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Computational Sustainability

Carla P. Gomes Institute for Computational Sustainability

Computing and Information Science Cornell University

Role of Information Sciences and Engineering in Sustainability

RISES DC, Feb. 2011

Expeditions in Computing

(CISE)

Support:

The Institute for Computational Sustainability (ICS) Research Team

29 graduate students

24 undergrad. students

Expeditions in Computing

(CISE)

Support:

ICS members and collaborators for their many contributions towards the development of a vision for

research, education, and outreach activities in the new area of Computational Sustainability

3

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:

Sustainable Development: development that meets the needs of the present without compromising the ability of future generations to meet their needs.

Gro Brundtland Norwegian

Prime Minister Chair of WCED

UN World Commission on Environment and Development,1987.

4

Follow-Up Reports: Intergovernmental Panel on Climate Change (IPCC 07) Global

Environment Outlook Report (GEO 07)

[Nobel Prize with Gore 2007] +130

countries

Global Warming

Erosion of Biodiversity Examples:

•!At the current rates of human destruction of natural ecosystems, 50% of all species of life on earth will be extinct in 100 years.

•!The biomass of fish is estimated to be 1/10 of what it was 50 years ago and is declining. Worm et al. (2006)

There are no major issues raised in Our Common Future for which the foreseeable trends are favourable.

Wilson, E.O. The Future of Life (2002) .

Safe Operating Boundaries: Crucial Biophysical Systems

5 Source: Planetary Boundaries: A Safe Operating Space for Humanity, Nature, 2009

Sustainability: Interlinked environment, economic, and social issues

Our Common Future recognized that environmental, economic and social issues are interlinked.

"The economy only exists in the context of societies,

and both society and economic activity are constrained by the earth s natural systems.

"A secure future depends upon the health of all 3 systems (environment, society, economy).

6

Sustainable Development encompasses balancing environmental, economic, and

societal needs.

7

Challenging Sustainability Problems Complex Dynamical Systems

Sustainability problems are unique in scale, impact, complexity, and richness:

Often involving multiple and highly interconnected components and players, in highly dynamic and uncertain environments.

ecosystems.noaa.gov

Smart Grid: Complex Digital Ecosystem

Natural Ecosystem

Offer challenges but also opportunities for the advancement of the state of the art in

computing and information science.

8

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 Computing and Information Science and related disciplines.

Computational Sustainability: Vision

We need critical mass in the new field of Computational Sustainability!!!

9

I Examples of Computational Sustainability problems

highlighting research themes II Our research themes III Building a community in Computational Sustainability IV Conclusions

Outline

10

E.g. Conservation and Biodiversity

I Environment

II Socio-Economic Systems

Sample of Computational Sustainability Problems

E.g. Poverty mapping and poverty reduction, and harvesting policies.

III Energy

E.g. Smart grid, Material discovery and Biofuels Farmers

Fuel distributors

Environmental impact

Consumers Energy crops

Non-energy crops

Social welfare

Food supply

Gasoline producers

Water quality

Soil quality

Local air pollution

Biodiversity

Energy market

Economic impact

11

Conservation and Biodiversity

Combatting Biodiversity Loss: Landscape Connectivity

Ideas from circuit theory for mapping critical linkages in complex landscapes

Brad McRae et al.

Connectivity for mountain in southern California. Blue - low current density (low densities of dispersing mountain lions); Yellow - movement bottlenecks, where connectivity is most vulnerable to hab destruction. Red - high current flow high priority areas for conservation or restoration.

With limited funding and constant threats of habitat loss how do we choose which habitats to protect so that landscapes will stay well-connected for

wild animal species?

Maintaining movement and connectivity across landscapes can help reduce inbreeding,

increase genetic diversity, and/or colonize new habitat.

Opportunities for new computational models integrating ecological and economic constraints.

Wildlife Corridors link core biological areas, allowing animal movement between areas. Typically: low budgets to implement corridors.

Example:

Goal: preserve grizzly bear populations in the U.S. Northern Rockies by creating wildlife corridors connecting 3 reserves:

Yellowstone National Park Glacier Park / Northern Continental Divide Salmon-Selway Ecosystem

Combatting Habit Loss and Fragmentation: Wildlife Corridors

cost suitability

14

Conservation and Biodiversity: Wildlife Corridors

Challenges in Constraint Reasoning and Optimization

Wildlife corridor design Computational problem " Connection Sub-graph Problem

Find a sub-graph of G that: contains the reserves; is fully connected; with cost below a given budget; and with maximum utility

Connection Sub-Graph - NP-Hard

Given a graph G with a set of reserves:

Connection Sub-graph Problem

Worst Case Result --- Real-world problems possess hidden structure that can be exploited allowing

scaling up of solutions.

Conrad, Dilkina, Gomes, van Hoeve, Sabharwal, Sutter; 2007-2010

Interdisciplinary Research Project:

Minimum Cost Corridor for the Connected Sub-Graph Problem

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25 km2 hex 1288 Cells $7.3M 2 hrs

50x50 grid 167 Cells $1.3B <1 sec

40x40 grid 242 Cells $891M <1 sec

25x25 grid 570 Cells $449M <1 sec

10x10 grid 3299 Cells $99M 10 mins

25 km2 hex Extend with 2xB=$15M 10x in Util

What if we were allowed extra budget? Need to solve problems large number of cells! ">64*4.+*+58'!,,(&,''

Models Are Important!!!

