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Frontiers in Research and Education in Computing: A View from the National Science Foundation Frontiers in Research and Education in Computing: A View from the National Science Foundation Jeannette M. Wing Assistant Director Computer and Information Science and Engineering and President’s Professor of Computer Science Carnegie Mellon University OOPSLA October 28, 2009 Orlando, FL

ss o t Acr Cu s ilitie sb Frontiers in Research and Education ein ss o t Acr Cu s ilitie sb Frontiers in Research and Education ein i. Computing: g. S n in neer e E Science Foundation A View from the Nationalngi twar f nd So sa uage g g Lan in ramm Prog Jeannette M. Wing Assistant Director Computer and Information Science and Engineering and President’s Professor of Computer Science Carnegie Mellon University OOPSLA October 28, 2009 Orlando, FL

NSF NSF

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General Themes • • • Fundamental, long-term research High-risk, high-return, potentially transformative Inter- and General Themes • • • Fundamental, long-term research High-risk, high-return, potentially transformative Inter- and multi-disciplinary Multi-perspective, collaborative Partnerships Academia ecosystem Industry Government • Societal Grand Challenges OOPSLA 7 Jeannette M. Wing

CISE-specific NSF-wide Investments CISE-specific NSF-wide Investments

CDI: Cyber-Enabled Discovery and Innovation Computational Thinking for Science and Engineering • Paradigm shift CDI: Cyber-Enabled Discovery and Innovation Computational Thinking for Science and Engineering • Paradigm shift – Not just computing’s metal tools (transistors and wires) but also our mental tools (abstractions and methods) • It’s about partnerships and transformative research. – To innovate in/innovatively use computational thinking; and – To advance more than one science/engineering discipline. • Investments by all directorates and offices – FY 08: $48 M, 1800 Letters of Intent, 1300 Preliminary Proposals, 200 Full Proposals, 36 Awards – FY 09: $63 M+, 830 Prelimary Proposals, 283 Full Proposals, 53+ Awards OOPSLA 9 Jeannette M. Wing

Range of Disciplines in CDI Awards • • • • OOPSLA • • • Range of Disciplines in CDI Awards • • • • OOPSLA • • • • Aerospace engineering Astrophysics and cosmology Atmospheric sciences Biochemistry Biomaterials Biophysics Chemical engineering Civil engineering Communications science and engineering Computer science Cosmology Ecosystems Genomics Geosciences 10 Linguistics Materials engineering Mathematics Mechanical engineering Molecular biology Nanocomputing Neuroscience Proteomics Robotics Social sciences Statistical physics Sustainability … Jeannette M. Wing

Range of Disciplines in CDI Awards • • • • OOPSLA Aerospace engineering • Range of Disciplines in CDI Awards • • • • OOPSLA Aerospace engineering • Linguistics Astrophysics and cosmology • Materials engineering Atmospheric sciences • Mathematics Biochemistry • Mechanical engineering Biomaterials • Molecular biology CT includes Languages Biophysics • Nanocomputing and Software Methods Chemical engineering • Neuroscience Civil engineering • Proteomics Communications science and • Robotics engineering • Social sciences Computer science • Statistics Cosmology • Statistical physics Ecosystems • Sustainability Genomics • … Geosciences … advances via Computational Thinking 11 Jeannette M. Wing

Science and Engineering Beyond Moore’s Law (not a special program) • Three directorates: CISE, Science and Engineering Beyond Moore’s Law (not a special program) • Three directorates: CISE, ENG, MPS – All investing in core science, engineering, and technology • Multi-core, many-core, massively parallel – Programming models, languages, tools • New, emerging substrates – Nanocomputing – Bio-inspired computing – Quantum computing OOPSLA 12 Jeannette M. Wing

