Computational Sustainability : Computational Methods for a Sustainable Environment, Economy, and Society

1. Computational Sustainability:Computational Methods for a SustainableEnvironment, Economy, and Society Bowdoin Carla P. Gomes (Lead PI Cornell University) Thomas Dietterich (PI Oregon State University) Jon Conrad (Co-PI Cornell University) John Hopcroft (Co-PI Cornell University)

2. Sustainability and Sustainable Development The 1987 UN report, “Our Common Future” (Brundtland Report): • Raised serious concerns about the State of the Planet. • Introduced the notion of sustainability and sustainable development: Sustainability: “development that meets the needs of the present without compromising the ability of future generations to meet their needs.” • Stated the urgency of policies for sustainable development. Gro Brundtland Norwegian Prime Minister Chair of WCED UN World Commission on Environment and Development,1987.

3. Follow-Up Report: Intergovernmental Panel on Climate Change (IPCC) (2007) ”There are no major issues raised in Our Common Future for which the foreseeable trends are favourable.” Erosion of Biodiversity • Examples: • The biomass of fish is estimated to be 1/10 of what it • was 50 years ago and is declining. • At the current rates of human destruction of natural ecosystems, 50% of all species of life on earth will be extinct in 100 years. Global Warming +130 countries [Nobel Prize with Gore 2007]

4. Vision Computer scientists can — and should — play a key role in increasing the efficiency and effectiveness of the way we manage and allocate our natural resources, while enriching and transforming Computer Science.

5. Outline I Research Goals and AgendaII Our TeamIII Project Management, and Collaboration PlanIV Knowledge Transfer, Education, and OutreachV Value-Added as an Expedition

6. Outline I Research Goals and AgendaII Our TeamIII Project Management, and Collaboration PlanIV Knowledge Transfer, Education, and OutreachV Value-Added as an Expedition

7. Overall Goal of our Expedition To establish a new field,Computational Sustainability: Focused on computational methods for balancing environmental, economic, and societal needs for a sustainable future. To inject Computational Thinking into Sustainability that will provide: • new insights into sustainability questions; • new challenges and new methodologies in Computer Science (Analogous to Computational Biology) We will create the Institute for Computational Sustainability (ICS), • to perform and foster research in Computational Sustainability • to establish a vibrant research community, reaching far beyond the participating members of this Expedition.

8. Sustainability Themes

9. Fueldistributors Gasolineproducers Farmers Non-energycrops Foodsupply Consumers Energycrops Energymarket Water quality Environmentalimpact Social welfare Soil quality Economicimpact Biodiversity Local airpollution Sustainability Themes I Conservation and Biodiversity E.g. Wildlife Corridors II Balancing Socio-economic Demands and the Environment E.g. Policies for harvestingrenewable resources III Renewable Energy E.g. Biofuels Ethanol Refinery

10. Computational Challenges

11. Challenges in Constraint Reasoning and Optimization:Conservation and Biodiversity: Wildlife Corridors Wildlife Corridorslink core biological areas, allowing animal, seed, and pollen movement between areas; Typically: low budgets to implement corridors. Computational problem Connection Sub-graph Problem Connection Sub-graph Problem Given a graph G with a set of reserves: Find a sub-graph of G that: contains the reserves; is connected; with cost below a given budget; and with maximum utility Connection Sub-Graph - NP-Hard Worst Case Result --- Real-world problems possess hidden structure that can be exploited allowing scaling up of solutions Science of Computation.

12. Real world instance: Corridor for grizzly bears in the Northern Rockies, connecting: Yellowstone Salmon-Selway Ecosystem Glacier Park Our Expedition is partnering with the Conservation Fund which works with US Fish & Wildlife Service and the Nature Conservancy on preserving habitat in the Northern Rockies . Glacier Park Salmon-Selway Yellowstone (12788 nodes) Scaling up Solutions by Exploiting Structure: Typical Case Analysis Identification of Tractable Sub-problems Streamlining for Optimization Static/Dynamic Pruning 5 km grid (12788 land parcels): minimum cost solution 5 km grid (12788 land parcels): +1% of min. cost Our approach reduced corridor cost from $1 Billion to $10 Million [Conrad et al. 2007] Interdisciplinary Research Project (IRP):Wildlife Corridors (Amundsen, Conrad, Gomes, Selman + postdoc + 2 Ph.D. students )

