Bowdoin
Computational Sustainability:
Computational Methods for a Sustainable
Environment, Economy, and Society
Carla P. Gomes
Thomas Dietterich
Jon Conrad
John Hopcroft
(Lead PI Cornell University)
(PI Oregon State University)
(Co-PI Cornell University)
(Co-PI Cornell University)
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.
UN World Commission on Environment and Development,1987.
Gro Brundtland
Norwegian Prime Minister
Chair of WCED
2
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.
Global Warming
+130
countries
•At the current rates of human destruction of natural
ecosystems, 50% of all species of life on earth will be
extinct in 100 years.
[Nobel Prize
with Gore 2007]
3
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.
4
Outline
I
Research Goals and Agenda
II Our Team
III Project Management, and Collaboration Plan
IV Knowledge Transfer, Education, and Outreach
V Value-Added as an Expedition
5
Outline
I
Research Goals and Agenda
II Our Team
III Project Management, and Collaboration Plan
IV Knowledge Transfer, Education, and Outreach
V Value-Added as an Expedition
6
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.
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Sustainability Themes
8
Sustainability Themes
I Conservation and Biodiversity
E.g. Wildlife Corridors
II Balancing Socio-economic Demands
and the Environment
E.g. Policies for harvesting
renewable resources
III Renewable Energy
Fuel
distributors
Farmers
Energy
crops
Food
supply
Consumers
Energy
market
Water quality
Environmental
impact
Biodiversity
Local air
E.g. Biofuels
Gasoline
producers
Non-energy
crops
Soil quality
pollution
Social welfare
Economic
impact
9
Ethanol Refinery
Computational Challenges
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Challenges in Constraint Reasoning and Optimization:
Conservation and Biodiversity: Wildlife Corridors
Wildlife Corridors link 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:
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.
contains the reserves;
is connected;
with cost below a given budget;
and
with maximum utility
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Real world instance:
Glacier Park
Corridor for grizzly bears in the
Northern Rockies, connecting:
Yellowstone
Salmon-Selway Ecosystem
Glacier Park
Salmon-Selway
(12788 nodes)
Yellowstone
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]
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 .
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
•
Highly stochastic environments
•
Multiple species (hundreds or thousands),
with interactions (e.g. predator/prey).
•
Spatially-explicit aspects within-species
•
Different models of land acquisition
IRP
Nisource:
Conservation
Fund (14 states)
IRP Joint Public/Private
Management for Biodiversity
(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
IRP Native
Plant Habitat
Recovery Victoria
(Australia)
Combinatorial Auctions
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
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Challenges in Dynamic Models and Optimization :
Balancing Socio-Economic Demands and Environment:
Economy
Key Issues in Dynamical Models:
multiple scales and multistability
Example of a Biological Growth Function F(x):
Logistics map: x t+1 = r xt (1 - xt), r is the growth rate
Increasing Complexity: multiple renewable
resources interactions
We are interested in 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
IRP: Rotational Management of Fishing Grounds
IRP: Joint Public/Private Management for Biodiversity
IRP: Fire Management in Forests
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Challenges in Highly Interconnected Multi-Agent Systems:
Renewable Energy
Energy Independence and Security Act
(Signed into law in Dec. 2007)
Ambitious mandatory goal of
36 billion gallons of renewable fuels by 2022
(five-fold increase from current level)
IRP Large Scale Logistics
Planning for Biofuels
Fuel
distributors
Farmers
Non-energy
crops
Energy
crops
Food
supply
Consumers
Energy
market
Water quality
Soil quality
Biofuel
Refineries
Environmental
impact
Biodiversity
Local air
pollution
Social welfare
Economic
impact
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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 enough
for 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!
Fuel
distributors
Farmers
Gasoline
producers
Non-energy
crops
Food
supply
Energy
crops
Energy
market
Water quality
Soil quality
Biodiversity
Consumers
Environmental
impact
Local air
pollution
IRP Impact of Biofuels:
Dynamic Equilibrium Models
Social welfare
Economic
impact
IRP Impact of Land-use on Climate
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,
Our team has a track record of making compelling scientific discoveries using such an approach.
Small world phenomenon
Pioneered science of networks
[Watts & Strogatz]
Phase Transitions in Computation
Heavy tails in Computation
Led to interactions between CS,
statistical physics, and math
Led to randomization and
portfolios in combinatorial search
[Selman & Kirkpatrick]
[Gomes et al.]
