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Creating a Data Science Ecosystem for Scientific, Societal and Educational Impact

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Creating a Data Science Ecosystem for Scientific, Societal and Educational Impact

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The new era of data science is here. Our lives and society are continuously transformed by our ability to collect data in a systematic fashion and turn that into value. The opportunities created by this change also comes with challenges that push for new and innovative data management and analytical methods as well as translating these new methods to applications in many areas that impact science, society, and education. Collaboration and ability of multi-disciplinary teams to work together and communicate to bring together the best of their knowledge in business, data and computing is vital for impactful solutions. This talk will discusses a reference ecosystem and question-driven methodology, called PPODS, to make impactful data science applications in many fields with specific examples in hazards, smart cities and biomedical research.

The new era of data science is here. Our lives and society are continuously transformed by our ability to collect data in a systematic fashion and turn that into value. The opportunities created by this change also comes with challenges that push for new and innovative data management and analytical methods as well as translating these new methods to applications in many areas that impact science, society, and education. Collaboration and ability of multi-disciplinary teams to work together and communicate to bring together the best of their knowledge in business, data and computing is vital for impactful solutions. This talk will discusses a reference ecosystem and question-driven methodology, called PPODS, to make impactful data science applications in many fields with specific examples in hazards, smart cities and biomedical research.

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Creating a Data Science Ecosystem for Scientific, Societal and Educational Impact

  1. 1. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Creating a Data Science Ecosystem for Scientific, Societal and Educational Impact İlkay ALTINTAŞ, Ph.D. Chief Data Science Officer, San Diego Supercomputer Center Division Director, Cyberinfrastructure Research, Education and Development Founder and Director, Workflows for Data Science Center of Excellence
  2. 2. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu SAN DIEGO SUPERCOMPUTER CENTER at UC San Diego Providing Cyberinfrastructure for Research and Education • Established as a national supercomputer resource center in 1985 by NSF • A world leader in HPC, data-intensive computing, and scientific data management • Current strategic focus on “Big Data”, “versatile computing”, and “life sciences applications” Recent Innovative Architectures • Gordon: First Flash-based Supercomputer for Data-intensive Apps • Comet: Serving the Long Tail of Science
  3. 3. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Data Science Today is Both a Big Data and a Big Compute Discipline BIG DATA COMPUTING AT SCALE Enables dynamic data-driven applications Smart Manufacturing Computer-Aided Drug Discovery Personalized Precision Medicine Smart Cities Smart Grid and Energy Management Disaster Resilience and Response Requires: • Data management • Data-driven methods • Scalable & dynamic process coordination • Resource optimization • Skilled interdisciplinary workforce New era of data science!
  4. 4. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu What is Data Science?
  5. 5. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Ultimate Goal BigData Insight Action Data Science
  6. 6. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu How does successful data science happen? Insight Data Product “Big” Data Question Exploratory Analysis and Modeling Insight
  7. 7. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Customer Demographic Previous Purchases Book reviews What kind of books does this customer like? Book recommendations Example: Book Recommendations
  8. 8. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Model of customer’s book preferences New book information Who is likely to like this book? Find Potential Audience for a New Book
  9. 9. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Action to market the book to the right audience Who is likely to like this book? Market a New Book
  10. 10. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Action to market the book to the right audience Who is likely to like this book? Insight Action Market a New Book
  11. 11. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Historical data Near real-time data Prediction Creating Actionable Information
  12. 12. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Prediction Action
  13. 13. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Why is the increased interest in Data Science?
  14. 14. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu + Big Data Scalable Computing Anywhere Anytime
  15. 15. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu What is and How Much Data Is Big Data?
  16. 16. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu 204 Million emails 200,000 photos 1.8 Million likes 2.78 Million video views 72 hours of video uploads Every minute…
  17. 17. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Velocity Variety Volume Scalable batch processing Stream processing Extensible data storage, access and integration Big Data Characteristics
  18. 18. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Nearly every problem today is transformed by big data.
