Data Science and Urban Science @ UW

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A talk at the Urban Science workshop at the Puget Sound Regional Council July 20 2014 organized by the Northwest Institute for Advanced Computing, a joint effort between Pacific Northwest National Labs and the University of Washington.

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  • 3
  • Institutional change rather than specific research projects
  • Institutional change rather than specific research projects
  • What is the studio?

    it’s an open research space where anyone on campus can come to collaborate with a data science team that consists of a several permanent staff with expertise in databases, machine learning, visualization, software engineering, reproducibility, cluster and cloud computing – these are new “research and development” career paths in applied data science, attracting those with significant software backgrounds interested in applying their expertise to science problems

    The Studio will also house a number of data science fellows – partially funded research scientists, visiting scientists, postdocs, and students (including IGERT students as Magda discussed)

    The Studio will be a delivery vector for a number of activities – the seminar series, the lunches, workshops and bootcamps. But you can engage directly with the Studio in a number of ways: the space will be designed to support drop-in collaboration, we will hold scheduled office hours, and a flagship program that I’m really excited about is our data science incubator, which I’ll describe in a moment.
  • These data science collaborations can spin out tools like SQLShare, but we need to make these technology-oriented collaborations more common.

    The next generation of this is an incubation program to scale and concentrate our collaborations

    We want to move from “accidental” encounters to engineered partnerships -- identify promising new opportunities and new partners around campus and invest our time with them.

    We need a shared environment where researchers can learn not only from our team, but also external mentors and most importantly **each other** – we routinely find shared solutions across very different fields. John Wilkerson in political science is using sequence alignment algorithms from biology for text analytics to trace the flow of ideas through legislation – he’ll have a student participating in our incubation program this spring.

    And we intend this to be a true startup environment, with siginifcant potential for technology spinout. We can help find new markets for existting technology as well as finding opportunities for new technologies.
  • Let me give you a brief example of a project a little further upstream that the incubation program can provide access to.

    This work is in a space of open data sharing platforms, along with Socrata here in Seattle, products from Google and Microsoft, and a number of other companies.

    Two observations motivate the products in this space:

    First, there’s a movement toward open data that has researchers, government agencies, and even companies exposing their data assets online for use by others for reasons of transparency, efficiency, accountability. Even for commercial data, there are marketplaces emerging to facilitate the buying and selling of data. All of these use cases need new technology. So that’s one reason.

    Second, if you’re going to use someone else’s data, you need it to be as accessible as possible. In particular, you need to help data analysts use the data “had previously been the realm of programmers and DB adminsistrators” – here I’m quoting Benjamin Romano from in an Xconomy article about Socrata.

    SQLShare is an open data system, but emphasizes rich data manipulation rather than just fetch and retrieval, interoperability with external tools and existing databases, local or cloud deployments, and built-in services for data integration, profiling, and visualization.

    Ginger mentioned this system in her talk – we have maintained a production deployment here on campus for three years focusing on science users. Our observation is that science use cases are a predictor for commercial use cases – businesses are beginning to use data the same way scientists always have – they collect it aggressively, torture it with analytics, use it to make predictions about the world. So we think if we can handle these difficult science use cases that we will also be addressing a significant commercial problem.
  • GENERALISATION POSSIBLE – NOT A ONE OFF ISSUE

  • BIEN DECRIRE LE GRAPH ET LES AXES
  • Cluster in space and often in time
  • Everywhere! But small, often missed by routine network detection…
    Eruptions!!
    What can they tell us?… What’s the state of the science?
  • Great – we have a classifiers that is accurate.
    Let’s extend this work to see to what extent opinions manifest themselves in actions of public health importance.
    A lot of discussion in media on return of vaccine-preventable disease outbreaks.
    Fear of autism, etc.
  • H1N1 vaccination rates recorded in January 2010 (older than 6 months) vs. average sentiment score of users in regions (black) and states (gray)
    Impressed with accuracy of 84% in a 4-class problem.
    Not a surprising result that state-level information might not be that strongly correlated. Wanted to dig deeper into the geographic features of users.