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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
Distributed
Highly
Interconnected
Components / Agents
Dynamics
Data and Uncertainty
Constraint Satisfaction and Optimization
Complexity levels in
Computational Sustainability Problems
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Transformative Computer Science Research:
Driven by Deep Research Challenges posed by Sustainability
Design of policies to effectively manage Earth’s natural
resources 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
Constraint
Reasoning
& Optimization
Gomes PI,W, Hopcroft Co-PI
SelmanCo-PI, ShmoysCo-PI
Study computational problems as natural phenomena
 Science of Computation
Many highly interconnected components;
 From Centralized to Distributed:
Computational Resource Economics
Resource Economics,
Environmental
Sciences & Engineering
Multiple scales (e.g., temporal, spatial, geographic)
 From Statics to Dynamics: Dynamic Models
Large-scale data and uncertainty
 Machine Learning, Statistical Modeling
Dynamical
Models
Guckenheimer
Strogatz
ZeemanPI,W
YakubuAA
Complex decision models
 Constraint Reasoning, Optimization, Stochasticity
AlbersCo-PI,W, Amundsen, Barrett, Bento,
ConradCo-PI, DiSalvo, MahowaldW,
MontgomeryCo-PI,W
Rosenberg,SofiaW, WalkerAA
Environmental &
Socioeconomic
Needs
Transformative
Synthesis:
Dynamics &
Learning
Science of Computation
Science of Computation
Data
& Machine
Learning
DietterichPI
WongCo-PI
ChavarriaH
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Outline
I Research Goals and Agenda
II Our Team
III Project Management, and Collaboration Plan
IV Knowledge Transfer, Education, and Outreach
V Value-Added as an Expedition
20
Multi-institutional, Multidisciplinary Research Team
6 Institutions, 7 colleges, 12 departments
Bento
Res. & Env.
Economics
Cornell
Shmoys
CS & OR
Cornell
Strogatz
Appl. Math
Cornell
Mahowald
Earth &
Atmos. Sci.
Cornell
Dietterich
CS
OSU
Gomes
CS
Cornell
Zeeman
Appl. Math
Bowdoin
Hopcroft
CS
Cornell
Yakubu
Appl. Math Montgomery
Howard Res. & Env.
Economics
OSU
Albers
Res. & Env.
Econo.
OSU
Institute for
Computational
Sustainability
Walker
Biology
&
Eng.
Cornell
Chavarria
HPC.
PNNL
Rosenberg
Conservation
Biology
Cornell
Guckenheimer
Appl. Math
Cornell
Sofia
Biology
Cornell
DiSalvo
Chemistry
Cornell
Barrett
Res. & Env.
Economics
Cornell
Amundsen
Conrad
Conservation Res. and Env.
Planning
Economics
Cons. Fund
Cornell
Selman
CS
Cornell
Wong
CS
OSU
21
Cornell University,
Ithaca, NY
Oregon State University
Corvallis, OR
Tradition of public service as part of
its land-grant mission (private & state university)
Several centers focusing on sustainability research and
dissemination results to community and policy makers
Strong programs in computer science, agriculture, natural
resources, biology, renewable energy, and environment
Provost’s campus wide initiative in Sustainability
Bowdoin College
Brunswick, ME
Liberal arts undergraduate college
Culture of undergraduate research
Howard University
Washington, DC
Historically Black University
Leading producer of African-American Ph.D.s
Focus on Science, Technology,
Engineering, and Mathematics
Tradition in agricultural extension service & research stations
#1 ranked Forestry School
Strong Ecosystem Informatics (IGERT, NSF Summer Inst.)
Strong programs in computer science, fisheries & wildlife,
. oceanography, and environmental
atmospheric Sciences,
sciences
Provost’s initiative in Ecosystem Informatics
Conservation Fund
Nationwide
Since 1985, the Fund and partners have safeguarded
over 6 million acres of wildlife habitat, forests,
and community greenspace.
Non-profit org. focused on conserving natural resources.
Pacific Northwest
National Laboratory
Department of Energy's (DOE's) laboratory: focus on
environmental science, climate change, remediation,
and security.
Access to HPC Systems
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!
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Outline
I Research Goals and Agenda
II Our Team
III Project Management, and Collaboration Plan
IV Knowledge Transfer, Education, and Outreach
V Value-Added as an Expedition
24
Integrated Management
Institute for Computational Sustainability:
Director
Associate Director
Deputy Director
Deputy Director
Carla P. Gomes
David Shmoys
Thomas Dietterich
Mary Lou Zeeman
(PI Cornell University)
(Co-PI Cornell Univeristy)
(PI Oregon State University)
(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.
