computer science: the ever-expanding spherelazowska.cs.washington.edu/nu.pdf · machine learning...

ComputerScience:TheEver-ExpandingSphere

EdLazowskaBill&MelindaGatesChairin

ComputerScience&EngineeringFoundingDirector,eScienceInstitute

UniversityofWashington

Dean’sSeminarSeries,McCormickSchoolofEngineeringNorthwesternUniversity

April2016

http://lazowska.cs.washington.edu/NU.pdf,pptx

Today

• AreminderoftheextraordinaryprogressthatComputerSciencehasachieved

• Aglimpseatwhat’shappeningtoday• A21st centuryviewofthefield• TheroleofComputerScienceinthemodernuniversity

– Onequickexample:TheUniversityofWashington eScienceInstitute• TheroleofComputerScienceinthemodernworld• Studentresponse(enrollmenttrends)• Institutionalresponse

1969– Fortysevenyearsago…

Credit: PeterLee,MicrosoftResearch

Credit: PeterLee,MicrosoftResearch

Withnearly5decadesofhindsight,whichhadthegreatestimpact?

• Unlessyou’rebigintoTang* andVelcro* (orsexanddrugs),theanswerisclear…

• Andsoisthereason…

EXPONENTIALS US*Commonly– althougherroneously – attributed tothespaceprogram

• Constantcapabilityatexponentiallydecreasingcost• Exponentiallyincreasingcapabilityatconstantcost

Theexponentialimprovementsthathavecharacterizedcomputingcanbeexploitedintwoways

StoragePrice/MB,USD(semi-logplot)

MicroprocessorPerformance,MIPS(semi-logplot)

JohnMcCallum /Havard Blok

Disk

RAM

Flash

RayKurzweil

1955196019651970197519801985199019952000200520102015 1970197519801985199019952000 2005

Today,theseexponentialimprovementsintechnology(andalsoinalgorithms!)areenablinga“bigdata”revolution

• Aproliferationofsensors– Thinkaboutthesensorsonyourphone

• Moregenerally,thecreationofalmostallinformationindigitalform– Itdoesn’t needtobetranscribedinordertobeprocessed

• Dramaticcostreductionsinstorage– Youcanaffordtokeepallthedata

• Dramaticincreasesinnetworkbandwidth– Youcanmovethedatatowhereit’sneeded

• Dramaticcostreductionsandscalabilityimprovementsincomputation– WithAmazonWebServices,1000computersfor1daycoststhesameas1computerfor

1000days

• Dramaticalgorithmicbreakthroughs– Machinelearning,datamining– fundamentaladvancesincomputerscienceand

statistics

• Evermorepowerfulmodelsproducingever-increasingvolumesofdatathatmustbeanalyzed

So,exactlywhat’smeantby“bigdata”?

Credit: DanAriely,DukeUniversity

Seriousanswer:“bigdata”isenablingcomputerscientiststoputthe“smarts” intoeverything

• Smarthomes• Smartcars• Smarthealth• Smartrobots• Smartcrowdsandhuman-computersystems• Smarteducation• Smartinteraction(virtualandaugmentedreality)• Smartcities• Smartdiscovery

Business + Technology in the Exponential Economy

Smarthomes(theleafnodesofthesmartgrid)

ShwetakPatel,UniversityofWashington2011MacArthurFellow

Smartcars

DARPAGrandChallenge DARPAUrbanChallenge GoogleSelf-DrivingCar

Adaptivecruisecontrol Self-parkingTeslaModelS

P4medicine

Smarthealth

Evidence-basedmedicineLarrySmarr – “quantifiedself ”

Smartrobots

Smartcrowdsandhuman-computersystems

ZoranPopovicUWComputerScience&Engineering

DavidBakerUWBiochemistry

Smarteducation

ZoranPopovicUWComputerScience&Engineering

Smartinteraction

Smartinteraction– contentcreation

SteveSeitzUWComputerScience&Engineering+GoogleSeattle

Smartcities

Smartdiscovery:“TheFourthParadigm”

1. Empirical+experimental2. Theoretical3. Computational4. Data-Intensive

JimGray

Eachaugments,vs.supplants, itspredecessors– “anotherarrowinthequiver”

Energy&Sustainability

Security,Privacy,&Safety

Advancing theDevelopingWorld

Medicine&GlobalHealth

Education

ScientificDiscovery

Transportation

NeuralEngineering

ElderCareAccessibility

Interactingwith thePhysicalWorld:“TheInternet ofThings”

mobilecomputing

robotics

computervision

machinelearning

humancomputerinteraction

datascience

sensors

naturallanguageprocessing CORECSE

AI,systems,theory,languages,

etc.

cloudcomputing

TechnologyPolicyandSocietalImplications

A21st centuryviewofComputerScience:Afielduniqueinitssocietalimpact





Education

ScientificDiscovery

Transportation

NeuralEngineering




Isthisstuffcomputerscience?