Single Commodity Flow

Directed Steiner Tree

Captures Better the Connectedness Structure #!

Exponential Number of Constraints $!

Provides good upper bounds (discover good cuts with ML)$!

Quite compact (poly size)

Dilkina and Gomes 2010

Science of Computation - Understanding Structure: Typical Case Analysis and Identification of Critical Parameters

(Synthetic Instances)

How is hardness affected as the budget fraction is varied?

Problem evaluated on semi-structured graphs

m x m lattice / grid graph with k terminals Inspired by the conservation corridors problem

Place a terminal each on top-left and bottom-right Maximizes grid use

Place remaining terminals randomly Assign uniform random costs and utilities from {0, 1, !, 10}

Utility Gap (Optimally Extended Min cost/ Optimal)

Runtime

From 6x6 to 10x10 grid (100 parcels): 1000 instances per data-point;

Runtime for Optimal Solution

No reserves: pure optimization

3 reserves

3 reserves

Scaling up Solutions by Exploiting Structure

•!Synthetic generator / Typical Case Analysis •!Identification of Tractable Sub-problems •!Exploiting structure via Decomposition

Static/Dynamic Pruning •!Streamlining for Optimization •!New Encodings

5 km grid (12788 land parcels):

minimum cost solution

5 km grid (12788 land parcels):

+1% of min. cost

Glacier Park

Yellowstone

Salmon-Selway

Real world instance: Corridor for grizzly bears in the Northern Rockies, connecting:

Yellowstone Salmon-Selway Ecosystem Glacier Park

(12788 nodes)

Our approach allows us to handle large problems and reduce corridor cost dramatically (hundreds of millions of dollars)

compared to existing approaches while providing guarantees of optimality in terms of utility:

Optimal or within 1% of optimality for interesting budget levels.

Multiple Species

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Grizzly Bear

Wolverines

Lynx

Collaborators: Michael K. Schwartz USDA Forest Service, Rocky Mountain Research Station Claire Montgomery Oregon State University

Identification of new problems to address multiple species: E.g. Steiner Multigraph Problem Upgrading Shortest Path Problem Dynamics and Game Theory : •! Study dynamics of interactions •! How to be fair??? •! What metrics?

Outreach and Education

20

Pedagogical Games Shortest path, Steiner trees, and much more about Computational Sustainability

Lots of undergrad/Meng students Having fun designing games for

A Travel Museum on Computational Sustainability

Edutainment Video Games for

middle school

Boynton Middle School Math Day

Effort led by David

Schneider

21

Connectivity Problems: Other applications

!! What characterizes the connection between two individuals?

The shortest path? Size of the connected component? A good connected subgraph?

!! Which people have unexpected ties to any members of a list of other individuals?

[Faloutsos, McCurley, Tompkins 04]

Network of Pandemic Influenza

Robert J. Glass,* Laura M. Glass,† Walter E. Beyeler,* and H. Jason Min* 2006 Facebook Network

If a person is infected with a disease, who else is likely to be?

Sensor Web as predicted by Matt Heavner

at University of Alaska Southeast. Sensor and Wireless Networks

22

Bird Conservation

Information Sciences D. Fink

Red Cockaded Woodpecker (RCW) is a federally endangered species

Current population is estimated to be about 1% of original stable population (~12,000 birds)

Conservation Funds manages Palmetto Peartree Preserve (North Carolina) 32 active RCW territories (as of Sept 2008)

Conservation and Biodiversity: Reserve Design for Bird Conservation

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Goal: Increase RCW population level Management options:

Prioritizing land acquisition adjacent to current RCW populations

Building artificial cavities Translocation of birds

Computational Challenge: scaling up solutions for considering a large number of years (100+) "" decomposition methods and exploiting structure

Red Cockaded W.

Biological and Ecological Model

Management Decisions:

Land acquisition Artificial cavities

Translocation of birds

Must explicitly consider interactions between biological/ecological

patterns and management decisions

Conservation and Biodiversity: Reserve Design for Bird Conservation

i

j

k

pkj

pij

1-!

t = 1 t = 2

t = 3

Stochastic diffusion model (movement and survival patterns)

in RCW populations Stochastic optimization model

Decisions: where and when to acquire land parcels Goal: Maximize expected number of surviving RCW

Maximizing the Spread of Cascades

Approaches: Sample Average Approximation Upper confidence Bounds

Influencing Cascades in Networks: Other Examples

Maximizing or Minimizing spread

!! Business Networks: Technology adoption among friends/peers. !! Social Networks:

Spread of rumor/news/articles on Facebook, Twitter, or among blogs/websites.

Targeted-actions (e.g. marketing campaigns) can be chosen to maximize the spread of these

phenomena.

Maximize the spread Minimize the spread !! Epidemiology: Spread of disease

–! In human networks, or between networks of households, schools, major cities, etc.

–! In agriculture settings. !! Contamination: The spread of

toxins / pollutants within water networks.