  19. 19. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Example: Geospatial Big Data • Flood of new data sources and types • Needs new data management, storage and analysis methods • Too big for a single server, fast growing data volume • Requires special database structures that can handle data variety • Too continuous for analysis at a later time, with increasing streaming rate, i.e., velocity • Varying degrees of uncertainty in measurements, and other veracity issues • Provides opportunities for scientific understanding at different scales more than ever, i.e., potential high value Real-time sensors Weather forecast Satellite imagery Sea Surface Temperature Measurements Drone imagery
  20. 20. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Example: Biomedical Big Data http://nbcr.ucsd.edu
  21. 21. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Scientific Big Data By the Numbers… • HPWREN: hpwren.ucsd.edu • 30 TB of data annually • MODIS: modis.gsfc.nasa.gov • 219 TB of data annually • Precision Medicine: Genome sequence • 4 EB (1018 bytes) of data in 2016 (Ref: www.fastcompany.com) • LIGO, Deep Space Network, Protein Data Bank, …
  22. 22. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu 100 MBs ~= couple of volumes of Encyclopedias A DVD ~= 5 GBs 1 TB ~= 300 hours of good quality video LHC ~= 15 PBs a year
  23. 23. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Exponential data growth!
  24. 24. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu 1021
  25. 25. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu How do we find the connections and answer questions that benefit the society? “We are drowning in information and starving for knowledge” – John Naisbitt Source: Megatrends, 1982
  26. 26. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu How do we amplify the value of Big Data?
  27. 27. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Create an Ecosystem that Enables Needs and Best Practices • data-driven • scalable • dynamic • process-driven • collaborative • accountable • reproducible • interactive • heterogeneous • includes many different expertise
  28. 28. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu A Typical Collaborative Data Science Ecosystem
  29. 29. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu ACQUIRE PREPARE ANALYZE REPORT ACT Approach: Focus on the Process and Team Work to Answer a Question …
  30. 30. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu ACQUIRE PREPARE ANALYZE REPORT ACT Basic Steps in a Data Science Process • Import raw dataset into your analytics platform • Explore & Visualize • Perform Data Cleaning • Feature Selection • Model Selection • Analyze the results • Present your findings • Use them ACQUIRE PREPARE ANALYZE REPORT ACT
  31. 31. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu COORDINATION AND WORKFLOW MANAGEMENT DATA INTEGRATION AND PROCESSING DATA MANAGEMENT AND STORAGE Process-driven Solution Architectures and the Role of Workflows
  32. 32. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu … COORDINATION AND WORKFLOW MANAGEMENT DATA INTEGRATION AND PROCESSING DATA MANAGEMENT AND STORAGE COMMUNICATION AND FEEDBACK EXPLORATION SCALABILITY PROVENANCE SECURITY ACQUIRE PREPARE ANALYZE REPORT ACT
  33. 33. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu WORKFLOW MANAGEMENT Application Integration, Coordination, Optimization, Communication, Reporting COMPOSABLE DATA SERVICES Deep Learning, Analytics, HPC, Training, Notebooks COMPOSABLE SYSTEMS GPU, CPU, Big Data, Neuromorphic, Networks, Storage, … PROVENANCE SECURITY RESOURCE MANAGEMENT Kubernetes Container Cloud
  34. 34. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu SOLUTION ARCHITECTURE DOMAIN KNOWLEDGE
  35. 35. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Using dynamic workflows for data science… … requires methodology, research and tool development.