“Thelastelectricalengineer”

“Iamworriedaboutthefutureofourprofession.…Iseetheworldasaninvertedpyramid.Itbalancesprecariouslyonthenarrowpointatthebottom.…Thispoint isbeing impressedintotheground bytheheavyweightatthewidetopoftheinvertedpyramidwherealltheapplicationsreside.…Electricalengineering willbeindangerof shrinking intoaneutronstarofinfiniteweightandimportance,butinvisibletotheknownuniverse.…SomewhereinthebasementofInteloritssuccessor…thelastelectricalengineerwillsit.”

BobLuckyIEEESpectrumMay1998

Credit: AlfredSpector, Google(ret.)

“ComputerScience:Theever-expandingsphere”





Education

ScientificDiscovery

Transportation

NeuralEngineering



mobilecomputing

robotics

computervision

machinelearning

humancomputerinteraction

datascience

sensors

naturallanguageprocessing CORECSE

AI,systems,theory,languages,

etc.

cloudcomputing


“ComputerScience:Theever-expandingsphere”

AtUW,we’vebeeninvestinginallareas:• We’veaugmented

thecore• Wehaveaserious

storyinallareasofthe“connections”ringandnearlyallofthesocietalchallengeareas

TheroleofComputerScienceinthemodernuniversity

• TheCenterforSensorimotorNeuralEngineering, anNSFEngineering ResearchCenter

• TheCenterforGameScience,fundedbytheGatesFoundationandDARPAtocreaterevolutionarygamesforscientificdiscoveryandforlearning

• TheeScienceInstitute,funded bytheMoore,Sloan,WashingtonResearch,andNationalScienceFoundations tobringadvancesindata-intensivediscoverytoresearcherscampus-wide

• dub – “design-use-build”– acampus-widecollaborationthathasmadeUWoneof thetopinstitutions inthenationinhuman-computer interaction

• Urban@UW,acampus-wideurbansciencecollaboration

• TheTaskarCenterforAccessibleTechnologydevelopsanddeploys technologies thatincreaseindependence andimprovequalityoflifeforindividualswithmotorandspeechimpairments

• Change,acampus-widecollaborationexploringhowtechnologycanimprovethelivesofunderserved populations inlow-incomeregions

• TheTechPolicyLab,ajointeffortofCSE,theSchoolof Law,andtheInformationSchool, fundedbyMicrosoft

• GIX – theGlobalInnovationExchange– anewkindofeducationthatisglobal,project-based,andintegratestechnology, design,andentrepreneurship

• TheIntelScienceandTechnologyCenterforPervasiveComputing,ledbyUW,withresearchersfromCornell,GeorgiaTech,Rochester,Stanford,andUCLA

“Allacrossourcampus,theprocessofdiscoverywillincreasinglyrelyonresearchers’abilitytoextractknowledgefromvastamountsofdata...Inordertoremainattheforefront,UWmustbealeaderinadvancingthesetechniquesandtechnologies,andinmaking[them]accessibletoresearchersinthebroadestimaginablerangeoffields.”[2007conceptpaper]

Onequickexample:TheUniversityofWashingtoneScienceInstitute

• UniversityofWashington– $725,000/yearforstaffsupport– $600,000/yearforfacultysupport

• NationalScienceFoundation– $2.8millionover5yearsforgraduateprogramdevelopmentand

Ph.D.studentfunding(IGERT)

• GordonandBettyMooreFoundationandAlfredP.SloanFoundation

– $37.8millionover5yearstoUW,Berkeley,NYU

• WashingtonResearchFoundation– $9.3millionover5yearsforfacultyrecruitingpackages,postdocs

• Also$7.1milliontotheclosely-alignedInstituteforNeuroengineering

Majorsourcesofsupportforour“coreeffort”

• TheFoundationshaveafocusonnoveladvancesinthephysical,life,environmental,andsocialsciences

• Theyrecognizedtheemergenceofdata-intensivediscoveryasanimportantnewapproachthatwouldleadtonewadvances

• Theyperceivedanumberofimpedimentstosuccess• Theysoughtpartnerswhowerepreparedtoworktogetherina

distributedcollaborative experiment focusedontacklingtheseimpediments

GenesisoftheMoore/Sloan“DataScienceEnvironments”effort

Vision

EdLazowskaCSE

Datasciencemethodology

Lifesciences Environmentalsciences

Socialsciences Physicalsciences

CeciliaAragonHumanCenteredDesign&Engr.