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Interdisciplinary Research Projects (IRPs):
The Building Blocks of our Expedition
Seedling IRPs
IRP Name
Faculty Team
1.
Wildlife Corridors for Grizzly Bears
Amundsen, Conrad, Gomes, Selman,
Shmoys
2.
Biofuels
Bento, Gomes, Mahowald, Shmoys,
Strogatz, Walker, Wong
3.
Bird Conservation
Rosenberg, Conrad, Dietterich, Gomes,
Hopcroft, Strogatz, Zeeman
4.
Native Plant Habitat Recovery in Victoria,
Australia
Dietterich, Gomes, Selman
5.
Joint Public/Private Management for
Biodiversity
Amundsen, Montgomery, Dietterich,
Gomes, Hopcroft
6.
Fire Management in Forests
Albers, Conrad, Guckenheimer, Selman
7.
Rotational Management of Fishing Grounds
Conrad, Guckenheimer, Yakubu, Zeeman
8.
Pastoral Systems in East Africa
Barrett, Conrad, Wong
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Nurturing New IRPs
Application/Synthesis Facilitators
Science of Dynamics & Optimization
Computation Optimization & Learning
Learning &
Dynamics
✔
Conservation
and Biodiversity
✔
✔
Balancing
Socio-Economic
Demands and
Environment
✔
✔
✔
Renewable
Energy
✔
✔
✔
Dietterich,
Hopcroft,
Selman
Guckenheim
er, Shmoys,
Zeeman
Dietterich,
Gomes,
Hopcroft
Agent
Integration
✔
Guckenheim
er, Wong
Dissemination,
Outreach, &
Diversity
✔
Conrad,
Montgomery,
Gomes
✔
✔
Albers,
Conrad,
Guckenheimer
✔
✔
Bento,
Gomes, Shmoys
Bento,
Gomes,
Shmoys
Hopcroft,
Zeeman
Application
Facilitators
Application Areas
Computational Themes
Synthesis Facilitators
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Outline
I Research Goals and Agenda
II Our Team
III Project Management, and Collaboration Plan
IV Knowledge Transfer, Education, and Outreach
V Value-Added as an Expedition
28
Institute Activities
Building Research Community
Research
Coordinating
transformative
synthesis
collaborations
Web Portal
Panels & tutorials at
major conferences
Interdisciplinary
Research
Projects (IRPs)
Host visiting
Scientists
ICS
Education
Outreach
Postdocs
Doctoral
students
Annual
symposium
Catalog
Of
Problems
Honors
projects
Research
seminar
series
Summer REU program targeting
minority students
Computational Sustainability follow-up to
State of the Planet course
Citizen Science
Project
Cornell Center for a
Sustainable Future
Conservation
Fund
Cornell
Cooperative
Extension
OSU Alliance for Computational
Sustainability
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.
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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
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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.
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Additional Channels for Impact and Outreach
Lab. of Ornithology
K. Rosenberg
Community,
policy makers,
local and
state leaders
Institute for Environment
Energy & Policy
Director: A. Bento
Conservation Fund
Ole Amundsen
Cornell
Biofuels Lab
L.Walker
Community and Rural
Development Institute
Cornell
Cooperative Extension
A. Bento
Northeast Sun Grant
Institute at Cornell
Director: L. Walker
New York Sea-Grant
Cornell Extension
Jon Conrad
OSU IGERT
Eco-System
Informatics
Institute
for Computational
Sustainability
USDA Forestry
Sciences Lab
EPA Western
Ecology Division
Howard Un
A.Yakubu
Research
community
& students
Cornell Center for
Sustainable Future
Director: F. DiSalvo
African Food and Security
Natural Resources Mgmt.Prgm
Director: C. Barrett
Earth & Atmospheric
Sciences
N. Mahowald
We will leverage a host of sustainability centers and programs at our 6 institutions.
Outline
I Research Goals and Agenda
II Our Team
III Project Management, and Collaboration Plan
IV Knowledge Transfer, Education, and Outreach
V Value-Added as an Expedition
36
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
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Summary
Expedition will lead to:
1) Foundational contributions to computer science driven by
Sustainability questions.
2) Societal and environmental impact --- development of computational
methods to alleviate sustainability problems (e.g. significant
opportunity for more efficient use of natural resources).
3) Establishment of the field of Computational Sustainability.
4) Integration of research, education, and outreach. New courses and
seminars integrated with Interdisciplinary Research Projects.
5) 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!
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