  36. 36. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Workflows for Data Science Center of Excellence at SDSC Goal: Methodology and tool development to build automated and operational workflow-driven solution architectures on big data and HPC platforms. Focus on the question, not the technology! Real-Time Hazards Management wifire.ucsd.edu Data-Parallel Bioinformatics bioKepler.org Scalable Automated Molecular Dynamics and Drug Discovery nbcr.ucsd.edu WorDS.sdsc.edu • Access and query data • Support exploratory design • Scale computational analysis • Increase reuse and reproducibility • Save time, energy and money • Formalize and standardize • Train
  37. 37. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Balance of: • team building • process management • performance optimization • provenance tracking • training and education
  38. 38. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu While working with experts on… • data modeling and integration • data management services • analytical methods • communication and visualization • domain expertise
  39. 39. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu How can I get smart people to collaborate and communicate? …to utilize data and computing to generate insights and solve a question. Focus on the question, not the technology! Team Building
  40. 40. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Process Management
  41. 41. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Process for Practice of Data Science Workflow Design Reporting Workflow Monitoring Workflow Execution Workflow Scheduling and Execution Planning Execution Review Provenance Analysis Deploy and Publish Programmability Ease of use, iteration, interaction, re-use, re-purpose Scalability From local experiments to large-scale runs Reproducibility Ability to validate, re-run, re-play BUILD and EXPLORE SHARE SCALE and ITERATE LEARN and REPORT
  42. 42. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Some P’s in PPoDS Platforms Process People Problem or Purpose ? Programmability
  43. 43. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Metrics for accountability should be built into the process. Timeline Purpose Expectations Planning of deliverables Cost
  44. 44. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Treat Each Step in the Solution Process as a Conceptual Pod Pod è sub-process Defined by: • Purpose and goal • Stakeholders • Expectations • Key questions to be answered, production/consumption relationships, needs, dependencies, limits, … • Contracts • Performance, economic, accuracy, policy, privacy, reproducibility, political, … • Knowns • Known unknowns
  45. 45. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Zooming into a simple example… PREPARE ANALYZE Data Exploration Schema Integration Query Processing Machine Learning …
  46. 46. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu The insights need to be evaluated to turn them into action. Platforms Process People Purpose? Programmability Metrics Product Insight Action ?
  47. 47. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Implementation of the actions needs many things working together. Process StakeholdersAutomation Action
  48. 48. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu GIS Files Sensor NoSQL Social Database Action The impact of the actions should be monitored, measured and evaluated. Evaluation Measure Monitor
  49. 49. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Evaluation will determine the next steps. Favorable Results? Revisit? Further Opportunities? Action Evaluation Real-time Action?
  50. 50. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu COORDINATION AND WORKFLOW MANAGEMENT … http://kepler-project.org National Resources (Gordon) (Comet) (Stampede)(Lonestar) Cloud Resources Execution Platforms Local Cluster Resources ACQUIRE PREPARE ANALYZE REPORT ACT
  51. 51. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dynamic data-driven coordination & resource optimization Requires: Ability to explore and scale on multiple platforms Workflows increasingly becoming the dynamic operations research tool for science.
  52. 52. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Where do we make use of such capabilities?
  53. 53. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Data Science for Social Good
  54. 54. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Smart City and Hazards IoT Applications • Many sensed and organizational open datasets • Potential to improve public safety and quality of life
  55. 55. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu How do we Better Predict Wildfire Behavior? • Wildfires are critical for ecology, but volatile • Fuel load is high due to fire suppression over the last century • Drought, higher temperatures • Better prevention, prediction and maintenance of wildfires is needed Photo of Harris Fire (2007) by former Fire Captain Bill Clayton Disaster management of (ongoing) wildfires heavily relies on understanding their Direction and Rate of Spread (RoS). Fire is Part of the Natural Ecology…. … but requires Monitoring, Prediction and Resilience
  56. 56. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu What was lacking is… a dynamic system integration of real-time sensor networks, satellite imagery, near-real time data management tools, wildfire simulation tools, and connectivity to emergency command centers .…. before, during and after a firestorm.
  57. 57. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu
  58. 58. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Big Data Fire Modeling Visualization Monitoring WIFIRE: A Scalable Data-Driven Monitoring, Dynamic Prediction and Resilience Cyberinfrastructure for Wildfires
  59. 59. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu High Performance Wireless Research and Education Network FARSITE http://hpwren.ucsd.edu/cameras >160 Meteorological Sensors and Growing Major success to bring internet to incident command in the field. Used in over 20 fires over time. Most popular operational fire behavior modeling system.