13. IRP Native Plant Habitat Recovery Victoria (Australia) Combinatorial Auctions IRP Nisource: Conservation Fund (14 states) IRP Joint Public/Private Management for Biodiversity Outreach Cornell’s eBird Citizen Science 10M+ bird observations since 2002, by general public IRP Collective Inference on Markov Models for Modeling Bird Migration Ruby-throated Hummingbird Sightings Negative observations Positive observations Additional Levels of Complexity: Stochasticity, Uncertainty, Large-Scale Data Modeling • Highly stochastic environments • Multiple species (hundreds or thousands), • with interactions (e.g. predator/prey). • Spatially-explicit aspects within-species • Different models of land acquisition • (e.g., purchase, conservation easements, auctions) • typically over different time periods • Dynamical models • Dynamics of species; • Movements and migrations; Themes - Transformative Synthesis: Optimization and Machine Learning Dynamics and Machine Learning Dynamics and Optimization

14. Example of a Biological Growth Function F(x): Logistics map: x t+1 = r xt (1 -  xt),  r is the growth rate Non-linear dynamics We are interestedin problems that involve policy decisions (e.g. when to open/close a fishery ground over time). Uncharted territory: Combinatorial optimization problems with an underlying dynamical model. Transformative Synthesis: New Class of Hybrid Dynamic Optimization Models Challenges in Dynamic Models and Optimization :Balancing Socio-Economic Demands and Environment: Economy Key Issues in Dynamical Models: multiple scales and multistability Increasing Complexity: multiple renewable resources interactions IRP: Rotational Management of Fishing Grounds IRP: Joint Public/Private Management for Biodiversity IRP: Fire Management in Forests

15. Fueldistributors BiofuelRefineries Farmers Non-energycrops Foodsupply Consumers Energycrops Energymarket Water quality Environmentalimpact Social welfare Soil quality Economicimpact Biodiversity Local airpollution Challenges in Highly Interconnected Multi-Agent Systems:Renewable Energy Energy Independence and Security Act(Signed into law in Dec. 2007) Ambitious mandatory goal of36 billion gallons of renewable fuels by 2022 (five-fold increase from current level) IRPLarge Scale Logistics Planning for Biofuels

16. Fueldistributors Gasolineproducers Farmers Non-energycrops Foodsupply Consumers Energycrops Energymarket Water quality Environmentalimpact Social welfare Soil quality Economicimpact Biodiversity Local airpollution Realistic Computational Economic Models • Current approaches limited in scope and complexity • E.g. based on general equilibrium models (e.g., Nash style) • Strong convexity assumptions to keep the model simple enoughfor analytical, closed-form solutions (unrealistic scenarios)  Limited computational thinking Transformative research directions • More realistic computational models in which meaningful solutions can be computed • Large-scale data, beyond state-of-the-art CS techniques • Study of dynamics of reaching equilibrium — key for adaptive policy making! IRP Impact of Biofuels: Dynamic Equilibrium Models IRP Impact of Land-use on Climate

17. Our team has a track record of making compelling scientific discoveries using such an approach. Phase Transitions in Computation Led to interactions between CS,statistical physics, and math Heavy tails in Computation Led to randomization andportfolios in combinatorial search Small world phenomenon Pioneered science of networks [Watts & Strogatz] [Selman & Kirkpatrick] [Gomes et al.] Science of Computation Our approach: The study computational problems as natural phenomena in which principled experimentation, to uncover hidden structure, is as important as formal analysis  Science of Computation,

18. Distributed Highly Interconnected Components / Agents Increasing Complexity Science of Computation Dynamics Data and Uncertainty Constraint Satisfaction and Optimization Complexity levels in Computational Sustainability Problems Overall Research Vision:Transformative Computer Science Research:Driven by Deep Research Challenges posed by Sustainability Design of policies to manage natural resources translating into large-scale optimization and learning problems, combining a mixture of discrete and continuous effects, in a highly dynamic and uncertain environment increasing levels of complexity Study computational problems as natural phenomena  Science of Computation Many highly interconnected components;  From Centralized to Distributed: Computational Resource Economics Multiple time scales  From Statics to Dynamics: Dynamic Models Large-scale data and uncertainty  Machine Learning, Statistical Modeling Complex decision models  Constraint Reasoning and Optimization