MagdaBalazinskaComputerScience&Engineering

CarlosGuestrinCSE

BillHoweCSE

RandyLeVequeAppliedMathematics

WernerStuetzleStatistics

TomDanielBiology

GingerArmbrustOceanography

AndyConnollyAstronomy

JohnVidaleEarth&SpaceSciences

JoshBlumenstockiSchool

MarkEllisGeography

TylerMcCormickSociology,Statistics,CSSS

ThomasRichardsonStatistics,CSSS

EmilyFoxStatistics

JeffHeerCSE

BillNobleGenomeSciences

DavidBeckChemicalEngr.

Ouroriginalcorefacultyteam(muchexpandednow)

• Educationalinitiatives attheBachelors,Masters,andDoctorallevels,plusprofessionaleducationcertificateprogramsandCourseraMOOCs

• Hiresof“pi-shaped”faculty(facilitatedbytheProvostandtheWashingtonResearchFoundation)

Amongouractivities

• Avibrantprogramofdual-mentoredpostdocsandgraduatestudents

• ApermanentstaffofsuperbPh.D.-levelDataScientistsspanningdisciplines

• Acollaboratory:theWRFDataScienceStudio• Myriadtrainingandmentoringactivities:

short-courses,workshops,officehours• Deeppartnerships,plusan“incubation

program”fordata-intensiveresearchprojects

Microbialcommunity visualizedwithDNAstain

100μm

Challenges:• Integrationacrossdifferentdatatypes• Distributedandremotelabs

Oneexampleofadeeppartnership

Roleofmicrobesinmarineecosystems• GingerArmbrust(Oceanography), BillHowe(CSE+eScienceInstitute)

Credit: GingerArmbrust, UniversityofWashington

Queryacrossdatasets inreal-time:“notjustfaster…different!”

DanHalperin,ResearchScientist,eScienceInstitute

KonstantinWeitzGraduatestudent,CSE


Integrating acrossphysics,biology,andchemistry

SatellitelinkSeaFlow instrument Labcomputer

Ship computer

Processeddata

LabcomputerCloud – SQLShare

Webdisplay –collaboratorcomputers

Othershipdatastreams

automated

manual

Completelyautomated


Connecting acrossdistributed labs

• Thereareover4,000homeless familiesintheTri-countyareaeveryyear

• Familiesspendonaverage8monthsmovingfromsheltertoshelter

• GoaloftheBill&MelindaGatesFoundation andBuildingChanges:Cutfamilyhomelessness inhalfby2020andreducethetimeafamilyspends homeless to1month

InSnohomish, Pierce,andKingcounties

PredictorsofPermanentHousing forHomelessFamilies• Projectleads:NeilRocheandAnjanaSundaram, TheBillandMelinda

GatesFoundation• DSSGFellows: Fablina Ibnat,JasonPortenoy, Chris Suberlak, JoanWang• ALVAstudents:CameronHolt,Xilalit Sanchez• DataScientistMentors:ArielRokem,Bryna Hazelton

Oneofdozensofincubationprojects

Credit: Fablina Ibnat,UniversityofWashington

Homelessfamiliesmaytakemanypathwaysthroughprograms

Emergency shelter

Transitional housing

Rapid re-housing

Permanent housing

Housing with services Unsuccessful

exitCredit: Fablina Ibnat,UniversityofWashington

KingHMISextract

PierceHMISextract

SnohomishHMISextract

Cleaneddataforhouseholds

Cleaneddataforfamilies

Familyenrollments

Mappingofenrollmentstoepisodes

Familyepisodes

Createdatacleaningpipeline formessyHomelessManagement InformationSystemsdata


Developvisualizationstoshowhowhomelessfamiliesmovethrough programs


Conduct analysistoidentifypredictorsofpermanenthousing

Correlation with successful outcome, by family characteristics

Correlation with successful outcome, by homelessness program

Emergency Shelter use tends to be associated with unsuccessful outcomes (unsurprising!)

