The American Institutes for Research (AIR) and Virginia Tech are collaborating to explore and develop new approaches to combining, manipulating and understanding big data. The two are also looking at how big data analytics can help answer questions critical to solving issues in education, workforce, health, and human and social development. They held two workshops on January 7 and 27, 2014- the first on Education and Workforce Analytics and the second on Health and Social Development Analytics.
Sdal air health and social development (jan. 27, 2014) finalkimlyman
The American Institutes for Research (AIR) and Virginia Tech are collaborating to explore and develop new approaches to combining, manipulating and understanding big data. The two are also looking at how big data analytics can help answer questions critical to solving issues in education, workforce, health, and human and social development. They held two workshops on January 7 and 27, 2014- the first on Education and Workforce Analytics and the second on Health and Social Development Analytics.
SDAL addresses social science in new ways that will transform how we understand the world. Among our goals: creating smart and resilient cities, combatting homelessness, understanding the spread of disease and developing effective public health responses, identifying innovation drivers, and meeting the demand for educated graduates in the field.
Sdal pires, bianica, riots in an urban slum 140813kimlyman
In order to understand the relationship between people, physical space, and future change, a diverse set of methods is used that focuses around three main research areas: agent-based modeling (ABM), geographical information science (GIS) and social network analysis (SNA). The intersection between these research areas can be represented through computational social science (CSS), which lies at the foundation of this research as it represents the interdisciplinary science that uses computational modeling and related techniques to study complex social systems. A computational model of the riots that broke-out in an urban slum after the 2007 Kenyan presidential election is used to demonstrate the value of integrating these research areas. Characteristics such as poverty, overpopulation, and a growing youth bulge put urban slums at greater risk for violence. Using empirical data for which to build the landscape and provide agents with unique attributes, an ABM is integrated with SNA and GIS to simulate the outbreak of riots. The model investigates the role individual identity, group identity, and social influence played on the occurrence and intensity of riots. Model results find that the cyclical nature in the emergence and dissolution of rioting is due to positive reinforcement, an effect that can be largely attributed to the agents’ social networks, and thus their interactions and influences through these networks. Riots arise from the interactions between individuals with unique attributes, all within a connected social network over a physical environment. In order to gain a better understanding of the macro-level patterns that emerge, the nonlinear and reinforcing nature of this system is modeled from the bottom-up.
Ethical Priniciples for the All Data RevolutionMelissa Moody
A presentation by Stephanie Shipp, from the Research Highlights session at the 2019 Women in Data Science Charlottesville Conference. Hosted by the UVA Data Science Institute.
Sdal air health and social development (jan. 27, 2014) finalkimlyman
The American Institutes for Research (AIR) and Virginia Tech are collaborating to explore and develop new approaches to combining, manipulating and understanding big data. The two are also looking at how big data analytics can help answer questions critical to solving issues in education, workforce, health, and human and social development. They held two workshops on January 7 and 27, 2014- the first on Education and Workforce Analytics and the second on Health and Social Development Analytics.
SDAL addresses social science in new ways that will transform how we understand the world. Among our goals: creating smart and resilient cities, combatting homelessness, understanding the spread of disease and developing effective public health responses, identifying innovation drivers, and meeting the demand for educated graduates in the field.
Sdal pires, bianica, riots in an urban slum 140813kimlyman
In order to understand the relationship between people, physical space, and future change, a diverse set of methods is used that focuses around three main research areas: agent-based modeling (ABM), geographical information science (GIS) and social network analysis (SNA). The intersection between these research areas can be represented through computational social science (CSS), which lies at the foundation of this research as it represents the interdisciplinary science that uses computational modeling and related techniques to study complex social systems. A computational model of the riots that broke-out in an urban slum after the 2007 Kenyan presidential election is used to demonstrate the value of integrating these research areas. Characteristics such as poverty, overpopulation, and a growing youth bulge put urban slums at greater risk for violence. Using empirical data for which to build the landscape and provide agents with unique attributes, an ABM is integrated with SNA and GIS to simulate the outbreak of riots. The model investigates the role individual identity, group identity, and social influence played on the occurrence and intensity of riots. Model results find that the cyclical nature in the emergence and dissolution of rioting is due to positive reinforcement, an effect that can be largely attributed to the agents’ social networks, and thus their interactions and influences through these networks. Riots arise from the interactions between individuals with unique attributes, all within a connected social network over a physical environment. In order to gain a better understanding of the macro-level patterns that emerge, the nonlinear and reinforcing nature of this system is modeled from the bottom-up.
