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  • Image credit: http://www.lsst.org/Science/lsst_science.shtml Space-time Warp
The detailed mass distribution in the cluster CL0024 is shown, with gravitationally distorted graph paper overlaid. This detailed dark matter distribution can be used to constrain theories of dark matter. Strong lensing of a background galaxy was inverted to yield a model for the mass distribution. This model was used to calculate the appearance of orthogonal graph paper placed behind the gravitational lens.
  • FIRST SUCCESSFUL DEMONSTRATION OF MULTISCALE MD-LBE COUPLING (fluid flow is crucial – not shown in simulation)
  • FIRST SUCCESSFUL DEMONSTRATION OF MULTISCALE MD-LBE COUPLING (fluid flow is crucial – not shown in simulation)
  • Image credit: http://www.lsst.org/Science/lsst_science.shtml Space-time Warp
The detailed mass distribution in the cluster CL0024 is shown, with gravitationally distorted graph paper overlaid. This detailed dark matter distribution can be used to constrain theories of dark matter. Strong lensing of a background galaxy was inverted to yield a model for the mass distribution. This model was used to calculate the appearance of orthogonal graph paper placed behind the gravitational lens.

PPT PPT Presentation Transcript

  • The Initiative in Innovative Computing at Harvard Alyssa A. Goodman IIC Director & Prof. of Astronomy
  • Agenda
    • What is IIC? (“Filling the Gap”)
    • Where did it come from? (A Story)
    • What have we done so far? (Startup Mode)
    • What are we about to do? (Projects, Hiring Plans)
    • What do we hope to do? (Long-term Goals)
  • Filling the “Gap” between Science and Computer Science Increasingly, core problems in science require computational solution Typically hire/“home grow” computationalists, but often lack the expertise or funding to go beyond the immediate pressing need Focused on finding elegant solutions to basic computer science challenges Often see specific, “applied” problems as outside their interests Scientific disciplines Computer Science departments
  • Where did IIC come from?
    • Short Version: Response to Harvard’s “expansion” in Science, and into Allston. See IIC Whitepaper (2004) & Task Force on Science & Technology report (2005) for more.
    • Long Version…
  • Computational challenges are common across scientific disciplines
    • How to:
      • Acquire, transmit, organize , and query new kinds of data?
      • Apply distributed computing resources to solve complex problems?
      • Derive meaningful insight from large datasets ?
      • Share, integrate and analyze knowledge across geographically dispersed researchers ?
      • Visually represent scientific results so as to maximize understanding?
    Opportunity to collaborate and apply insights from one field to another
  • Workflow and WORKFLOW Write a paper, the quantitative results of which are shared globally, digitally. RESPOND Write a paper for a Journal. “ Respond” Work with 20 people in 5 countries, in real-time COLLABORATE Work with your student “ Collaborate” Study the toxic effects of chlorine runoff in the U.S . Study the density structure of all star-forming gas in… ANALYZE Find a link between one factory’s chlorine runoff & disease Study the density structure of a star-forming glob of gas “ Analyze” CDC Wonder “ National Virtual Observatory”/ COMPLETE COLLECT Microscope, Stethoscope, Survey Telescope “ Collect” Public Health Astronomy Examples
  • Real World Workflow e.g. Emergency Medicine in the Age of High-Speed Networks, Fast Processors, Mass Storage, and Miniature Devices IIC/Harvard contact: Matt Welsh, DEAS
  • Continuum “ Pure” Discipline Science (e.g. Galileo) “ Pure” Computer Science (e.g. Turing) “ Computational Science” Missing at Most Universities
  • Filling the “computational science” gap: IIC
    • Problem-driven approach
      • … focusing effort on solving problems that will have greatest impact & educational value
    • Collaborative projects
      • … combining disciplinary knowledge with computer science expertise
    • Interdisciplinary effort
      • … to ensure that best practices are shared across fields and that new tools and methodologies will be broadly applicable
    • Links with industry
      • … to draw on and learn from experience in applied computation
    • Institutional funding
      • … to ensure effort is directed towards key needs and not driven solely by narrow priorities of funding agencies
  • Workflow For any particular scientific investigation: Where does, and could, “computational science” make improvements in this cycle?
  • Where are the optimal “IIC” problems? Low High Computer Science Payoff Domain Science Payoff Low HIgh “ Never Mind” Computer Science Department Science Departments CS Departments What is the right shape for that boundary?