  • MENTION INABILITY TO REPRODUCE ACCURATE CLASSIFIER
  • Dow Constantine’s office, King County Executive
    Fred Jarrett, Chief of Staff for King County
    Tom Stritikus, Dean of the UW College of Education
    Thaisa Way, Landscape Architecture
    Bill Glenn, Socrata
    local company behind data.seattle.gov
  • Data Science and Urban Science @ UW

    1. 1. Data Science @ UW
    2. 2. 2 “It’s a great time to be a data geek.” -- Roger Barga, Microsoft Research “The greatest minds of my generation are trying to figure out how to make people click on ads” -- Jeff Hammerbacher, co-founder, Cloudera
    3. 3. The Fourth Paradigm 1. Empirical + experimental 2. Theoretical 3. Computational 4. Data-Intensive Jim Gray 7/21/2014 Bill Howe, UW 3
    4. 4. “All across our campus, the process of discovery will increasingly rely on researchers’ ability to extract knowledge from vast amounts of data… In order to remain at the forefront, UW must be a leader in advancing these techniques and technologies, and in making [them] accessible to researchers in the broadest imaginable range of fields.” 2005-2008 In other words: • Data-driven discovery will be ubiquitous • UW must be a leader in inventing the capabilities • UW must be a leader in translational activities – in putting these capabilities to work • It’s about intellectual infrastructure (human capital) and software infrastructure (shared tools and services – digital capital)
    5. 5. A 5-year, US$37.8 million cross-institutional collaboration to create a data science environment 5 2014
    6. 6. $9.3 million from Washington Research Foundation to Amplify the Moore/Sloan effort • 6 X 5-year Faculty lines in Data Science • 6 X startup packages • 15 X 3 yr postdoctoral fellows • Funds to remodel and furnish a WRF Data Science Studio • Also $7.1 million to closely-related Institute for Neuroengineering, $8.0 million to Institute for Protein Design, $6.7 million to Clean Energy Institute 6
    7. 7. 7/21/2014 Bill Howe, UW 7 Data Science Kickoff Session: 137 posters from 30+ departments and units
    8. 8. 8 PIs on Moore/Sloan effort + eScience Institute Steering Committee + UW participants in February 7 Data Science poster session Broad collaborations
    9. 9. Establish a virtuous cycle • 6 working groups, each with • 3-6 faculty from each institution
    10. 10. Key Activity: Promote interdisciplinary careers • Interdisciplinary graduate students – New, interdisciplinary “Data Science” Ph.D. tracks and program • Interdisciplinary postdocs (“Data Science Fellows”) – Dual-mentored postdocs with interests in both methods and a domain science • Interdisciplinary research scientists (“Data Scientists”) • Work across disciplines to solve people’s data science challenges • Interdisciplinary faculty – Supported with special hiring and funding initiatives • “Senior Research Fellows” – Short-term and long-term visitors • A diverse faculty steering committee
    11. 11. UW Data Science Education Efforts 7/21/2014 Bill Howe, UW 11 Students Non-Students CS/Informatics Non-Major professionals researchers undergrads grads undergrads grads UWEO Data Science Certificate MOOC Intro to Data Science IGERT: Big Data PhD Track New CS Courses Bootcamps and workshops Intro to Data Programming Data Science Masters (planned) Incubator: hands-on training
    12. 12. 12 Educational transformation Big Data access and management Big Data modeling Big Data analytics Collaborative Big Data scienceData Key Activity: Foster Interdisciplinary Education • Ultimate goal: A new PhD program – Initial goal: A new certificate based on Big Data tracks in all departments – Education highlights: data science courses, co-advising, and internships • End-to-End Research Agenda – Big Data mgmt, analytics, modeling, & collaboration • Cyberinfrastructure Development – Big Data analysis service
    13. 13. • Additional data science educational activities – Coursera MOOCs • Introduction to Data Science (Bill Howe) • Computational Methods of Data Analysis (Nathan Kutz) • High Performance Scientific Computing (Randy LeVeque) – Traditional courses • Many! Example: Biochemistry for Computer Scientists (Joe Hellerstein) • We try to list relevant courses on the eScience Institute website – UW Educational Outreach • 3-course Certificate in Data Science • 3-course Certificate in Cloud Data Management & Analytics • 3-course Certificate in Cloud Application Development on Amazon Web Services • 3-course Certificate in Data Visualization – Workshops and bootcamps • Software Carpentry (Winter & Spring 2013; Winter, Spring, & Summer 2014) • Cosmology and Machine Learning (Autumn 2014)