!! Invasive species Mitigation strategies can be chosen to

minimize the spread of such phenomena.

Viral Marketing

[Domingos and Richardson, KDD 2001]

[Kempe, Kleinberg, and Tardos, KDD 2003] [Krause and Guestrin 2007]

26

Additional Levels of Complexity: Large-Scale Data Modeling, Stochasticity, Uncertainty,

•! How to estimate species distributions and habitat suitability

•! Movements and migrations; •! Understanding species interaction •! Climate change •! Stochasticity and Uncertainty

•! How to get the data?

•! Other factors (e.g., different models of land conservation (e.g., purchase, conservation easements, auctions) typically over different time periods).

27

How to estimate species distributions?

Eastern Phoebe Migration

Steven Philips, Miro Dudik & Rob Schapire

Maxent Information Sciences

D. I. MacKenzie, J. D. Nichols, G. B. Lachman, S. Droege, J. A. Royle, and C. A. Langtimm. 2002

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Kelling

Active research area bringing together machine and statistical learning:

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28

eBird: Citizen Science at the Cornell Lab. Of Ornithology

The Citizen Science project at the Lab of Ornithology at Cornell empowers everyone interested in birds — from research labs to backyards to remote

forests, anyone who counts birds – to contribute to research.

!! 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

Examples of research and outreach outputs:

CLO: Track record of influencing

conservation science.

Online Research Kit The State of The Birds

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Processing and Analysis Information Sciences

eBird

Citizen Science and HCI

""How to infer and factor in expertise level of observer?

""How to engage citizens? Ideally to the desired locations

"" Active Learning: where to sample next? How to reduce uncertainty & increase accuracy of species distribution models

US eBirdLocations

Sampling bias:

Highly concentrated data

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' More --- next talk by Bill Tomlinson

Monitoring of Global Forest Cover !! Automated Land change

Evaluation, Reporting and Tracking System (ALERT) •! Planetary Scale Information System for

assessment of disturbances in the global forest ecosystem: •! Forest fires, droughts, floods, logging/deforestation, conversion to agriculture

!! This system will help

•! quantify the carbon impact of changes in the forests

•! Understand the relationship to global climate variability and human activity

•! Provide monitoring and verification system needed for the UN REDD+ program for saving tropical forests

!! Provide ubiquitous web-based access to changes occurring across the globe, creating public awareness

Slide by Vipin Kumar

Planetary Skin ! a global nervous system that will integrate land-sea-air-and-space-based senso helping the public and private sectors make decisions to prevent and adapt to climate change. Time

Planetary Skin Institute

Information technology can make a huge impact on the environmental community by helping develop global datasets (e.g., for

water, food, energy), that are key for sustainability research.

aR'

Sensing and Monitoring for Sustainability

!! Want most useful information at minimum cost !! Exploit structure to efficiently find near-optimal solutions

–! Many important sensing utility functions are submodular "E.g.: Greedy algorithm finds near-optimal set of observations

!! Resulting algorithms perform well on real applications –! E.g., top score in Battle of the Water Sensor Networks challenge –! Can handle robustness, complex constraints, dynamics, !

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Slide by Andreas Krause

Sensor networks for monitoring environments: data collection, analysis, synthesis, and inference in large-

scale sensor networks.

Understanding Species Interactions: Food Webs

!! Food webs who eats whom among species within their natural habitats –! Nodes – species –! Edges – feeding relationships

between the species Question how does Nature balance the abundance of different species? Can we predict the consequences of invasive alien species?

Can we predict the consequences of extinction due to biodiveristy loss?

33

Neo D. Martinez Pacific Ecoinformatics and Computational Ecology Lab

www.FoodWebs.org

Understanding Species Interactions: Network Science - Food Webs




34


www.FoodWebs.org

Understanding Species Interactions: Network Science - Food Webs




35


www.FoodWebs.org

Network modeling, prediction, and optimization –

challenging research area when considering the dynamics of nodes.

Key to understanding coupled human-natural systems and allow

prediction of potential ecological and economic consequences of natural

and human-driven forces acting upon them.

Understanding Climate Change: A Data-Driven Approach

•!Climate change is the defining issue of our era •!Actionable predictive insights are required to inform policy •!Physics-based models are essential but not adequate

–! Models make relatively reliable predictions at global scale for ancillary variables such as temperature

–! But provide the least reliable predictions for variables that are crucial for impact assessment such as regional precipitation

Regional hydrology exhibits large variations among major IPCC model projections

Disagreement between IPCC models

Predictive Modeling Enable predictive modeling of typical and extreme behavior from multivariate spatio-temporal data

Relationship Mining Enable discovery of complex dependence structures: non-linear associations or long range spatial dependencies

Complex Networks Enable studying of collective behavior of interacting eco-climate systems

High Performance Computing Enable efficient large-scale spatio-temporal analytics on exascale HPC platforms with complex memory hierarchies

Transformative Computer Science Research •!Science Contributions

–! Data-guided uncertainty reduction by blending physics models and data analytics

–! A new understanding of the complex nature of the Earth system and mechanisms contributing to adverse consequences of climate change

•!Success Metric –! Inclusion of data-driven analysis as a standard part of climate

projections and impact assessment (e.g., for IPCC)

... data-intensive science [is] …a new, fourth paradigm for scientific exploration." - Jim Gray

Project aim: A new and transformative data-driven approach that complements physics-based models and improves prediction of the potential impacts of climate change

Lead PI: Vipin Kumar

37

Socio-Economic Systems: Poverty, Agricultural Systems, Harvesting Policies

Poverty maps

Identifying the poor is the first essential step

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Targeting maps

Targeting the best response to reduce poverty ,3+>*4#>$63F'I$$''How to estimate the impact and marginal returns of different assets?