  • IIC Research Branches ( and Projects Draw upon >1 ) V DC DB/P AS I Plus…Educational Programs that bring IIC Science to Harvard students, and to the public at large. Improved data acquisition. Novel hardware approaches (e.g. GPUs, sensors). Development of efficient algorithms. Cross-disciplinary comparative tools (e.g. statistical). Management, and rapid retrieval, of data. “ Research reproducibility” …where did the data come from? How? e-Science aspects of large collaborations. Sharing of data and computational resources and tools in real-time. Physically meaningful combination of diverse data types. Instrumentation Analysis & Simulations Databases/ Provenance Distributed Computing Visualization
  • Education is central to IIC’s mission
    • At Harvard:
      • Undergraduate & graduate courses focused on “data-intensive science”
      • New graduate certificate program , within existing Ph.D. programs
      • Research opportunities at undergraduate, graduate, and postdoctoral levels
    • Beyond Harvard:
      • New museum , highlighting the kind of science done at the IIC
    • Image & Meaning Collaboration
    • IIC Seminar Series at Harvard
    • Astronomical Medicine (IIC/CfA/HMS/MGH/BWH-SPL)
    • 1st Call for Ideas (deadline was 3/15/06)
    IIC’s First Activities (2005-) V I V DC DB/P AS V DC DB/P AS I V DC DB/P AS I
  • “ Image and Meaning”
    • “ I-M”=Working group of scientists, computer scientists, graphic artists, writers, publishers, designers organized and led by Felice Frankel , now at IIC!
    • Goal: To increase both scientists understanding of their own data, and the public’s understanding of scientists’ findings, through graphical display.
    • Activities:
      • Large conferences at MIT in 2001 and Getty Center in 2005.
      • Smaller “IM2.x” local workshops throughout 2006-7, including @ IIC.
      • Upcoming IM/SIGGRAPH , in conjunction with SIGGRAPH 2007.
      • Online community to be hosted by IIC, beginning later this year. (Social Network model.)
  • Seminar Sampler (Fall 2005-Spring 2006) Wednesday Afternoons, see iic.harvard.edu!! Cyberenvironments Jim Myers  yesterday! Future of e-Publishing Phil Campbell Profiles in Supercomputing Pete Eltgroth Provenance Luc Moreau Interactive Media Curtis Wong Games, Simulation & Learning Eric Klopfer Grid, Agile Methods, Array-based Databases, Bio & Neuro informatics, Clinical Applications in Autism Research, Astronomical Medicine… And more… Digital Visual Literacy Andy van Dam/Anne Spalter Virtual Observatory as a Model for Information Sharing Roy Williams Building a Grid-enabled Gateway for Science & Engineering Mark Green Science & the Semantic Web Jim Hendler CS & Visual Depiction (Frankel, Rheigans, Durand, Pfister) Panel on Emergence of Cyberinfrastructure Carl Kesselman UK e-Science Anne Trefethen Multi-Scale Modeling Tim Kaxiras Numerical Cosmology & 3D Viz Volker Springel/Nick Holliman Service-Oriented Science Ian Foster How to Build Google in Your Spare Time Jim Reese
  • Responses to 1st IIC Call for Ideas V DC DB/P AS V DC DB/P AS V DC DB/P AS DC DB/P I V DC DB/P AS I V DC DB/P DC V DC DB/P AS V DC DB/P AS I V DC DB/P AS I V DC V DC DB/P AS V I Time-Series Research Collaborative A Portal for the National Virtual Observatory LHC/LSST/MWA Consortium for Data-Intensive Science Connectional Analysis of Synaptic Circuitry in the Mammalian Nervous System (The “Connectome”) Framework for Multimodal Studies in Genetics, Biology & the Mind Knowledge Ecology of Science (Peer-to-Peer Collaboration Networks) Spatial Ontology Mapping (Community-based) Enhanced Viz/Analysis Tools for Archaeo/Geo/Seismology Medical Treatment Outcomes Online Gene Pattern + The Virtual Data Center Multiscale Hemodynamics Atomistic Modeling of Biomolecular Function
  • Building the Best (Startup) Program V DC DB/P AS I Instrumentation Analysis & Simulations Databases/ Provenance Distributed Computing Visualization Project 1
  • Building the Best (Startup) Program V DC DB/P AS I Project 1 Instrumentation Analysis & Simulations Databases/ Provenance Distributed Computing Visualization Project 2 Project 3
  • Now… V DC DB/P AS V DC DB/P AS V DC DB/P AS DC DB/P I V DC DB/P AS I V DC DB/P DC V DC DB/P AS V DC DB/P AS I V DC DB/P AS I V DC V DC DB/P AS V I planning grant planning grant looking into collab around 3D displays ongoing discussion w/Harvard librarians integrated into Data-Intensive Project Time-Series Research Collaborative A Portal for the National Virtual Observatory LHC/LSST/MWA Consortium for Data-Intensive Science Connectional Analysis of Synaptic Circuitry in the Mammalian Nervous System (The “Connectome”) Framework for Multimodal Studies in Genetics, Biology & the Mind Knowledge Ecology of Science (Peer-to-Peer Collaboration Networks) Spatial Ontology Mapping (Community-based) Enhanced Viz/Analysis Tools for Archaeo/Geo/Seismology Medical Treatment Outcomes Online Gene Pattern + The Virtual Data Center Multiscale Hemodynamics Atomistic Modeling of Biomolecular Function
