Engineering Information Integration and Informatics • SEI: Focuses on developing information technology to solve a particular science or engineering problem and generalizing the solution to other related problems. • A significant domain challenge • A significant computer science problem that is a barrier to achieving the domain challenge • Information Integration: Provide a uniform view to a multitude of heterogeneous, independently developed data sources. • Reconciling heterogeneous formats • Web semantics • Decentralized data-sharing • Data-sharing on advanced cyberinfrastructure • On-the-fly integration • Information Integration Resources:

11. The SARS virus has been sequenced!

12. Historical Data

13. Faculty Growth

14. IIS Trends

15. IDM Budget Year to Year comparisons are not exact, e.g., pre 2004 budget includes IGERT, Sensors, Panel Travel, IPA Salary and Travel.

16. CISE BUDGET Overview

17. IIS Budget Overview • Combined transferred from other agencies and regular program funds have been relatively flat, (modulo reorg) • Growth in funds managed by IIS has been primarily in ITR

18. Funding Trends • Funds available to support research have nearly doubled in the past five years • However, proposals have almost tripled • From less than one per year per CS faculty member to more than one per year. • Shift from ~90% awards support a single PI to ~40% supporting teams, and 5% “large” teams • Budget outlook for the next years- slow growth a likely scenario. Transition of ITR funds into regular programs

19. IIS Competitions 2004 vs 2005 • FY 2004 • Responsible for about 2590 proposals • Success rates 17% CAREER, ~5% regular, ~7% ITR • FY 2005 • Raise acceptance rate of regular 2004 proposals to 10-12% • CAREER in July as normal • Science & Engineering Informatics/Information Integration and Universal Access: December 2004 • Data, Inference, and Understanding and Systems in Context: April 2005 with most funding from FY 2006

20. IIS FY04

21. What data is available on NSF Awards?http://dellweb.bfa.nsf.gov/

22. IIS Competitions: 2005 vs 2006 • FY 2005 • July (2004): CAREER • Dec (2004): SEIII, UA, CRCNS • April(2005): Everything Else • FY 2006 • July (2005): CAREER • Dec (2005): Information Integration, CRCNS • April (2006): Everything Else including SEI • Restrictions • Any individual can be PI or coPI of no more than 2 proposals • A PI cannot submit a proposal that covers the same research to more than one competition.

23. National Security & Homeland Security Research at the National Science Foundation • NSF Mission: To promote the progress of science; to advance the national health, prosperity, and welfare; and to secure the national defense. • Why NSF? • Peer-review system identifies highly meritorious projects. • Established relationship with top researchers on a broad range of topics identifies match between agency needs and researcher’s capabilities. • Types of Support • Foundational research in areas such cryptography, network security, machine translation, speech analysis, robotics, information fusion, image analysis, data mining, collaborative systems, social network analysis, bioinformatics, critical infrastructure protection, economics and political science with application to a broad range of problems including National Security & Homeland Security. • Supplements funded by other agencies to existing NSF investigators to perform unclassified research with short-term application of interest to the agency.

24. Interagency transfers related to homeland security: $27.6M

25. NSF Directorates

26. Human and Social Dynamics:(Deadline Winter) • Understanding the human and social dynamics of change in our contemporary world • to develop a comprehensive, multi-disciplinary approach to understanding human and social dynamics; • to exploit the convergence in biology, engineering, information technology, and cognition to advance the understanding of behavior and performance at both the individual and social levels;

27. Science of Learning Centers • 3 to 5 Center awards and 20+ Catalyst awards (workshops, etc.) a year. New competitive expected in FY 2005 • The neural basis of learning in humans and other species; • Machine learning, learning algorithms, knowledge representation, robotics, adaptive systems, and computational simulation of cognitive systems; • Visualization and representation of complex phenomena and multidimensional data; • Analogical reasoning, mathematical reasoning, causal analysis, general and domain-specific aspects of mathematical and scientific problem-solving, creativity, and intelligence; • Learning of disciplinary content including assessment, structure of disciplinary knowledge, pedagogical content knowledge, learning in formal and informal educational settings, and equitable access to learning; • Learning technologies, including intelligent tutoring systems, visualization tools, computer-supported collaborative environments, digital libraries, and real-time assessment tools;

28. CISE Computing Research Infrastructure • Infrastructure Acquisition. These awards have budgets up to $2,000,000.  Cost sharing is not required on small grants; cost sharing of 20% is required on medium grants but only from Ph.D.-granting institutions; cost sharing of 30% is required on large grants but only from Ph.D.-granting institutions. • Community Resource Development.  These awards have budgets from $300,000 to $2,000,000:  medium from $300,000 to $800,000 and large over $800,000.  Cost sharing is not required on these awards.  Development projects create a resource for an entire CISE research community, such as a testbed for evaluating research results or a large data resource that contains problems a community is trying to solve (e.g., annotated speech data). • Planning.  These awards facilitate the preparation of a proposal for a medium or large infrastructure acquisition grant.   They have budgets up to $50,000 for one institution or up to $100,000 if more than one institution is involved.  Cost sharing is not required. 