    14. 14. • An open shared R&D space where researchers from across the campus will come to collaborate • A resident data science team – Permanent staff of ~5 Data Scientists – applied research and development – ~15-20 Data Science Fellows (research scientists, visitors, postdocs, students) – Entrepreneurial mentorship • Modes of engagement – Drop-in open workspace – Studio “Office Hours” – Incubation Program – Plus seminars, sponsored lunches, workshops, bootcamps, joint proposals … Key Activity: “Re-establish the watercooler”
    15. 15. Key Activity: Create scalable impact through a Data Science Incubation Program • Scale and concentrate our efforts – Move from “accidental” encounters to engineered partnerships – Identify emerging opportunities around campus – Provide a shared environment where researchers can learn from an in-house team, external mentors, and each other • A startup environment! – “Seed grant” program • Lightweight – 1-page proposals – Significant potential for technology spinout – new markets for existing technology and new technology for existing markets
    16. 16. Key Activity: Democratize Access to Big Data and Big Data Infrastructure • SQLShare: Database-as-a-Service for scientists and engineers • Myria: Easy, Scalable Analytics-as-a-Service
    17. 17. Open Data sharing platforms • Database-as-a-service for open data analytics • Interoperable with external tools and languages • Local or cloud deployments • Interoperable with existing database platforms • Built-in data integration, profiling, analytics Google Fusion Tables 17 Entrepreneurship 1) “Data once guarded for assumed but untested reasons is now open, and we're seeing benefits.” -- Nigel Shadbolt, Open Data Institute 2) Need to help “non-specialists within an organization use data that had been the realm of programmers and DB admins” -- Benjamin Romano, Xconomy “Businesses are now using data the way scientists always have” -- Jeff Hammerbacher, Cloudera
    18. 18. Halperin, Howe, et al. SSDBM 2013
    19. 19. 19 Scalable Analytics as a Service
    20. 20. 20
    21. 21. Kenya Health Information System Data Grégoire Lurton June 12, 2014 Abie FlaxmanDan HalperinGregoire Lurton
    22. 22. In the beginning
    23. 23. In the beginning
    24. 24. “Much of the material remains unprocessed, or, if processed, unanalyzed, or, if analyzed, not read, or, if read, not used or acted upon” Objectives  Design generalizable method to process HIS- like data  Make important dataset available for analysis  Explore actionable data analysis of HIS data Why do we care?
    25. 25. Metadata Trace - saving Reports of year n saved in January of year n+1 Years were not recorded for the first year of use…
    26. 26. REDPy Repeating Earthquake Detector (Python) An eScience Incubator Project Project Lead: Alicia Hotovec-Ellis Data Scientist: Jake Vanderplas John Vidale Alicia Hotovec-Ellis Jake Vanderplas
    27. 27. What is a “repeating” earthquake? EVENT# 1 2 3 4 5 6 7
    28. 28. Why do we study repeating earthquakes?
    29. 29. The problem(s)… Time (minutes) Time(HH:MM:SS)
    30. 30. Clustering for Ordered in time Event# Event # Ordered with OPTICS Event# Event #
    31. 31. I talked with Alicia a bit yesterday, and she showed me that her earthquake-repeater- searching implementation is more general, and more powerful than I had thought, and closer to trial by others (and I have a particular use in mind in the ongoing iMUSH experiment on Mount St Helens)<snip> So I'm encouraging her to continue to work on it a day per week or so for the forseeable future, assuming you have the facilities to continue the incubation. The project outlives the incubator…… Publications in the works on both the software and the science – from three months of half-time work
    32. 32. Using Twitter data to identify geographic clustering of anti-vaccination sentiments Ben Brooks June 12, 2014 Benjamin Brooks Andrew Whitaker Abie Flaxman