Poverty interventions need to be targeted to specific areas Asset-based investments have spatially-varying marginal returns

'

Policies for Poverty Reduction:

Which set of interventions to apply to each targeted area to maximize the poverty reduction, subject to resource constraints (e.g. budget))?

Challenging problems at the intersection of machine learning and optimization, applied economics and social science. J?%+,'D4%%&['4#2''J$%&8'c4#"'

Average marginal returns of different assets (Uganda)

AI for Development

40

Catastrophe Modeling for Rwandan Disease Surveillance

Can mobile phones be used as an early warning system for disease outbreaks? Bayesian anomaly detection algorithms A. Kapoor, N. Eagle, E. Horvitz

Spatiotemporal Diffusion of Contraceptive Norms

How do contraceptive norms spread through rural areas of the developing world? Spatiotemporal diffusion models have the potential to better evaluate the efficacy of HIV prevention techniques and inform policy decisions related to public health. – H. Yoshioka, N. Eagle

Many other exciting projects!!!!

41

Harvesting Policies Fishery Management

Natural Resource Management: Policies for harvesting renewable resources

!! The biomass of fish is estimated to be 1/10 of what it was 50 years ago and is declining (Worm et al. 2006).

!! The state of the world s marine fisheries is

alarming

!! Researchers believe that the collapse of the world s major fisheries is primarily the result of the mismanagement of fisheries (Clark 2006; Costello et al. 2008).

There is therefore a clear urgency of finding ways of defining policies for managing fisheries is a sustainable manner.

Over Harvesting

43

Natural Resource Management: Policies for harvesting renewable resources

Economy

Increasing Complexity: more complex models and multiple

species interactions

Example of a Biological Growth Function F(x): Logistic map: x t+1 = r xt (1 - xt), r is the growth rate

We are interested in identifying policy decisions (e.g. when to open/close a fishery ground over time).

Combinatorial optimization problems with an underlying dynamical model. Class of Computationally Hard Hybrid Dynamic Optimization Models

Clark 1976; Conrad 1999; Ang, Conrad, Just, 2009; Ermon , Conrad, Gomes and Selman 2010

An Application to the Pacific Halibut Fishery in Area 3A Regulated by International Pacific Halibut Commission (IPHC)

Pacific Halibut Fishery in Area 3A

Ermon , Conrad, Gomes and Selman UAI 2010

General MDP Framework

Resource dynamics Closed loop policy To maximize total discounted utility

E/$(#5'*&d'e&M',5$6C',+F&' >5$6?4,)6+58'

Goal: policy for optimal exploitation over time

" natural MDP formulation

Resource growing over time Uncertain dynamics Economics of harvesting

Ermon , Conrad, Gomes and Selman UAI 2010

Main result

!! Fixed costs and variable costs !! Concavity of the growth function !! Robust optimization framework (Worst-case for Nature)

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The Pacific Halibut Fishery in Area 3A

!! Regulated by International Pacific Halibut Commission (IPHC)

Optimal policies for management of renewable resources using MDPs

Total Allowable Catch Policy " Constant Proportion Policy

"rate of fishing ~ 12.3% of estimated stock.

Our Result: Policies with periodic closures of a fishery can outperform Constant Proportion Policies

Ermon, Conrad, Gomes, Selman, UAI 2010

Markov Decision Processes (MDPs) to Model for many renewable resource allocation problems.

Renewable resources: forests and fisheries

Problems with a similar mathematical structure:

- Pollution management, - Invasive species control, - Supply chain management and - Inventory control, and many more.

General Approach to model renewable resources with complex dynamics!!!!

Optimal policies for management of renewable resources using MDPs Ermon, Conrad, Gomes, Selman, UAI 2010

%!Energy

Energy efficiency and renewable energy

49

Energy Efficiency

!! Global warming and climate change concerns have led to major changes in energy policy in many industrial countries.

!! There are tremendous opportunities to increase energy efficiency, such as through the design of control systems for smart buildings, smart grids, smart vehicles, data centers, etc.

–! For example, in the United States buildings consume 39% of the energy used nationwide and 70% of the electric energy used. Smart buildings

Data Centers

!! Environmental Protection Agency estimates data center capacity will grow 10% year in next decade

!! Data center costs "!increase 20% year (v.s. 6% for overall IT). "!Huge Inefficiencies - server utilization rarely exceeds 6% and facility

utilization as low as 50%. (McKinsey & Co Report 2008)

!! Co2 – carbon emissions of the world s data centers

& emissions of Argentina and the Netherlands (McKinsey & Co Report 2008)

51

Main reasons for companies to try to cut energy costs

The IT industry and academia are actively researching new ways of increasing energy efficiency ----- advanced power management hardware,

smart cooling systems , virtualization tools, dense server configurations, etc. But increasing energy efficiency is not the main path towards reducing carbon emissions

Renewable Energy

!! The development of renewable energy sources that are cleaner and generate little or no carbon can have a much greater environmental impact.