CISE CISE

Core and Cross-Cutting Programs CCF CNS IIS Core • Algorithmic F’ns • Communications & Core and Cross-Cutting Programs CCF CNS IIS Core • Algorithmic F’ns • Communications & Information F’ns • Software & Hardware F’ns • Computer Systems • Network Systems • Infrastructure • Education & Workforce • Human-Centered • Information Integration & Informatics • Robust Intelligence Cross-Cutting • Cyber-Physical Systems • Data-intensive Computing • Network Science and Engineering • Trustworthy Computing Plus many other programs with other NSF directorates and other agencies OOPSLA 14 Jeannette M. Wing

Expeditions • Bold, creative, visionary, high-risk ideas • Whole >> part i i • Expeditions • Bold, creative, visionary, high-risk ideas • Whole >> part i i • Solicitation is deliberately underconstrained – Tell us what YOU want to do! – Response to community • Loss of ITR Large, DARPA changes, support for high-risk research, large experimental systems research, etc. • Expect to fund 3 awards, each at $10 M for 5 year OOPSLA 15 Jeannette M. Wing

FY 08 -FY 09 Awards What might be a good Expedition in Programming Languages FY 08 -FY 09 Awards What might be a good Expedition in Programming Languages and/or Software Engineering? FY 08 Awards • Computational Sustainability – Gomes, Cornell, Bowdoin College, the Conservation Fund, Howard University, Oregon State University and the Pacific Northwest National Laboratory • Intractability – Arora, Princeton, Rutgers, NYU, Inst for Adv. Studies • Molecular Programming – Winfrey, Cal Tech, UW • Open Programmable Mobile Internet – Mc. Keown, Stanford FY 09 Awards • Robotic Bees – Wood, Harvard • Modeling Tools for Disease and Complex Systems – Clarke, CMU, NYU, Cornell, SUNY Stony Brook, University of Maryland • Customized Computing Technology – Cong, UCLA OOPSLA 16 Jeannette M. Wing

Cross-Cutting Programs Cross-Cutting Programs

Drivers of Computing Society Science OOPSLA Technology 19 Jeannette M. Wing Drivers of Computing Society Science OOPSLA Technology 19 Jeannette M. Wing

Data Intensive Computing Data Intensive Computing

How Much Data? • • NOAA has ~1 PB climate data (2007) Wayback machine How Much Data? • • NOAA has ~1 PB climate data (2007) Wayback machine has ~2 PB (2006) CERN’s LHC will generate 15 PB a year (2008) HP is building Wal. Mart a 4 PB data warehouse (2007) AT&T handles 17. 6 PB of traffic over its backbone network a day (2009) Google processes 20 PB a day (2008) “all words ever spoken by human beings” ~ 5 EB Int’l Data Corp predicts 1. 8 ZB of digital data by 2011 640 K ought to be enough for anybody. Slide source: Jimmy Lin, UMD OOPSLA 21 Jeannette M. Wing

Convergence in Trends • Drowning in data • Data-driven approach in computer science research Convergence in Trends • Drowning in data • Data-driven approach in computer science research – graphics, animation, language translation, search, …, computational biology • Cheap storage – Seagate Barracuda 1 TB hard drive for $90 • Growth in huge data centers • Data is in the “cloud” not on your machine • Easier access and programmability by anyone – e. g. , Amazon EC 2, Google+IBM cluster, Yahoo! Hadoop OOPSLA 22 Jeannette M. Wing

Data-Intensive Computing Sample Research Questions Science – What are the fundamental capabilities and limitations Data-Intensive Computing Sample Research Questions Science – What are the fundamental capabilities and limitations of this paradigm? – What new programming abstractions (including models, languages, algorithms) can accentuate these fundamental capabilities? – What are meaningful metrics of performance and Qo. S? Technology – How can we automatically manage the hardware and software of these systems at scale? – How can we provide security and privacy for simultaneous mutually untrusted users, for both processing and data? – How can we reduce these systems’ power consumption? Society – What (new) applications can best exploit this computing paradigm? OOPSLA 23 Jeannette M. Wing