Ethical Priniciples for the All Data RevolutionMelissa Moody
A presentation by Stephanie Shipp, from the Research Highlights session at the 2019 Women in Data Science Charlottesville Conference. Hosted by the UVA Data Science Institute.
Smart Data - How you and I will exploit Big Data for personalized digital hea...Amit Sheth
Amit Sheth's keynote at IEEE BigData 2014, Oct 29, 2014.
Abstract from:
http://cci.drexel.edu/bigdata/bigdata2014/keynotespeech.htm
Big Data has captured a lot of interest in industry, with the emphasis on the challenges of the four Vs of Big Data: Volume, Variety, Velocity, and Veracity, and their applications to drive value for businesses. Recently, there is rapid growth in situations where a big data challenge relates to making individually relevant decisions. A key example is personalized digital health that related to taking better decisions about our health, fitness, and well-being. Consider for instance, understanding the reasons for and avoiding an asthma attack based on Big Data in the form of personal health signals (e.g., physiological data measured by devices/sensors or Internet of Things around humans, on the humans, and inside/within the humans), public health signals (e.g., information coming from the healthcare system such as hospital admissions), and population health signals (such as Tweets by people related to asthma occurrences and allergens, Web services providing pollen and smog information). However, no individual has the ability to process all these data without the help of appropriate technology, and each human has different set of relevant data!
In this talk, I will describe Smart Data that is realized by extracting value from Big Data, to benefit not just large companies but each individual. If my child is an asthma patient, for all the data relevant to my child with the four V-challenges, what I care about is simply, “How is her current health, and what are the risk of having an asthma attack in her current situation (now and today), especially if that risk has changed?” As I will show, Smart Data that gives such personalized and actionable information will need to utilize metadata, use domain specific knowledge, employ semantics and intelligent processing, and go beyond traditional reliance on ML and NLP. I will motivate the need for a synergistic combination of techniques similar to the close interworking of the top brain and the bottom brain in the cognitive models.
For harnessing volume, I will discuss the concept of Semantic Perception, that is, how to convert massive amounts of data into information, meaning, and insight useful for human decision-making. For dealing with Variety, I will discuss experience in using agreement represented in the form of ontologies, domain models, or vocabularies, to support semantic interoperability and integration. For Velocity, I will discuss somewhat more recent work on Continuous Semantics, which seeks to use dynamically created models of new objects, concepts, and relationships, using them to better understand new cues in the data that capture rapidly evolving events and situations.
Smart Data applications in development at Kno.e.sis come from the domains of personalized health, energy, disaster response, and smart city.
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Abstract
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I will review: 1) understanding and analysis of informal text, esp. microblogs (e.g., issues of cultural entity extraction and role of semantic/background knowledge enhanced techniques), and 2) how we built Twitris, a comprehensive social media analytics (social intelligence) platform.
I will describe the analysis capabilities along three dimensions: spatio-temporal-thematic, people-content-network, and sentiment-emption-intent. I will couple technical insights with identification of computational techniques and real-world examples using live demos of Twitris (http://twitris2.knoesis.org).
Gather evidence to demonstrate the impact of your researchIUPUI
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Presented at the 11th Healthcare CIO Certificate Program, School of Hospital Management, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand on February 24, 2021
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Data Analytics has incredible potential to impact education worldwide. There is significant amount of data being collected related to schools and students (e.g. personal information, attendance, marks, reduced lunches and so on), but much of it is administrative and/or siloed and/or unexamined. This document talks about data analytics in education domain.
Smart Data - How you and I will exploit Big Data for personalized digital hea...Amit Sheth
Amit Sheth's keynote at IEEE BigData 2014, Oct 29, 2014.