!! There has been considerable technological progress in the area of renewable energy sources, in part fostered by government incentives.

52

Renewable Energy

53

Energy Independence and Security Act (2007) DOE was charged with implementing the Smart Grid:

Complex Digital Ecosystem

For example consumers will have smart meters that can 1.! track energy consumption, 2.! monitor individual power circuits in the

home, 3.! control smart appliances and actively

manage their energy use; 4.! dynamically select different providers, 5.! sell excess energy (e.g., car battery,

wind, biomass) to grid, etc

"from largely non-digital, electromechanical grid to a network of digital systems and power infrastructure; "from a centralized, producer-controlled network to a more decentralized system allowing greater consumer and local producer interaction.

Research in sensing and measuring technologies, advanced control methods (monitor and respond to events), dynamic pricing, improved interfaces,

decision support and optimization tools, and security and privacy.

How to integrate the different sources into the smart grid ?

challenging dynamical problems!!!

What is the best mix of

energy generation technologies?

What is the best mix of storage technologies?

Where to locate renewable

energy plants?

Challenge of Managing the Power Grid: Balancing different supply and demand sources

US Power Grid

Total energy produced must be equal to the amount consumed by the system loads AT ALL TIMES:

Managing the Power Grid is a Balancing Act!!!

55

Ambitious mandatory goal of 36 billion gallons of renewable fuels by 2022

(five-fold increase from 2007 level)

Energy Independence and Security Act (Signed into law in Dec. 2007)

Farmers

Fuel distributors



Non-energy crops

Social welfare

Food supply

Gasoline producers

Water quality

Soil quality

Local air pollution

Biodiversity

Energy market

Economic impact

Advanced Biofuels ( Cleaner ) (non food crops and biomass )

Switchgrass Wood Waste Animal Waste Municipal Waste

First generation Biofuels

Energy Efficiency and Renewable Energy: Biofuels

Computational challenges:

Large Scale Logistics Planning Realistic Computational Models to Evaluate Impact

56

Feedstock Map

Large Scale Stochastic Logistics Planning for Biofuels

Large-scale investment in new technology provides exciting logistical planning and optimization challenges and opportunities

Biomass Map

Potential biorefinery locations

Transportation Network (Roads, Rail, Marine)

Distribution Terminals and Inter-Modal Facilities to

Transfer Liquid Fuel

Resulting optimization models are beyond the scope of the current state of the art in several ways: - large-scale input; - stochastic nature (e.g.,feedstock

and demand) " new models to capture

uncertainty "!new stochastic

optimization algorithms;

- dynamics of evolution of demand and capacity

Farmers

Fuel distributors



Non-energy crops

Social welfare

Food supply

Gasoline producers

Water quality

Soil quality

Local air pollution

Biodiversity

Energy market

Economic impact

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!

Impact on Land-use Impact on food prices?

Realistic Computational Models and Metrics to Study Sustainability Impacts

(e.g. Impact of Renewable Energy Sources )

How to measure risks/ predict rare events?

Life Cycle Analysis General Equilibrium

Models

Models and Metrics!!!!!

Complex Adaptive Systems and

Multi-Agent Systems

Multi-agent systems, multi-agent equilibrium models, game theory, and design of effective mechanisms and policies for the exchange of goods.

deling and control of complex high-dimensional systems ombining physics-based models, model-based reasoning,

optimization and control methods, and machine learning models built from large-scale

heterogeneous data.

Farmers

Fuel distributors



Non-energy crops

Social welfare

Food supply

Gasoline producers

Water quality

Soil quality

Local air pollution

Biodiversity

Energy market

Economic impact

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!

Impact on Land-use Impact on food prices?

Realistic Computational Models and Metrics to Study Sustainability Impacts

(e.g. Impact of Renewable Energy Sources )

How to measure risks/ predict rare events?

Life Cycle Analysis General Equilibrium

Models

Models and Metrics!!!!!

Complex Adaptive Systems and

Multi-Agent Systems

Multi-agent systems, multi-agent equilibrium models, game theory, and design of effective mechanisms and policies for the exchange of goods.

Modeling and control of complex high-dimensional systems by combining physics-based models, model-based reasoning,

optimization and control methods, and machine learning models built from large-scale

heterogeneous data.

