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Computational Sustainability : Computational Methods for a Sustainable Environment, 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).

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slide1

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)

sustainability and sustainable development
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.

follow up report intergovernmental panel on climate change ipcc 2007
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]

vision
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.

slide5

Outline

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

slide6

Outline

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

overall goal of our expedition
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.
sustainability themes1

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

slide11
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.

slide12

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 )

additional levels of complexity stochasticity uncertainty large scale data modeling

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

challenges in dynamic models and optimization balancing socio economic demands and environment

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

challenges in highly interconnected multi agent systems renewable energy

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

slide16

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

science of computation

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,

slide18

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

transformative computer science research driven by deep research challenges posed by sustainability

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

slide20

Outline

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

multi institutional multidisciplinary research team 6 institutions 7 colleges 12 departments
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

slide22

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

slide23

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!

slide24

Outline

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

integrated management
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.

interdisciplinary research projects irps the building blocks of our expedition
Interdisciplinary Research Projects (IRPs):The Building Blocks of our Expedition

Seedling IRPs

nurturing new irps
Nurturing New IRPs

Application/Synthesis Facilitators

slide28

Outline

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

institute activities
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

education and outreach
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.

state of the planet course
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

slide32
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.

additional channels for impact and outreach
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.

slide34

Outline

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

value added as an expedition
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
summary
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!