Data-Intensive Computing Infrastructure for CISE Community • Google + IBM partnership announced in February Data-Intensive Computing Infrastructure for CISE Community • Google + IBM partnership announced in February 2008 – – Access to 1600+ nodes, software and services (Hadoop, Tivoli, etc. ) Available to entire community Cluster Exploratory (Clu. E) seed program April 23, 2008: Press release on Clu. E awards to 14 universities • http: //www. nsf. gov/news_summ. jsp? cntn_id=114686&org=NSF&from=news – Oct 5 -6, 2009: Clu. E PI meeting, Mountain View, CA • https: //wiki. umiacs. umd. edu/ccc/index. php/CLu. E_PI_Meeting_2009 • HP + Intel + Yahoo! + UIUC cluster announced in July 2008 – 1000+ nodes – Bare machine, not just software (Hadoop) accessible – Hosted at UIUC, available to entire community Beyond Map. Reduce! • Other companies welcome! OOPSLA 24 Jeannette M. Wing

Cyber-Physical Systems Cyber-Physical Systems

Smart Cars A BMW is “now actually a network of computers” [R. Achatz, Seimens, Smart Cars A BMW is “now actually a network of computers” [R. Achatz, Seimens, Economist Oct 11, 2007] Credit: Paul. Stamatiou. com Cars drive themselves Lampson’s Grand Challenge: Reduce highway traffic deaths to zero. [Butler Lampson, Getting Computers to Understand, OOPSLA Microsoft, J. ACM 50, 1 (Jan. 2003), pp 70 -72. ] 26 Smart parking Jeannette M. Wing

Smart Fliers Credit: NASA/JPL smart helicopters Credit: Boeing An airplane is a network of Smart Fliers Credit: NASA/JPL smart helicopters Credit: Boeing An airplane is a network of computers. CPS Luncheon 27 Credit: Harvard university smart insects Jeannette M. Wing

Embedded Medical Devices Credit: Baxter International infusion pump pacemaker IBM Research 28 scanner Credit: Embedded Medical Devices Credit: Baxter International infusion pump pacemaker IBM Research 28 scanner Credit: Siemens AG Jeannette M. Wing

Sensors Everywhere Credit: Arthur Sanderson at RPI Hudson River Valley Sonoma Redwood Forest Kindly Sensors Everywhere Credit: Arthur Sanderson at RPI Hudson River Valley Sonoma Redwood Forest Kindly donated by Stewart Johnston smart buildings Credit: MO Dept. of Transportation OOPSLA smart bridges 29 Jeannette M. Wing

Robots Everywhere Credit: Paro Robots U. S. , Inc. At home: Paro, therapeutic robotic Robots Everywhere Credit: Paro Robots U. S. , Inc. At home: Paro, therapeutic robotic seal Credit: Carnegie Mellon University Credit: Honda At work: Two ASIMOs working together in coordination to deliver refreshments At home/clinics: Nursebot, robotic assistance for the elderly At home: i. Robot Roomba vacuums your house OOPSLA 30 Jeannette M. Wing

Assistive Technologies for Everyone brain-computer interfaces of today Credit: Dobelle Institute memex of tomorrow Assistive Technologies for Everyone brain-computer interfaces of today Credit: Dobelle Institute memex of tomorrow Credit: Emotiv IBM Research 31 Jeannette M. Wing Credit: Paramount Pictures

What is Common to These Systems? • They have a computational core that interacts What is Common to These Systems? • They have a computational core that interacts with the physical world. • Cyber-physical systems are engineered systems that require tight conjoining of and coordination between the computational (discrete) and the physical (continuous). • Trends for the future – Cyber-physical systems will be smarter and smarter. – More and more intelligence will be in software. OOPSLA 32 Jeannette M. Wing

Cyber-Physical Systems Sample Research Challenges Science • Co-existence of Booleans and Reals – Discrete Cyber-Physical Systems Sample Research Challenges Science • Co-existence of Booleans and Reals – Discrete systems in a continuous world • Reasoning about uncertainty – Human, Mother Nature, the Adversary Technology • Intelligent and safe digital systems that interact with the physical world • Self-monitoring, real-time learning and adapting Society • Systems need to be unintrusive, friendly, dependable, predictable, … New Challenges for PL and SE communities: - “Hybrid” languages: discrete and continuous - Languages, logics, models with probabilistic state transitions: uncertainty - Software services systems that learn and adapt in real-time OOPSLA 33 Jeannette M. Wing