  • 2006-7 Project Portfolio The Connectome +Astronomical Medicine Computational Framework for Neuroinformatics and Genetics Data-Intensive Science and High Capacity Scientific Databases Genepattern and the Virtual Data Center (VDC) National Virtual Observatory Portal +Envisioning Science Program
  • “ Astronomical Medicine”
    • Brigham & Women’s Hospital, Surgical Planning Lab
    • Massachusetts General Hospital, Martinos Center
    • Harvard-Smithsonian Center for Astrophysics
    • IIC
    • Present Team:
    • Alyssa Goodman (IIC & CfA, Co-I)
    • Michael Halle (IIC & BWH, Co-I)
    • Douglas Alan (IIC, Sen. Scientific S/W Engineer)
    • Michelle Borkin (IIC, Res. Assoc.)
    • Jens Kauffmann (IIC & CfA, postdoc)
    Demo Movie
  • The “Connectome”: Wiring Diagram for a Complete Brain Circuit (Connectional Analysis of Synaptic Circuitry in the Mammalian Nervous System)
    • 3D images from electron-microsope images of serial sections (slices)
      • Large volumes studies: up to 500 mm cubes
      • High resolution:  5nm x-y ; 50 nm in z ( 10 5 x 10 5 x 10 4 = 10 14 voxels )
      • Large datasets: 10-100 TB
    • Potentially intractable computationally w/o a hierarchical approach
      • Start with the large, dominant pathways: The biggest wires and the biggest excitatory connections.
      • Use this as scaffolding to then solve other pathways: inhibition, lateral connections, feedback.
    V DC DB/P AS I
  • Virtual Observatory Portal V DC
  • Virtual Observatory Portal?
  • Virtual Observatory Portal?
  • Virtual Observatory Portal Default values are shown in green Data on: One object One Region A list of objects A list of regions I want: Spectra Images Catalogs (click all that apply) I want to : Use VO tools to browse data Download data to local computer Would you like help writing a script to do your query? Yes or No Continue Virtual Observatory Portal V DC
  • A Computational Framework for Neuroinformatics and Genetics
    • Collaboration Amongst Several HMS Hospitals & Departments:
      • Neurology
      • Radiology
      • Psychology
      • Molecular Genetics
      • IIC
    Goal: Create an integrated framework for simultaneous analysis and reproducible retrieval of multimodal data in structural & functional brain imaging and genetics.
  • Cortical Thickness AD vs. Controls A Computational Framework for Multimodal Studies in GENETICS, BIOLOGY, AND THE MIND Histological Correlates of AD V DC DB/P AS I Lab 1 Lab 3 Lab 2 Lab 4 Lab 5 Core Imaging Methodologies Family history pedigree software toolkit Topology differences in cocaine addiction Computational Framework
  • A Computational Framework for Multimodal Studies in GENETICS, BIOLOGY, AND THE MIND V DC DB/P AS I “ An Entire Disease or Condition of the Brain” Computational Framework
  • Data-Intensive Science
    • Collaboration Amongst:
    • Physics Department
    • DEAS
    • Harvard-Smithsonian Center for Astrophysics
    • IIC & Harvard CIO’s
    • CERN & NSF
    Goal: Create powerful HPC / Grid capabilities in data-intensive science, advanced analytical algorithms in Astronomy & Physics, & advanced research in scientific VLDBMS
  • Data-Intensive Science
    • Of interest to ABCD:
      • Tier 2 Grid Node & Staff will come to Harvard
      • HPC Real-Time Computing capability for MWA project
      • Large, fast, storage for Pan-Starrs Project
      • Seeds of a “Center” for Time-Series Analysis
  • Gene Pattern - Virtual Data Center
    • Collaboration Amongst:
    • The Broad Institute of Harvard & MIT
    • Harvard Institute for Quantitative Social Science (IQSS)
    • IIC
    Goal: Integrate biomedical computational workflow engines with a statistical framework and canonical data repository originally developed for social science research
  • Multiscale Hemodynamics
    • Collaboration Amongst:
    • DEAS/Applied Mathematics
    • Physics
    • DEAS/Computer Science
    • HMS/Cardiology
    • DEAS/Chemical Engineering
    • IIC
    Goal: Build an accurate multiscale simulation of hemodynamics to enable significant advances in fundamental knowledge of blood flow and treatment of related diseases.