Abstract from:
http://cci.drexel.edu/bigdata/bigdata2014/keynotespeech.htm
Big Data has captured a lot of interest in industry, with the emphasis on the challenges of the four Vs of Big Data: Volume, Variety, Velocity, and Veracity, and their applications to drive value for businesses. Recently, there is rapid growth in situations where a big data challenge relates to making individually relevant decisions. A key example is personalized digital health that related to taking better decisions about our health, fitness, and well-being. Consider for instance, understanding the reasons for and avoiding an asthma attack based on Big Data in the form of personal health signals (e.g., physiological data measured by devices/sensors or Internet of Things around humans, on the humans, and inside/within the humans), public health signals (e.g., information coming from the healthcare system such as hospital admissions), and population health signals (such as Tweets by people related to asthma occurrences and allergens, Web services providing pollen and smog information). However, no individual has the ability to process all these data without the help of appropriate technology, and each human has different set of relevant data!
In this talk, I will describe Smart Data that is realized by extracting value from Big Data, to benefit not just large companies but each individual. If my child is an asthma patient, for all the data relevant to my child with the four V-challenges, what I care about is simply, “How is her current health, and what are the risk of having an asthma attack in her current situation (now and today), especially if that risk has changed?” As I will show, Smart Data that gives such personalized and actionable information will need to utilize metadata, use domain specific knowledge, employ semantics and intelligent processing, and go beyond traditional reliance on ML and NLP. I will motivate the need for a synergistic combination of techniques similar to the close interworking of the top brain and the bottom brain in the cognitive models.
For harnessing volume, I will discuss the concept of Semantic Perception, that is, how to convert massive amounts of data into information, meaning, and insight useful for human decision-making. For dealing with Variety, I will discuss experience in using agreement represented in the form of ontologies, domain models, or vocabularies, to support semantic interoperability and integration. For Velocity, I will discuss somewhat more recent work on Continuous Semantics, which seeks to use dynamically created models of new objects, concepts, and relationships, using them to better understand new cues in the data that capture rapidly evolving events and situations.
Smart Data applications in development at Kno.e.sis come from the domains of personalized health, energy, disaster response, and smart city.
Citizen Sensor Data Mining, Social Media Analytics and ApplicationsAmit Sheth
Opening talk at Singapore Symposium on Sentiment Analysis (S3A), February 6, 2015, Singapore. http://s3a.sentic.net/#s3a2015
Abstract
With the rapid rise in the popularity of social media, and near ubiquitous mobile access, the sharing of observations and opinions has become common-place. This has given us an unprecedented access to the pulse of a populace and the ability to perform analytics on social data to support a variety of socially intelligent applications -- be it for brand tracking and management, crisis coordination, organizing revolutions or promoting social development in underdeveloped and developing countries.
I will review: 1) understanding and analysis of informal text, esp. microblogs (e.g., issues of cultural entity extraction and role of semantic/background knowledge enhanced techniques), and 2) how we built Twitris, a comprehensive social media analytics (social intelligence) platform.
I will describe the analysis capabilities along three dimensions: spatio-temporal-thematic, people-content-network, and sentiment-emption-intent. I will couple technical insights with identification of computational techniques and real-world examples using live demos of Twitris (http://twitris2.knoesis.org).
Gather evidence to demonstrate the impact of your researchIUPUI
This workshop is the 3rd in a series of 4 titled "Maximize your impact" offered by the IUPUI University Library Center for Digital Scholarship. Faculty must provide strong evidence of impact in order to achieve promotion and tenure. Having strong evidence in year 5 is made easier by strategic dissemination early in your tenure track. In this hands-on workshop, we will introduce key sources of evidence to support your case, demonstrate strategies for gathering this evidence, and provide a variety of examples. These sources include citation metrics, article level metrics, and altmetrics as indicators of impact to support your narrative of excellence.
Can medical education take advantage of Learning Analytics techniques? How? Where? In this presentation a study is analyzed pinpointing three areas in which Medical Education needs to invest and all three are related to Learning Analytics.