A (Flower) Model for Expediting Progress Sectors Industry Gov’t (e. g. , military) medical A (Flower) Model for Expediting Progress Sectors Industry Gov’t (e. g. , military) medical aero Industry Gov’t Academia Gov’t (NSF, NSA, NIH, Do. D, …) auto finance Fundamental Research energy civil chemical OOPSLA 34 transportation materials Jeannette M. Wing

Our Evolving Networks are Complex 1970 IBM Research 1980 36 1999 Jeannette M. Wing Our Evolving Networks are Complex 1970 IBM Research 1980 36 1999 Jeannette M. Wing

Our Evolving Networks are Complex 1970 IBM Research 1980 37 1999 Jeannette M. Wing Our Evolving Networks are Complex 1970 IBM Research 1980 37 1999 Jeannette M. Wing

Our Evolving Networks are Complex 1970 IBM Research 1980 38 1999 Jeannette M. Wing Our Evolving Networks are Complex 1970 IBM Research 1980 38 1999 Jeannette M. Wing

Network Science and Engineering Sample Research Challenges Science Understand the complexity of large-scale networks Network Science and Engineering Sample Research Challenges Science Understand the complexity of large-scale networks - Understand emergent behaviors, local–global interactions, system failures and/or degradations - Develop models that accurately predict and control network behaviors Technology Develop new architectures, exploiting new substrates - Develop architectures for self-evolving, robust, manageable future networks - Develop design principles for seamless mobility support - Leverage optical and wireless substrates for reliability and performance - Understand the fundamental potential and limitations of technology Society Enable new applications and new economies, while ensuring security and privacy - Design secure, survivable, persistent systems, especially when under attack - Understand technical, economic and legal design trade-offs, enable privacy protection - Explore AI-inspired and game-theoretic paradigms for resource and performance optimization OOPSLA 39 Network science and engineering researchers Distributed systems and substrate researchers Security, privacy, economics, AI, social science researchers Jeannette M. Wing

Network Science and Engineering Sample Research Challenges Science Understand the complexity of large-scale networks Network Science and Engineering Sample Research Challenges Science Understand the complexity of large-scale networks - Understand emergent behaviors, local–global interactions, system failures and/or degradations - Develop models that accurately predict and control network behaviors Network science and engineering researchers Sample Challenges for PL and SE: - Models, logics, languages, tools, etc. for complex (emergent) behavior of evolving networks Distributed Develop new architectures, Technology see Pamela Zave’s “Software Engineering for the Next Internet” - Please systems and exploiting new substrates ICSE 2009 Keynote substrate - Develop architectures for self-evolving, robust, manageable future networks - Develop design principles for seamless mobility support - Leverage optical and wireless substrates for reliability and performance - Understand the fundamental potential and limitations of technology Society Enable new applications and new economies, while ensuring security and privacy - Design secure, survivable, persistent systems, especially when under attack - Understand technical, economic and legal design trade-offs, enable privacy protection - Explore AI-inspired and game-theoretic paradigms for resource and performance optimization OOPSLA 40 researchers Security, privacy, economics, AI, social science researchers Jeannette M. Wing

Trustworthy Computing • Trustworthy = reliability, security, privacy, usability • Deepen and broaden Cyber Trustworthy Computing • Trustworthy = reliability, security, privacy, usability • Deepen and broaden Cyber Trust • Three emphases for FY 09 – Foundations of trustworthy • Models, logics, languages, algorithms, metrics • E. g. , Science of Security – Privacy – Usability OOPSLA too 41 Jeannette M. Wing