Homelessness Prevention programs more strongly associated with positive outcomes than transitional housing

Substance abuse strongly associated with unsuccessful outcomes

Parent employment strongest predictor of successful outcomes


TheroleofComputerScienceinthemodernworld

1. Every21st centurycitizenneedstohavefacilitywith“computationalthinking”– problemanalysisanddecomposition(stepwiserefinement),abstraction,algorithmicthinking,algorithmicexpression,stepwisefaultisolation(debugging),modeling– Computational thinking isnot“thisparticular

operatingsystem”or“thatparticularprogramminglanguage”

– Computational thinking isnotevenprogramming.It’samodeofthought– awayofapproaching theworld

– Programming isthehands-on, inquiry-basedwaythatweteachcomputationalthinking andtheprinciplesofcomputer science

2. FieldsfromAnthropologytoZoologyarebecominginformation fields,andthatthosewhocanbendthepowerofthecomputertotheirwill–computationalthinking,butalsocomputerscienceingreaterdepth– willbepositionedforgreatersuccessthanthosewhocan’t– Datascienceisaperfectexample

3. PrettymuchalloftheSTEMjobsareincomputerscience– Inthecomputing industry, whichisnotDilbert– it’san

intellectuallyexciting,highlycreativeandinteractive,“powertochangetheworld”field

– Inallsortsofother fieldswherepeopleeducatedascomputer scientists– notmerelypeoplewithsomecomputer sciencebackground– areessential

73%

10%

3%3%

5%

6%

JobGrowth,2014-24- U.S.BureauofLaborStatistics

Computer occupations (15-1100)

Engineers(17-2000)

Lifescientists (19-1000)

Physical scientists (19-2000)

Socialscientists andrelatedworkers(19-3000)

Mathematical science occupations (15-2000)

STEMjobgrowth,2014-24(USBureauofLaborStatistics)

Data from the spreadsheet at http://www.bls.gov/emp/ind-occ-matrix/occupation.xlsx

55%

26%

6%

4%

5%4%

JobOpenings(Growth+Replacement),2014-24- U.S.BureauofLaborStatistics

Computer occupations (15-1100)

Engineers(17-2000)

Lifescientists (19-1000)

Physical scientists (19-2000)

Socialscientists andrelatedworkers(19-3000)

Mathematical science occupations (15-2000)

Datafromthespreadsheetathttp://www.bls.gov/emp/ind-occ-matrix/occupation.xlsx

STEMjobopenings(growth+replacement),2014-24(USBLS)

Data from the spreadsheet at http://www.bls.gov/emp/ind-occ-matrix/occupation.xlsx

16%ofallnewwages,acrossall fields

Credit: Code.org

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000

CurrentAnnualCompletions

AdditionalAnnualCompletionsNeeded,2016-21

ComputerScience

Engineering

HealthProfessions*

Research,Science,

Technical*

*Gapexistsatthegraduateand/orprofessionallevelonly

HighDemandFieldsinWashingtonState,BaccalaureateLevel&AboveWSAC/SBCTC/WTECB,October2013

Data from Table 2 at http://www.wsac.wa.gov/sites/default/files/2013.11.16.Skills.Report.pdf

FieldswithworkforcegapsinWashingtonState(Baccalaureatelevelandabove)

KingCountyWA’sAerospaceWorkforceQ: What field has the largest total

number of current employees in King County’s aerospace industry?

Q: What field has the greatest predicted number of new employees needed by King County’s aerospace industry from 2013-2023?

Q: What field has the greatest predicted compound annual growth rate for King County’s aerospace industry from 2013-2023?

Q: What field has the greatest predicted annual gap between supply and demand for King County’s aerospace industry from 2013-2023 (where “supply” is not “degrees granted” but rather the industry’s current ability to hire)?

A: Computer Science

Studentsarerespondingtoallthreeimperatives

1. Demandforintroductorycoursesisbooming

2. Demandforupper-divisionandgraduatecoursesbynon-majorsisbooming

3. Demandforthemajorisbooming

0

500

1000

1500

2000

2500

3000

UniversityofWashingtonCSEIntroductoryCourseAnnualEnrollment

(1-yearmovingtotal)

CSE143

CSE142

UWCSEintroductorycourseenrollment(1-yearrollingaverage)

0

100

200

300

400

500

600

700

800

900

1000

Top10First-ChoiceMajorsofUWConfirmedIncomingFreshmen

BusinessAdministration

ComputerScience&Engineering

Biology

MechanicalEngineering

Bioengineering

Psychology

Biochemistry

Aeronautics&Astronautics

Mathematics

Chemistry

Top10first-choicemajorsofUWconfirmedincomingfreshmen

2007-08EconomicsGovernmentSocialStudiesPsychologyEnglish&AmericanLiterature &LanguageHistoryAnthropologyHistory&LiteratureBiochemical SciencesAppliedMathematicsMolecular&Cellular BiologyHumanEvolutionaryBiologyNeurobiologyBiologyMathematicsSociologyChemistryPhysicsVisual&Environmental StudiesHistory&ScienceComputer ScienceEngineering&AppliedScience(AB)Chemical&PhysicalBiologyEnvironmental Science&PublicPolicyFineArts/History ofArt&Architecture