  60. 60. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Closing the Loop using Big Data -- Wildfire Behavior Modeling and Data Assimilation -- • Computational costs for existing models too high for real-time analysis • a priori -> a posteriori • Parameter estimation to make adjustments to the (input) parameters • State estimation to adjust the simulated fire front location with an a posteriori update/measurement of the actual fire front location Conceptual Data Assimilation Workflow with Prediction and Update Steps using Sensor Data
  61. 61. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Fire Modeling Workflows in WIFIRE Real-time sensors Weather forecast Fire perimeter Landscape data Monitoring & fire mapping
  62. 62. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Firemap Tool • A web-based GIS environment: • access information related to fire behavior • analyze what-if scenarios • model real-time fire behavior • generate reports • Powered by WIFIRE Firemap Web Interface WIFIRE Data Interfaces WIFIRE Workflows Computing Infrastructure http://firemap.sdsc.edu
  63. 63. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Data-Driven Fire Progression Prediction Over Three Hours Collaboration with LA and SD Fire Departments http://firemap.sdsc.edu August 2016 – Blue Cut Fire Tahoe and Nevada Bureau of Land Management Cameras: 20 cameras added with field-of-view
  64. 64. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Northern CA Fires 10/09/17 through now… 300K+ unique visitors and ~3M hits in 5 days http://firemap.sdsc.edu
  65. 65. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Some Machine Learning Case Studies • Smoke and fire perimeter detection based on imagery • Prediction of Santa Ana and fire conditions specific to location • Prediction of fuel build up based on fire and weather history • NLP for understanding local conditions based on radio communications • Deep learning on multi-spectra imagery for high resolution fuel maps • Classification project to generate more accurate fuel maps (using Planet Labs satellite data)
  66. 66. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Classification project to generate more accurate fuel maps • Accurate and up-to-date fuel maps are critical for modeling wildfire rate of speed and potential burn areas. • Challenge: • USGS Landfire provides the best available fuel maps every two years. • The WIFIRE system is limited by these potentially 2-year old inputs. Fuel maps created at a higher temporal frequency is desired. • Approach: • Using high-resolution satellite imagery and deep learning methods, produce surface fuel maps of San Diego County and other regions in Southern California. • Use LandFire fuel maps as the target variable, the objective is create a classification model that will provide fuel maps at greater frequency with a measure of uncertainty. Cluster 1: Short Grass
  67. 67. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu WIFIRE Team: It takes a village! • PhD level researchers • Professional software developers • 27 undergraduate students • UC San Diego • UC Merced • MURPA University • University of Queensland • 1 high school student • 5 MSc and 5 MAS students • 2 PhD students (UMD) • 1 postdoctoral researcher UMD - Fire modeling UCSD MAE - Data assimilation SDSC - Cyberinfrastructure, Workflows, Data engineering, Machine Learning, Information Visualization, HPWREN Calit2/QI- Cyberinfrastructure, GIS, Advanced Visualization, Machine Learning, Urban Sustainability, HPWREN SIO - HPWREN
  68. 68. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Process for Precision Education • How are the students performing? • What does a drop out process really start? What are early signs? • How many students do we expect for a subject next year? What are the trends? • When will a student graduate? • What are personalized learning paths? • When is the best time to take a course to graduate on time? • How does the curriculum serve the local economy and workforce? Some Questions
  69. 69. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Parts of the Solution • Stakeholders • Datasets • Compliance requirements • Defined actions • Analytical methods • Technical infrastructure Bias Transparency Verification Accuracy Ethics Reproducibility
  70. 70. Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Dr. ILKAY ALTINTAS ialtintas@ucsd.edu Contact: Ilkay Altintas, Ph.D. Email: ialtintas@ucsd.edu Questions? PartsofthepresentedworkisfundedbyNSF,DOE, NIH,UCSanDiegoandvariousindustrypartners.

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