  • The ‘Envisioning Science’ Program
    • Collaboration Amongst:
    • Faculty of Arts and Sciences ( Felice Frankel !!)
    • Harvard Medical School
    • MIT, NSF, Apple…
    • IIC
    Mission: To enable Scientists, computer scientists, graphic designers, journalists, and editors to co-develop new methods of scientific communication and education focused on scientific images . Ongoing Image and Meaning Conference series Picturing to Learn program
  • Modeling Blood Flow (Multiscale Hemodynamics)
    • Develop parallelization, visualization tools,
    • to scale up to real applications
    • Ultimate goal is MULTISCALE HEMODYNAMICS
    • Movie: Multiscale approach for translocation of DNA through a nanopore
      • Molecular Dynamics for DNA
      • Lattice Boltzmann Equation for the solvent
    • S. Melchionna, S. Succi, M. Fyta, L. Stein ,E. Kaxiras , M. Seltzer
    V DC DB/P AS
  • Modeling Blood Flow (Multiscale Hemodynamics)
    • Develop parallelization, visualization tools,
    • to scale up to real applications
    • Ultimate goal is MULTISCALE HEMODYNAMICS
    V DC DB/P AS
  • Agenda
    • What is IIC? (“Filling the Gap”)
    • Where did it come from? (A Story)
    • What have we done so far? (Startup Mode)
    • What are we about to do? (Projects, Hiring Plans)
    • What do we hope to do? (Long-term Goals)
  • IIC will evolve over three phases Phase I 2005-08
    • Timing
    • IIC staffing level, combo of
      • new faculty
      • senior scientists
      • admin staff
    • Number of projects
    • Educational mission
      • New courses offered
      • Outreach programs
    • Other key milestones
    Phase II 2008-10 Phase III 2011+ Total ~25 to ~100 ~5-7 to ~15-18 New courses to museum Evaluation schedule (internal, external committees)
  • Organizational Challenges
    • Result of “Allston” Science & Technology Task Force
      • IIC intended to be a “University” (not a single school) initiative
    • FAS (Faculty of Arts & Science) Constraints
      • Faculty Appointments
      • Non-Faculty Appointments
    • Startup Space
    • “ Chicken-and-Egg” Problem with Recruiting
    • Good, but not certain, Funding Prospects
    • Role of DEAS Computer Science
  • Challenges for Discussion
    • How will IIC research enable better collaboration amongst geographically distributed researchers?
    • What are the best technologies for visualizing enormous data sets?
    • How can "human-in-the-loop" software, where we admit that humans are better than computers at many (particularly graphical) tasks, best be created and used
    • Are "mashups," where many software packages are "mashedup" together the way of the future, or is it reasonable to strive for "perfect" standalone software packages?
    • If mashups ultimately prevail, what is the business model for developing them?
    • How do we best appoint new IIC faculty at Harvard, given that their work often does not fit within existing departmental boundaries?
    • How can IIC best partner with industry to accomplish goals of mutual interest, and which of those goals are paramount?
  • “Challenges” Low High Computer Science Payoff Domain Science Payoff Low HIgh “ Never Mind” Computer Science Department Will CS/DEAS use slots for these people? How big is that overlap? Will departments hire “computationalists” with regular slots? How big is this overlap? How do we give Senior non-faculty similar stature to faculty? (e.g. P.I. rights, job security) Science Departments CS Departments
  • IIC: Mission
    • The Institute for Innovative Computing (IIC) will make Harvard a world leader in the innovative and creative use of computational resources to address forefront scientific problems.
    • We will focus on developing capabilities that are applicable to multiple disciplines , by undertaking specific, well-defined projects , thereby developing tools and approaches that can be generalized and shared .
    • We will foster the flow of ideas and inventions along the continuum from basic science to scientific computation to computational science to computer science .
    • We will train a next generation of creative and computationally capable scientists, build linkages to industry , and communicate with the public at large.
  • The Initiative in Innovative Computing at Harvard Alyssa A. Goodman IIC Director & Prof. of Astronomy
  • Sample Long Term Goal
    • “ 3D Data Desk”
      • Demo, using data from http://www.electoral-vote.com/2004/info/president.csv)
      • Perseus file