New for FY 10 New for FY 10

Clickworkers Collaborative Filtering Collaborative Intelligence Collective Intelligence Computer Assisted Proof Crowdsourcing e. Society Genius Clickworkers Collaborative Filtering Collaborative Intelligence Collective Intelligence Computer Assisted Proof Crowdsourcing e. Society Genius in the Crowd Human-Based Computation Participatory Journalism Pro-Am Collaboration Recommender Systems Reputation Systems Social Commerce Social Computing Social Technology Swarm Intelligence Wikinomics Wisdom of the Crowds DAC 43 Jeannette M. Wing

In ly ial oc S DAC ige ell t 44 ing ut p m In ly ial oc S DAC ige ell t 44 ing ut p m Co nt Clickworkers Collaborative Filtering Collaborative Intelligence Collective Intelligence Computer Assisted Proof Crowdsourcing e. Society Genius in the Crowd Human-Based Computation Participatory Journalism Pro-Am Collaboration Recommender Systems Reputation Systems Social Commerce Social Computing Social Technology Swarm Intelligence Wikinomics Wisdom of the Crowds Jeannette M. Wing

Socially Intelligent Computing 19 th C-20 th C Computer : : = Human | Socially Intelligent Computing 19 th C-20 th C Computer : : = Human | Machine | Human + Machine | Network of Computer 20 th C 20 th-21 st C now and future Sample Questions: - What is the collective “intelligence” of humans and machines working together? - When must we rely on the participation of humans for their reasoning ability (i. e. , intelligence)? - What is “computable” by these kinds of “computers”? - Can we understand the capabilities of humans and computers working in harmony, solving problems neither can solve alone? - Can we design systems with intentional, rather than accidental behavior in mind? OOPSLA 45 Jeannette M. Wing

Socially Intelligent Computing 19 th C-20 th C Computer : : = Human | Socially Intelligent Computing 19 th C-20 th C Computer : : = Human | Machine | Human + Machine | Network of Computer 20 th C 20 th-21 st C now and future Sample Questions: New Challenges for PL of humans - What is the collective “intelligence”and SE: and machines working together? - How do on program these “computers”? - When must we relyyouthe participation of humans for - What languages? their reasoning ability (i. e. , intelligence)? - What by these design and analysis - What is “computable”softwarekinds of “computers”? methods? - Can we understand the capabilities of humans and computers working in harmony, solving problems neither can solve alone? - Can we design systems with intentional, rather than accidental behavior in mind? NSF (CISE+SBE) Social-Computational Systems (So. CS) (pronounced “socks”) Program OOPSLA 46 Jeannette M. Wing

Others • Joint with other directorates and offices • • Activities with other agencies, Others • Joint with other directorates and offices • • Activities with other agencies, e. g. , DARPA, DHS, IARPA, NGA, NIH, NSA Partnerships with companies • • OOPSLA – – – – CISE + BIO + SBE + MPS: Computational Neuroscience (with NIH) CISE + EHR: Advanced Learning Technologies CISE + ENG: Cyber-Physical Systems, Multi-core (with SRC) CISE + MPS: FODAVA (with DHS), MCS CISE + OCI: Data. Net OCI + CISE + ENG + GEO + MPS: Peta. Apps Creative IT (co-funding with other directorates) – – Google+IBM, HP+Intel+Yahoo!: Data-Intensive Computing SRC: Multi-core Research infrastructure: CRI, MRI … Please see website www. cise. nsf. gov for full list. 47 Jeannette M. Wing

Research Ideas in the Works Research Ideas in the Works

IT and Sustainability (Energy, Environment, Climate) IT as part of the problem and IT IT and Sustainability (Energy, Environment, Climate) IT as part of the problem and IT as part of the solution • IT as a consumer of energy – 2% (and growing) of world-wide energy use due to IT • IT as a helper, especially for the other 98% – Direct: reduce energy use, recycle, repurpose, … – Indirect: e-commerce, e-collaboration, telework -> reduction travel, … – Systemic: computational models of climate, species, … -> inform science and inform policy • Engages the entire CISE community – – – OOPSLA Modeling, simulation, algorithms Energy-aware computing Science of power management Sensors and sensor nets Intelligent decision-making Energy: A new measure of algorithmic complexity and system performance, along with time and space CISE’s part of NSF’s FY 10 Climate Research Initiative 49 Jeannette M. Wing