59

The role of Experimental Research

Crop Soil Science Material Discovery

Agricultural Systems and GHG s emissions

60

Agriculture is the primary driver of land use change and deforestation and an important source of Greenhouse Gases (GHG)s: 52% and 84% of global anthropogenic CH4 and N2O, significant C02

Nitrogen Based Fertilizers

31%

Source of Greenhouse Gases

Nitrogen Based Fertilizers

Nitrogen Cycle and Fertilizers

61

Spatial and temporal analysis of Nitrogen cycle

Study of fertilizers and design of Experiments

Collaborator Harold van Es

Dead Zone in Gulf of Mexico

Fertilizers have played a key role in the increase of food production but they key culprits in the production of greenhouse gases emissions creating dead zones

Townsend & Howarth (2010) Scientific American

Design of Scientific Experiments (4 Treatments: A,B,C,D)

Perfectly balanced 2.33

Below average Above average

Latin Square

Spatially Balanced Latin Square

!! Hybrid IP/CSP based –! Assignment formulation –! Packing formulation –! Different CSP models

!! SAT/ CSP based approach –! State of the art model for Latin Square + symmetry breaking by initializing first row and column (SBDD doesn t help; this is not a completion problem)

!! Local search based approach

These approaches do not scale up

max order 6

(Target number 30).

63

Science of Computation: Discovering patterns, laws, and hidden structure

in computational phenomena

Streamlining Constraint Reasoning

Discovery of structural properties across solutions (machine learning); Divide ( streamline ) the search space by imposing such additional properties.

Design of Scientific Experiments for Studying Fertilizers – Spatially Balanced Latin Squares Existence of SBLS – open problem in combinatorics

"!Streamlining "!Scaling up of solutions

Gomes and Sellmann 2003/2004

YES: XOR-Streamlining based on random parity constraints

(Valiant and Vazirani 1986, Unique SAT) Provable bounds for Counting and Sampling.

Gomes, Sabharwal, Selman, 2006

Discovery of Construction for SBLS for experimental design

LeBras, Perrault, Gomes, 2010

Stre

amlin

ing

by P

erm

utat

ion

of

Com

plet

e C

olum

ns o

f a

Cyc

lic L

atin

Squ

are

SBLS Order 35

Can we find an domain independent way of streamlining?

64

Material Discovery for Fuel Cell Technology

Material Discovery for Fuel Cell Technology

65

Goal of Material Discovery: –! Find new products –! Find product substitutes –! Understand material properties

Approach: Analysis of inorganic libraries

"Sputter 2 or 3 metals (or oxides) onto a silicon wafer (which produces a thin-film) "!Use x-ray diffraction to study crystallographic structure of the thin-film

"!Note: electromagnetic radiation experiments are very expensive!!

Example: study of platinum-tantulum library showed correlation between crystallographic phase and improved fuel cell oxidation catalysis (Gregoire et al 2010)

Fuel Cell

Applications

Ronan LeBras, Damoulas, John M. Gregoire, Sabharwal, Gomes, van Dover

Problem Definition

66

Problem Definition

67

Fe

Al

Si

Problem Definition

68

Fe

Al

Si

(38% Fe, 45% Al, 17% Si)

Problem Definition

69

Fe

Al

Si

(38% Fe, 45% Al, 17% Si)

[Source: Pyrotope, Sebastien Merkel]

Problem Definition

70

Fe

Al

Si

(38% Fe, 45% Al, 17% Si)

15 20 25 30 35 40 45 50 55 600

20

40

60

80

100

120

The Problem: Labeling Points with Phase(s)

71

Fe

Al

Si

15 20 25 30 35 40 45 50 55 600

20

40

60

80

100

120

15 20 25 30 35 40 45 50 55 600

20

40

60

80

100

120

INPUT:

Fe

Al

Si

15 20 25 30 35 40 45 50 55 600

20

40

60

80

100

120

15 20 25 30 35 40 45 50 55 600

20

40

60

80

100

120

15 20 25 30 35 40 45 50 55 60 650

10

20

30

40

50

60

70

pure phase regions

UNDERLYING PHYSICAL STRUCTURE:

Fe

Al

Si

Labeled regions

mixed phase region

mixed phase region

OUTPUT:

!!

!!+""

""

NP-hard

Synthesis of Constraint Reasoning and Machine Learning Approaches for

Material Discovery Standard Clustering Approaches: Based on pure Machine learning techniques + Good at providing a rough , data driven,

global picture + Can incorporate complex dependencies

and global similarity structure - Easily miss critical details (physical properties)

72

Two global alignment kernels for a ternary problem instance

Optimization Model

Constraint reasoning and optimization + Great at enforcing the detailed constraints + Can encode and capture the physics behind the process - Do not scale up

What s New: Solving it Properly Requires!

73

! a robust, physically meaningful, scalable, automated solution method that combines:

Similarity Kernels & Clustering

Machine Learning for a global data-driven view

Underlying Physics

A language for Constraints enforcing local details

Constraint Programming model

Significantly outperforming previous methods.

74

I Computational Sustainability II Examples of Computational Sustainability problems

highlighting research themes III Institute for Computational Sustainability

IV Building a community in Computational Sustainability V Conclusions

Outline

Interdisciplinary Research Projects (IRPs): The Building Blocks of our Institute

!! A major part of the effort has been identifying a variety of collaborative projects that bring computer scientists together with domain experts in a number of fields

!! Individual projects organized under the umbrella of IRPs (Interdisciplinary Research Projects)

–! Currently 13 Application IRPs, 5 Technical IRPs –! Many more in incubation!