Top25concentrationsatHarvard2015-16EconomicsGovernmentComputer ScienceAppliedMathematicsPsychologySocialStudiesNeurobiologyStatisticsHumanDevelopmental&RegenerativeBiologyEnglishHistorySociologyHistory&LiteratureIntegrativeBiologyMolecular&Cellular BiologyMathematicsPhysicsChemistryHumanEvolutionaryBiologyHistory&ScienceEngineering&AppliedScience(SB)Biomedical EngineeringAnthropologyPhilosophyVisual&Environmental Studies Credit: HarryLewis

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

Life

Physical

Computational/Mathematical

DistributionofsciencemajorsatHarvard

Credit: HarryLewis

0

50

100

150

200

250

300

350

400

450

500

L&S:Undeclared

L&S:Math&PhysSciDiv

L&S:SocialSciencesDiv

L&S:UndergraduateDiv

L&S:Arts&HumanitiesDiv

L&S:BioSciencesDiv

L&S:Admin-CS

Berkeleyupper-divisionCSenrollmentfromL&SoutsideofCS

K-12too:CSAPparticipation,whilestillpathetic,isnowgrowing

Code.orgwillcauseallofthistoaccelerate

Hadi PartoviCode.org

Institutionalresponse:K-12

1983

IBMPCXT4.77MHz8088128KBRAMPCDOS2.0

401pagereport15pageindex

Scrollforward30years,to2013

WhichbegattheNextGenerationScienceStandards…

• In3outof4 highschoolsnationwide,computersciencethatincludesprogrammingisnotoffered– Butthat’sfarbetterthanjustayear

ortwoago!

• In22ofthe50states,computersciencedoesnotcounttowardsthemathorsciencegraduationrequirement– Butthat’sfarbetterthanjustayear

ortwoago!

Pathetic… butdespiteall,progressisbeingmade

Credit: Code.org

• Manypositivesigns– “SchoolsofComputerScience”areproliferating– Whatevertheprefix,thekeything isthatComputerSciencebeviewedasa

unitoftheentireuniversity• Computerscienceprogramsneedtoact thisway

• However

Institutionalresponse:highereducation

0"

10,000"

20,000"

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50,000"

60,000"

70,000"

1966

"19

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"19

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"20

11"

2012

"

Computer)Science)Bachelors)Degrees)Granted)

– Sometendencytoviewcurrentsituationasatransient

• E.g.,hirelecturers;usefaculty fromotherfields– Facilitiesareahugeproblem

• Mustaccommodatescale• Mustrespondtoevolvingnatureofthefield

0

50,000

100,000

150,000

200,000

250,000

300,000

ComputerScience Engineering LifeSciences(incl.agricultural)

SocialSciences(incl.psychology)

PhysicalSciences(incl.environmental)

MathematicalSciences

Annualjobsavailablevs.degreesgranted

Annual jobsavailable Annual Bachelorsdegrees AnnualMastersdegrees Annual Doctoral degrees

BLSjobprojection data:http://www.bls.gov/emp/ind-occ-matrix/occupation.xlsxS&E Indicatorsdegreedata:http://www.nsf.gov/statistics/2016/nsb20161/uploads/1/12/at02-01.xlsx

BLS job projection data: http://www.bls.gov/emp/ind-occ-matrix/occupation.xlsxS&E Indicators degree data: http://www.nsf.gov/statistics/2016/nsb20161/uploads/1/12/at02-01.xlsx

0

50,000

100,000

150,000

200,000

250,000

300,000

ComputerScience Engineering LifeSciences(incl.agricultural)

SocialSciences(incl.psychology)

PhysicalSciences(incl.environmental)

MathematicalSciences

Annualjobsavailablevs.degreesgranted

Annual jobsavailable Annual Bachelorsdegrees AnnualMastersdegrees Annual Doctoral degrees

BLSjobprojection data:http://www.bls.gov/emp/ind-occ-matrix/occupation.xlsxS&E Indicatorsdegreedata:http://www.nsf.gov/statistics/2016/nsb20161/uploads/1/12/at02-01.xlsx

BLS job projection data: http://www.bls.gov/emp/ind-occ-matrix/occupation.xlsxS&E Indicators degree data: http://www.nsf.gov/statistics/2016/nsb20161/uploads/1/12/at02-01.xlsx

Roomforgrowth:AnnualSTEMjobopenings(BLS) vs.degreesgranted(NSF)

Isthisagreattimeorwhat?

http://lazowska.cs.washington.edu/NU.pdf,pptx

computer science: the ever-expanding spherelazowska.cs.washington.edu/nu.pdf · machine learning...

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