    33. 33. Initial approach • Sentiment regarding vaccination can be discerned from Twitter. • Can we find city- or county-level pockets of anti- vaccination sentiment? • Do these locales correlate with outbreak and vaccination rate data (beyond H1N1)?
    34. 34. Training data issues • Training data from PSU study labeled tweets as positive, negative, neutral, or irrelevant. • Many tweet categorizations seemed suspect. • Produced new training dataset; switched approach to negative tweets vs. all others. • Of tweets we labeled as negative, PSU training data agreed with 36%. • Sample non-negative tweets in training dataset from PSU study: • “RT @Lyn_Sue Lyn_Sue18 Reasons Why u Should NOT Vaccinate Your Children Against The Flu This Season” • “1882 -3 O RT @alexHroz Citizens From All Walks Intend To Refuse Swine Flu "Vaccine,” • “Eighteen Reasons Why You Should NOT Vaccinate Your Children Against The Flu This Season by Bill Sard” • “Swine Flu Vaccine not necessary and not healthy:”
    35. 35. Background: Previous work • “For our sentiment classification, we used an ensemble method combining the Naive Bayes and the Maximum Entropy classifiers…The accuracy of this ensemble classifier was 84.29%.”
    36. 36. Other sentiment approaches • Precision Of all tweets labeled negative by the algorithm, what percentage are “true negatives”? • Recall Of all “true negative” tweets, what percentage are labeled negative by the algorithm? Precision Recall Vaccine-specific keywords 19% 59% Modified general sentiment 25% 41% Naïve Bayes 79% 19% Logistic regression 70% 28% Labeled data from PSU study 41% 36%
    37. 37. Other sentiment approaches • Data labeled by human beings does not perform dramatically better than other classifiers! Precision Recall Vaccine-specific keywords 19% 59% Modified general sentiment 25% 41% Naïve Bayes 79% 19% Logistic regression 70% 28% Labeled data from PSU study 41% 36%
    38. 38. Scalable Analytics over Call Record Data in Developing Nations Project Lead Ian Kelley Information School University of Washington E-mail: ikelley@uw.edu eScience Data Incubator - 12 June 2014 Andrew WhitakerIan Kelley Josh Blumenstock
    39. 39. Map migration patterns of workers during labor market shortages (Rwanda) Measure and categorize mobility patterns Determine peoples’ geographic center of gravity Discover the effects of violent events on internal population mobility (Afghanistan) Track activity patterns over time; identify changes Map connected areas of country eScience Data Incubator - 12 June 2014
    40. 40. eScience Data Incubator - 12 June 2014 Average position during a time period (e.g., day, week)
    41. 41. eScience Data Incubator - 12 June 2014
    42. 42. Towards An Urban Science Incubation Cohort 44 OneBusAway: Transit Traveler Information Systems Foreclosure Rates and changes in poverty concentration PNW Seismic Network Early Warning System Ocean Observatories Initiative Education CRPE
    43. 43. Seattle the tech and innovation hub • “most innovative state” (Bloomberg 12/13) • “smartest city” (Fast Company, 11/13) • only US city on “ten best Internet cities” (UBM’s Future Cities blog, 8/13) • ranked 2nd for women entrepreneurs (geekwire, 2/13) • ranked 4th as global startup hub, > NYC (geekwire, 11/12) • “the top tech city” (geekwire, 6/12) • …and so on 45
    44. 44. eScience Institute + Urban Science • Better public engagement than in physical and earth sciences • Leverages our core interest in open data and open science • Acute need relative to traditionally data-intensive fields – relative newcomers in DS techniques and technologies – We prefer collaborations with smaller labs and individuals as opposed to “Big Science” projects • Seattle offers a unique testbed as an urbanizing region – Brookings “metro”: Interconnected urban, suburban, rural, environment – Engaged, active communities – Strong local interest in open data, open government – Global hub for technology and innovation (next slide) • Connections with King County Executive’s office, State CIO’s office, Seattle CTO’s office, local gov data companies (Socrata) 46
    45. 45. Data Science @ UW We are at the dawn of a revolutionary new era of discovery and learning

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