Application IRP: Bird Conservation

Application IRP: Biofuels

Technical IRP: Dynamical Systems

Technical IRP: Optimization

Species distribution modeling

Bird migration

RCW preservation

Reducing sampling bias

Equilibrium models

Impact of gasoline taxes

Carbon offsets in cap-and-trade

. . . . Problem structure

Stochastic optimization

Multi-agent inference

Balanced designs

Parameter estimation

Synchronization

Monitoring of sustainability

Critical transitions

Examples:

IRP Name Researchers Collaborators Students

1. Reserve Selection and Wildlife Corridors

Conrad, Gomes, Sabharwal, Selman, Shmoys

Dilkina, Dimiduk, Drusvyatskiy

2.

Bird Conservation •!Species distribution •!Bird migration •!RCW preservation

Lab of O: Rosenberg, Webb TCF: Allen, Amundsen, Vaughan Cornell: Conrad, Damoulas, Gomes, Sabharwal, Shmoys OSU: Dietterich, Hutchinson, Sheldon, Wong

Lab of O: Farnsworth, Fink, Hames, Hochachka, Kelling, Wood Cornell: Hooker, Joachims, Riedewald

Ahmadizadeh, Castorena, Dilkina, Elmachtoub, Finseth

3. Fishery Management and Fish Preservation

Conrad, Gomes, Sabharwal, Selman, Yakubu, Zeeman

Jerald, Ziyadi Alam, Ermon, Haycraft, Li, Wiley

4. Disease Prevention in Dairy Cows

Damoulas, Ellner, Gomes, Sabharwal, Selman

Grohn, Cha Ahmadizadeh, LeBras, Prakash, Smith, Teose

5. Forest Fires Borradaile, Dietterich, Montgomery, Rusmevichientong, Shmoys, Wong

Ferst, Spencer, Gagnon, Houtman, Mark, Reynolds

Application IRPs

continued !


6. Modeling Ecological Dynamics Ellner

Jones, Mao-Jones, Ritchie, King, Rohani, Ionides, Kendall, Reumann, Newman, Ferrari, Hooker

7. Biofuels •!Equilibrium models •!Cap-and-trade

Bento, Walker Anderson

Bergstrom, Cuvilliez, Kang, Klotz, Landry, Latza, Mayton, Patel, Roth, Stanislaus, Taranto, Weiz

8. Pastoral Systems in East Africa

Barrett, Gomes, Selman, Wong Kariuki, Leeuw, Smith Guo, Toth, Zhuo

9. Poverty Mitigation and Food Insecurity

Barrett, Damoulas, Gomes, Lentz, Naschold

Allen, Bell, Lang, Maxwell, McGlinchy, Murray

Dilkina, Guo, Harou, Lang

10. Invasive Species Conrad, Shmoys Dietterich,Gomes Hutchinson, Sheldon

Yang, Ermon, Spencer

11. Impact of Climate Change

Guckenheimer, Mahowald, Zeeman

Many colleagues in climate science

12. Stress/Environment Interactions Zeeman McCobb Gribizis

13. Material Discovery Damoulas, Gomes, Sabharwal Gregoire, van Dover LeBras

Application IRPs (continued )

78


14.

Dynamical Systems •!Parameter estimation •!Synchronization •!Monitoring of sustainability

Guckenheimer, Strogatz, Zeeman Haiduc, Kuehn

15.

Network Science •!High dimensional data •!Sensor networks •!Dynamics & Sync

Gomes, Hopcroft, Selman, Strogatz Ermon, Sheldon

16. Bridging Constraint Reasoning and Machine Learning

Damoulas, Dietterich, Gomes, Sabharwal

Gregoire, van Dover LeBras

17.

Combinatorial Reasoning and Stochastic Optimization •!Problem structure •!Stochastic optimization •!Multi-agent inference •!Balanced designs

Gomes, Sabharwal, Selman, Shmoys, Sheldon

Malitski, Sellmann

Cheung, Dilkina, Ermon, Guo, Kroc, LeBras, Perrault, Ramanujan

18.

Large Scale Computing Paradigms for Massively Large Data Sets and Simulation-Intensive Studies •!Cloud computing

Gomes, Sabharwal, Selman

Ahmadizadeh, Dilkina, Kroc

Technical IRPs

79

Constraint Satisfaction and Optimization

Data and Uncertainty

Distributed Highly

Interconnected Components / Agents

Complexity levels in Computational Sustainability Problems

Dynamics

Study computational problems as natural phenomena " Science of Computation

Many highly interconnected components; "" From Centralized to Distributed: multi agent systems

Complex dynamics and Multiple scales "" From Statics to Dynamics: Complex systems and dynamical systems

Large-scale data and uncertainty "" Machine Learning, Statistical Modeling

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

Complex decision and optimization problems " Constraint Reasoning and Optimization

80

Transformative Computer Science Research: Driven by Deep Research Challenges posed by Sustainability

Data & Machine

Learning DietterichPI

WongCo-PI

ChavarriaH

Environmental & Socioeconomic

Needs Dynamical

Models Guckenheimer

Strogatz

ZeemanPI,W

YakubuAA

Constraint Reasoning

& Optimization Gomes PI,W

, Hopcroft Co-PI

SelmanCo-PI, ShmoysCo-PI

Resource Economics, Environmental

Sciences & Engineering AlbersCo-PI,W, Amundsen, Barrett, Bento,

ConradCo-PI, DiSalvo, MahowaldW, MontgomeryCo-PI,W

Rosenberg,SofiaW, WalkerAA

Transformative Synthesis:

Dynamics & Learning

Science of Computation

Science of Computation

Study computational problems as natural phenomena " Science of Computation

Many highly interconnected components; "" From Centralized to Distributed: multi agent systems

Complex dynamics and Multiple time scales "" From Statics to Dynamics: Complex systems and dynamical systems

Large-scale data and uncertainty "" Machine Learning, Statistical Modeling

Complex decision and optimization problems " Constraint Reasoning and Optimization

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

Institute Activities

ICS

Research Coordinating

transformative synthesis

collaborations

Interdisciplinary Research

Projects (IRPs)

Web Portal Seminars

Building Research Community

Host visiting Scientists

Conference & Workshops

External Collaborations

Summer REU program targeting minority students

Computational Sustainability courses

Research seminar series

Postdocs Doctoral students Honors

projects

Education

Cornell Cooperative

Extension

Citizen Science Lab. Of Ornithology

Outreach

Conservation Fund

Rocky Mountain Research Station

The Nature Conservancy

K-12 Activities

Travel Museum

Computational Sustainability Community Conferences, Workshops,

Discussion Groups

CompSust 09: Cornell, 2009 Over 225 international researchers from several disciplines and institutions (universities, labs, government)

International Workshop on Constraint Reasoning and Optimization for Computational Sustainability •! CROCS-09 at CP-09 •! CROCS at CPAIOR-10 •! CROCS at CP-10 NIPS-09 Mini Symposium:

Machine Learning for Sustainability

Blogs:

Over 150 participants and lots of topics!!

CompSust 10: MIT, 2010

AAAI 2011 Special Track on Computational Sustainability

February 3, 2011: abstracts due February 8, 2011: papers due

Conclusions


Interdisciplinary field that aims to apply techniques from computing and information science, and related fields (e.g., engineering, operations research, applied mathematics, and statistics ) for Sustainable Development.

84

Sustainable Development encompasses balancing environmental, economic, and societal needs.

85

Sustainability

Climate

Natural Resources

Social Factors

Human Factors

Education

Economic factors

Energy

Public Policy

Computational Sustainability attempt at a definition

86

Wide interdisciplinary field , encompassing disciplines as diverse as economics, sociology, environmental sciences and engineering , biology, crop and soil science, meteorology and atmospheric science.

Key challenge: to effectively and efficiently establish interdisciplinary collaborations – the level of interconnectedness of social, economic, and environmental issues

makes it really challenging!

Focus: Develop computational models, methods and tools for a broad range of sustainability related applications and task: from supporting decision making and policy analysis concerning the management and allocation of natural resources to the development of new sustainable techniques, products, practices, attitudes, and behaviors.


87

Computer science and related fields

Sustainability fields

New challenging applications for Computer Science

Computational Thinking that will provide new insights into sustainability problems:

Analogy with Computational Biology

New methodologies In Computer Science New methodologies

In Sustainability fields

Summary

Computational Sustainability has great potential to advance the state of the art of computer science and related

disciplines with unique societal benefits!

88

Thank You!

89 From the Cloud Institute, NY

Why Computational Sustainability?

Thank you!


Statistics

eScience

Networks &

Systems

HCI

Game Theory

Operations Research

Systems Eng.

Appl. Mathematics

AI

Alg. Theory

CompSust

Climate

Natural Resources

Social Factors

Human Factors

Education

Economic factors

Energy

Public Policy

90

Challenges Managing scientific data: coping with a multidisciplinary world

Table of Product Characteristics id Property name Value

MilkProd productsrep MilkA MilkProd quantity 10000 MilkProd validity date 10/06/2006

CheeseProd productsrep

Minas CheeseProd quantity 2000 CheeseProd validity date 12/02/2006 CheeseProd shape Circular

Slide by Claudia Bauzer Medeiros, 2010

Deluge of heterogeneous data from distributed noisy sensors: Need for Technical skills Socio-emotional skills Transcultural skills

How to get people to care? Personalization

!! Identify places consumers have an

impact and are impacted by resource usage

–! Use social science to understand consumer attitudes

–! Make sustainability economically relevant

!! Identify reasons for industry to care, e.g., –! Perception as Environmental Leader –! Energy reductions in operations and products –! New markets, products, services

•! Address tussle between Security & Privacy of data

–! Facilitate ubiquitous monitoring-inferencing-actuation

–! Simplify device and data management for mere mortals

93

Goal: Trusted Personal Energy Management at the edge of the Smart Grid

Source : EPRI

Source : ASU

Slide by Eve M. Schooler

Existing and proposed wind farms in US and Mexico (2008)

•! 26,000+ turbines, 1.5% of potential Build-out to reach potential would

require >1.7 million turbines •! Areas with most favorable winds are also often associated with migratory pathways

Andrew Farnsworth, Ken Rosenberg, 2009

: Wind Energy and Bird Conservation

IWhere to not locate wind farms? Guidelines for locating wind farms

Ken Rosenberg (ICS) part of

the science team for the State of the Birds Report

computational sustainability - computing research ... · institute for computational sustainability...

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