19. Constraint Reasoning & Optimization Gomes PI,W, Hopcroft Co-PI SelmanCo-PI, ShmoysCo-PI Renewable Energy Biodiversity Transformative Synthesis: Optimization& Learning Transformative Synthesis: Dynamics and Optimization Resource Economics, Environmental Sciences & Engineering AlbersCo-PI,W, Amundsen, Barrett, Bento, ConradCo-PI, DiSalvo, MahowaldW, MontgomeryCo-PI,W Rosenberg,SofiaW,WalkerAA Environmental & Socioeconomic Needs Data & Machine Learning DietterichPI WongCo-PI ChavarriaH Dynamical Models Guckenheimer Strogatz ZeemanPI,W YakubuAA Transformative Synthesis: Dynamics &Learning Transformative Computer Science Research:Driven by Deep Research Challenges posed by Sustainability Design of policies to effectively manage Earth’s naturalresources translate into large-scale decision/optimization and learning problems, combining a mixture of discrete and continuous effects, in a highly dynamic and uncertain environment  increasing levels of complexity Study computational problems as natural phenomena  Science of Computation Many highly interconnected components;  From Centralized to Distributed: Computational Resource Economics Multiple scales(e.g., temporal, spatial, geographic)  From Statics to Dynamics: Dynamic Models Large-scale data and uncertainty  Machine Learning, Statistical Modeling Science of Computation Complex decision models  Constraint Reasoning, Optimization, Stochasticity Science of Computation

20. Outline I Research Goals and AgendaII Our TeamIII Project Management, and Collaboration PlanIV Knowledge Transfer, Education, and OutreachV Value-Added as an Expedition

21. Multi-institutional, Multidisciplinary Research Team6 Institutions, 7 colleges, 12 departments Bento Res. & Env. Economics Cornell Hopcroft CS Cornell Zeeman Appl. Math Bowdoin Dietterich CS OSU Gomes CS Cornell Mahowald Earth & Atmos. Sci. Cornell Yakubu Appl. Math Howard Strogatz Appl. Math Cornell Montgomery Res. & Env.Economics OSU Shmoys CS & OR Cornell Albers Res. & Env. Econo. OSU Institute for Computational Sustainability Walker Biology & Eng. Cornell Chavarria HPC. PNNL Rosenberg Conservation Biology Cornell Wong CS OSU Selman CS Cornell Guckenheimer Appl. Math Cornell DiSalvo Chemistry Cornell Amundsen Conservation Planning Cons. Fund Conrad Res. and Env. Economics Cornell Barrett Res. & Env. Economics Cornell Sofia Biology Cornell

22. Cornell University, Ithaca, NY Oregon State University Corvallis, OR Tradition of public service as part of its land-grant mission (private & state university) Tradition in agricultural extension service & research stations #1 ranked Forestry School Several centers focusing on sustainability research and dissemination results to community and policy makers Strong Ecosystem Informatics (IGERT, NSF Summer Inst.) . Strong programs in computer science, fisheries & wildlife, atmospheric Sciences, oceanography, and environmental sciences Strong programs in computer science, agriculture, natural resources, biology, renewable energy, and environment Provost’s campus wide initiative in Sustainability Provost’s initiative in Ecosystem Informatics Bowdoin College Brunswick, ME Conservation Fund Nationwide Since 1985, the Fund and partners have safeguarded over 6 million acres of wildlife habitat, forests, and community greenspace. Liberal arts undergraduate college Culture of undergraduate research Non-profit org. focused on conserving natural resources. Pacific Northwest National Laboratory Howard University Washington, DC Historically Black University Department of Energy's (DOE's) laboratory: focus on environmental science, climate change, remediation, and security. Leading producer of African-American Ph.D.s Focus on Science, Technology, Engineering, and Mathematics Access to HPC Systems

23. Institutional Support • Strong institutional support: • From top level management (president, provost) • Researchers eager to share data, problems, and anxious for new computational models to solve their sustainability problems • Cornell Center for a Sustainable Future (CCSF) with • organizational support and matching funds • (5% non-federal money). Institute for Computational Sustainability And this space!

24. Outline I Research Goals and Agenda II Our TeamIII Project Management, and Collaboration PlanIV Knowledge Transfer, Education, and OutreachV Value-Added as an Expedition

25. Integrated Management Institute for Computational Sustainability: Director Carla P. Gomes (PI Cornell University) Associate Director David Shmoys(Co-PI Cornell Univeristy) Deputy Director Thomas Dietterich (PI Oregon State University) Deputy Director Mary Lou Zeeman (PI Bowdoin University) Plan, coordinate, and evaluate exciting and aggressive research, education, and outreach program. Disseminate computational sustainability. Executive Committee: PI’s + Co-PI’s Meets once a month. Advisory Board: Prominent researchers in fields related to computational sustainability, both from the application side (2 people) and the computer science side (2 people). Will also invite an NSF representative. Meets once a year. Collaboration Plan: Through Interdisciplinary Research Projects (IRPs, shared personnel), executive committee meetings, exchange students and faculty, three-day annual meetings, meetings at regular CS conferences.

