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.
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. The Fourth Paradigm
1. Empirical + experimental
2. Theoretical
3. Computational
4. Data-Intensive
Jim Gray
7/21/2014 Bill Howe, UW 3
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. A 5-year, US$37.8 million cross-institutional
collaboration to create a data science environment
5
2014
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. 7/21/2014 Bill Howe, UW 7
Data Science Kickoff Session:
137 posters from 30+ departments and units
8. 8
PIs on Moore/Sloan effort
+ eScience Institute
Steering Committee
+ UW participants in
February 7 Data Science
poster session
Broad collaborations
9. Establish a virtuous cycle
• 6 working groups, each with
• 3-6 faculty from each institution
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. 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
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. • 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. • 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. 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. 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. 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
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. 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.
27.
28. 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
33. 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
34. Using Twitter data to identify geographic
clustering of anti-vaccination sentiments
Ben Brooks
June 12, 2014
Benjamin
Brooks
Andrew
Whitaker
Abie Flaxman
35. 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)?
36. 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:”
37. 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%.”
38. 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%
39. 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%
40. 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
41. 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
42. eScience Data Incubator - 12 June 2014
Average position during a time period (e.g., day, week)
44. 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
45. 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
46. 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
47. Data Science @ UW
We are at the dawn of
a revolutionary new era of discovery and learning
Notes de l'éditeur
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