Computer Science and Economics Computer Science influencing Economics influencing Computer Science - Automated mechanism Computer Science and Economics Computer Science influencing Economics influencing Computer Science - Automated mechanism design underlies electronic commerce, e. g. , ad placement, on-line auctions, kidney exchange - Internet marketplace requires revisiting Nash equilibria model - Use intractability for voting schemes to circumvent impossibility results Research Issues at the Interface of Computer Science and Economics Workshop - Ithaca, September 3 -4, 2009, sponsored by CISE - Stellar line up of computer scientists and economists - http: //www. cis. cornell. edu/conferences_workshops/CSECON_09/ OOPSLA 50 Jeannette M. Wing

Computer Science and Biology • Gene sequencing and bioinformatics are a given • Trend Computer Science and Biology • Gene sequencing and bioinformatics are a given • Trend now is looking at common principles between the two disciplines – Complex systems • • Uncertainty of environment Networked Real-time adaptation Fault-tolerant, resilient – Information systems – Programmed systems • Synthetic biology • First decade of CS+Bio was low-hanging fruit. Second decade will form deeper and closer connections. OOPSLA 51 Jeannette M. Wing

Education Education

Education Implications for K-12 Question and Challenge for the Computing Community: What is an Education Implications for K-12 Question and Challenge for the Computing Community: What is an effective way of learning (teaching) computational thinking by (to) K-12? - What concepts can students (educators) best learn (teach) when? What is our analogy to numbers in K, algebra in 7, and calculus in 12? - We uniquely also should ask how best to integrate The Computer with teaching the concepts. Computer scientists are now working with educators and cognitive learning scientists to address these questions. OOPSLA 53 Jeannette M. Wing

C. T. in Education: Community Efforts CRA-E Computing Community CSTA NSF Rebooting College Board C. T. in Education: Community Efforts CRA-E Computing Community CSTA NSF Rebooting College Board National Academies Computational Thinking workshops K-12 BPC OOPSLA ACM-Ed CPATH AP 54 CSTB “CT for Everyone” Steering Committee • Marcia Linn, Berkeley • Al Aho, Columbia • Brian Blake, Georgetown • Bob Constable, Cornell • Yasmin Kafai, U Penn • Janet Kolodner, Georgia Tech • Larry Snyder, U Washington • Uri Wilensky, Northwestern Jeannette M. Wing

Adding “C” to STEM = Science, Technology, Engineering, and Mathematics • Time is right. Adding “C” to STEM = Science, Technology, Engineering, and Mathematics • Time is right. – Society needs more STEM-capable students and teachers. Programming Languages and Software Engineering – The Administration understands the importance of STEM. Sensibilities are critical to the “C” in STEM. • Hill Event to promote this vision – Wed, May 29, 2009 12: 00 - 1: 30 PM B 339 Rayburn House Office Building OOPSLA 55 Jeannette M. Wing

Last Word: The Future of Computing is Bright! Last Word: The Future of Computing is Bright!

Drivers of Computing Society Science Technology J. Wing, “Five Deep Questions in Computing, ” Drivers of Computing Society Science Technology J. Wing, “Five Deep Questions in Computing, ” CACM January 2008 DAC 57 Jeannette M. Wing

Drivers of Computing 7 A’s Anytime Anywhere Affordable Access to Anything by Anyone Authorized. Drivers of Computing 7 A’s Anytime Anywhere Affordable Access to Anything by Anyone Authorized. Society Science Technology • What is computable? • P = NP? • (How) can we build complex systems simply? • What is intelligence? • What is information? J. Wing, “Five Deep Questions in Computing, ” CACM January 2008 DAC 58 Jeannette M. Wing

Thank You! Thank You!

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