26. Interdisciplinary Research Projects (IRPs):The Building Blocks of our Expedition Seedling IRPs

27. Nurturing New IRPs Application/Synthesis Facilitators

28. Outline I Research Goals and AgendaII Our TeamIII Project Management, and Collaboration PlanIV Knowledge Transfer, Education, and OutreachV Value-Added as an Expedition

29. Institute Activities Building Research Community Research Coordinating transformative synthesis collaborations Web Portal Catalog Of Problems Panels & tutorials at major conferences Interdisciplinary Research Projects (IRPs) Host visiting Scientists Annual symposium ICS Education Outreach Conservation Fund Postdocs Citizen Science Project Doctoral students Research seminar series Honors projects Cornell Cooperative Extension Cornell Center for a Sustainable Future Summer REU program targeting minority students Computational Sustainability follow-up to State of the Planet course OSU Alliance for Computational Sustainability

30. Education and Outreach Research on Computational Sustainability will significantly broaden the field of computer science and attract a new generation of students who traditionally may not have considered studying computer science --- thus contributing to a revitalization of CS education. Undergraduate and graduate students will be drawn into hands-on computational sustainability research by joining IRP-teams. The ICS will organize regular seminars and an annual symposium and summer school.

31. State of the Planet Course “Students unite to create State of the Planet course” - K.L. Rypien et al., Nature, Vol 447, July 2007, p775 - mentors Tom Eisner and Mary Lou Zeeman (co-PI) • 250 students, 45 different majors. 95% recommend it to their peers: • “… the class … has started me thinking about career paths I hadn’t considered before. I find most of the lectures inspiring and hopeful; they leave me feeling that I can actually make a difference in the world.” • “Amazing course! Very thought-provoking, interesting, varied, and exciting. Best course I've taken at Cornell so far. I have been recommending it to everyone.” We will create a companion course on Computational Sustainability Renewable Energy Food & water Biodiversity Carbon and climate

32. Integration of Research and Outreach Example:Citizen Science at the Cornell Laboratory Of Ornithology • Increase scientific knowledge Gather meaningful data to answer large-scale research questions • Increase scientific literacy Enable participants to experience the process of scientific investigation and develop problem-solving skills • Increase conservation action Apply results to science-based conservation efforts Citizen Science empowers everyone interested in birds to contribute to research.

33. Additional Channels for Impact and Outreach Community, policy makers, local and state leaders Community and Rural Development Institute Lab. of Ornithology K. Rosenberg Institute for Environment Energy & Policy Director: A. Bento Conservation Fund Ole Amundsen CornellCooperative Extension A. Bento Cornell Biofuels Lab L.Walker Northeast Sun Grant Institute at Cornell Director: L. Walker New York Sea-Grant Cornell Extension Jon Conrad OSU IGERT Eco-System Informatics Institutefor ComputationalSustainability Cornell Center forSustainable Future Director: F. DiSalvo USDA ForestrySciences Lab African Food and SecurityNatural Resources Mgmt.Prgm Director: C. Barrett EPA Western Ecology Division Howard UnA.Yakubu Earth & Atmospheric Sciences N. Mahowald Research community & students We will leverage a host of sustainability centers and programs at our 6 institutions.

34. Outline I Research Goals and AgendaII Our TeamIII Project Management, and Collaboration PlanIV Knowledge Transfer, Education, and OutreachV Value-Added as an Expedition

35. Value-Added as an Expedition • Fundamentally new intellectual territory for computer science • Unique societal benefits • Tremendous potential for recruiting new groups (including under-represented minorities) to computer science • Establishing the field of Computational Sustainability requires critical mass and visibility that cannot be achieved with piece-meal efforts • Our research proposal is fundamentally cross-disciplinary: • IRPs require large teams involving both domain scientists and computer scientists • Transformative syntheses are also across disciplines in CS • NSF is the agency best positioned to lead this initiative

36. Summary Expedition will lead to: • Foundational contributions to computer science driven by Sustainability questions. • Societal and environmental impact--- development of computational methods to alleviate sustainability problems (e.g. significant opportunity for more efficient use of natural resources). • Establishment of the field of Computational Sustainability. • Integration of research, education, and outreach.New courses and seminars integrated with Interdisciplinary Research Projects. • Increasing diversity in computer science.Bringing in a new generation of students, traditionally not drawn to CS; also, broadening the public image of CS. Thank You!