29. Privacy & Data: 2005 “Focus in IISWhat is Data Mining? • KDD: The process of identifying valid, novel, useful, and understandable patterns in data • Washington/Press: Any process that allows one to draw conclusions from data, e.g., • identify novel patterns in data, • searching for known patterns in data, • integrating data from multiple sources • searching for records about an individual in one or more databases

30. Benefits of “Data Mining” • Accelerate collection and analysis of scientific data • Greater medical understanding of the human and the environment • Early symptoms of diseases • Dietary and Environmental factors • Cost savings and reductions in errors in medicine • Personalized Services (e.g., TiVo) • Counterterrorism

31. Connecting the Dots

32. …but not my dots

33. Personalized Services

34. s.1484 July 2003 To require a report on Federal Government use of commercial and other databases for national security, intelligence, and law enforcement purposes, and for other purposes. IN THE SENATE OF THE UNITED STATES July 29 (legislative day, JULY 21), 2003 SEC. 4. GENERAL PROHIBITIONS. (a) IN GENERAL- Notwithstanding any other provision of law, no department, agency, or other element of the Federal Government, or officer or employee of the Federal Government, may conduct a search or other analysis for national security, intelligence, or law enforcement purposes of a database based solely on a hypothetical scenario or hypothetical supposition of who may commit a crime or pose a threat to national security.

35. Privacy: Why is it important • Prevention of crimes such as identity theft. • Prevention of discrimination based on factors such as medical conditions • Enhance Law enforcement/ homeland security • ASA HUTCINSON: Prior to 9/11, there were fewer than 100 names on the no fly list. Today TSA provides carriers with no fly … lists which have been dramatically expanded. • KWAME HOLMAN: However, commission chairman Tom Kean said the lists of potential terrorists given to air carriers still are incomplete. • THOMAS KEAN: As we understand it, the intelligence community doesn't want air carriers to possess many of these names because they feel maybe they could tip off terrorists or compromise sensitive sources and methods of intelligence collection.

36. Some Challenges • Allow two organizations to find the intersection between two sets without either finding out any information on the other’s elements not in the intersection • Allow for “equal with probability above a threshold” vs. equal, i.e., I travel under “Michael Pazzani”, “Micheal Pazzani” “Michael J. Pazzani” “MichaelJ Pazzani” • Allow an organization to discover a general pattern without finding out any information about the individuals. • Building Sociotechnical systems that understand the implications of false positives and false negatives and the assumptions behind specific algorithms. • Digital Rights Management: Allow owners of information to set policies on how it may be used and to trace how it has been used. • Anonymizing Data while retaining important properties, particularly “entity-relationship” or relational.

37. What CISE/NSF/IIS is doing • In FY05,there will be a special emphasis on drawing conclusions from data while maintaining the privacy of individuals in the Division of Information and Intelligent Systems (CISE/IIS) • Basic research on privacy and data analysis • Inform the adoption of new policies • Suggestions on additional questions to ask are welcome • Privacy researchers are advised to understand the social and legal environment in addition to the technical. • Funding NAS/CSTB study on privacy (with Census and other agencies)

38. How you can help NSF • Submit High Quality Proposals • Innovative, but achievable • Well-written • Address broader impacts • Participate in Reviewing/Panels • Give Constructive Detailed Reviews • Make an effort to understand and support research outside your paradigm • Keep Program Manager informed of findings • Nuggets • CISE Newsletter • PowerPoint of Conference Presentations • Rotate in as a Program Manager (IPA) or Division Director

39. My proposal wasn’t accepted. Should I resubmit? YES, but… • The reviewers didn’t get it: • Was the proposal clear? Especially summary & introduction • Did you explain how it is a significant advantage over state of art broadly defined,not just in your specialized area • Hint: Make Proposal Summary look like an ideal review • Did you address all the review criteria? Read announcement carefully • Broader Impacts – Mention Dept & School Outreach. Make it easy for others to build upon your work, education and reserach • Research Plan- What will you do in year 3? Evaluation Plan? • Was it sent to the right program? Most specific program is probably best (Information Integration vs. Information and Data Management) • Too preliminary: Do some initial work and resubmit or explain alternative and methodology • Constructive Criticism in reviews. Fix, update, make sure it’s clear • Multi-investigator: Integrate research topics rather than append them. Be critical of each other. • All the reviewers that it was pretty good, but none thought it was excellent  Are you sure the topic is important and innovative? • Compare to funded proposals • Peers/Mentors • Abstracts on Web site and Full Proposals via Freedom of Information Act • No review was detailed  Appeal

40. How to be a good reviewer • Be specific. • No: “Others have done something like this” • Yes: “See Smith (2004), AI Journal pp 11-19” • Be detailed • Journal length vs workshop length review. • Take and defend a strong position: Avoid calling anything “Okay” or “Good”. Why is this “Outstanding” or describe why this “Needs Considerable Improvement” • Be Constructive • Allow PI to learn from your expertise • Distinguish Fatal Flaws from Suggestions for Improvement, particularly for NSF wide competitions • Mention strengths, innovations and impacts in detail, particularly for NSF wide competitions • Don’t be conservative: Support high risk if there is potential for large impact. Ignore insignificant flaws. • Acknowledge the quality of work outside your research specialty or with different foundations

41. San Diego and Houston Girl Scouts In-School Program • SDSC Program • Broad-based, program-wide results that demonstrate success related to improved math and science performance for preK-12 students

42. COPLINK: An Intelligent Workbench for Information Analysis and Visualization • Crime analysts and detectives need to perform analysis on a large set of data to investigate criminal and terrorist activities. Analysis tools can be customized to help crime analysts perform such tasks quickly and effectively.

43. Surface Reconstruction from Unorganized Data Points • models of real-world objects • laser-range scanner to automatically collect a set of points from the surface of the object, and then use the points to compute an accurate and detailed model.

44. National Virtual Observatory • Will unite the astronomical databases of many observatories • NVO will maximize the potential for new scientific insights from the data by making them available in an accessible, unified form to researchers, amateur astronomers, and students.

45. Program Director Positions for Rotators • Computer Vision • Databases and Computer Security • Grant to cover 12 Month Salary to your university • 16K+ a year living expenses (first 2 years) • Travel to your home institution • 50 days IR&D (but hard to find 50 days) • Different Perspective on Federal Funding