People & Organizational Issues in Health IT Implementation (February 24, 2021)Nawanan Theera-Ampornpunt
Presented at the 11th Healthcare CIO Certificate Program, School of Hospital Management, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand on February 24, 2021
Research process and research data management. Many universities are looking at how they can better serve the needs of researchers. Ken Chad Consulting worked with the University of Westminster to look the needs and attitudes of researchers and admin staff in terms of research data management (RDM). The result led the University to look first at the whole lifecycle and workflows of research administration. This in turn led to the innovative, rapid development of a system to support researchers and admin staff. Presented by Suzanne Enright (University of Westminster) and Ken Chad at the annual UKSG conference in April 2014
INSPIRE @ IMSH 2016 in San Diego, CA was a hit for newcomers and prior attendees. Learn about the growth and progress of INSPIRE, simulation-based research, and new projects down the pipeline.
Data Analytics has incredible potential to impact education worldwide. There is significant amount of data being collected related to schools and students (e.g. personal information, attendance, marks, reduced lunches and so on), but much of it is administrative and/or siloed and/or unexamined. This document talks about data analytics in education domain.
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Sdal air education workforce analytics workshop jan. 7 , 2014.pptx
1. Education / Workforce Analytics
and Big Data –
A Joint AIR and Virginia Tech Workshop
SALLIE KELLER, DIRECTOR
SOCIAL AND DECISION ANALYTICS LABORATORY
VIRGINIA BIOINFORMATICS INSTITUTE AT VIRGINIA TECH
Social and Decision Analytics Laboratory
2. Outline
Social and Decision Analytics Laboratory
• Pressures of Today
• Big data
– Why important?
– What about privacy?
• Education/Workforce
analytics
– What makes it big data?
– How does big data change
current approaches?
• Methodology challenges
3. Education/Workforce Pressures of Today
• Management of costs and expenditures
– Be less reliant on public funding
• Achieve higher standards and greater outcomes
– Across populations of students with diverse backgrounds,
abilities, and aspirations
– Produce graduates with job-ready skills whose collective
productivity can immediately impact the economy
• Training for higher cognitive capabilities
– Utilize a greater diversity of learning ecosystems
• Social networks, multidisciplinary, reverse classrooms, workforce
training, life-long learning, etc.
– Incorporate an ever changing flow of technological innovations
Social and Decision Analytics Laboratory
4. Education Analytics Opportunities
For example….
• Learning and demography?
• Learning and experiential characteristics of the learner?
• Learning and content presentation sequencing effectiveness?
• Learning and environments to stimulate curiosity?
• Learning and social interaction effects on learning progress?
Social and Decision Analytics Laboratory
5. Workforce Analytics Opportunities
Social and Decision Analytics Laboratory
Developing a STEM-educated
and capable workforce
• Equilibrium of demand and
supply?
• Measures of success?
• Improved data collection
approaches?
Education and workforce
development are difficult to
decouple
6. Big
Data
-‐
Doesn’t
matter
what
its
called,
only
matters
what
you
do
with
it
Social and Decision Analytics Laboratory
• Big data
– Structured & unstructured
– Collections
• Designed
• Observational/convenience
• Statistics / analytics
– Replication, reproducibility,
representativeness
– Description, association, causation
• prediction ≠ correlation
• Cost drivers
– Analytics and informatics, NOT data collection
7. Now Big Data is Changing Social Sciences
• Social science research has
traditionally been informed by
surveys and statistically
designed experiments
– Clean, well-controlled, limited in
scale (~103) and/or resolution
• Bringing “Big data” to bear
for social policy
– Data informed computational
social science models
– Quantitative social science
methods and practice at scale
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9. Personal Data - New Asset Class
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• European Council 1995/1996:
– “… any information relating to an
identified or identifiable natural
person; an identifiable person
is one who can be identified
(data subject), directly or indirectly,
in particular by reference to an
identification number or to one
or more factors specific to his
physical, physiological, mental,
economic, cultural or social identity.”
• World Economic Forum 2011:
– “… digital data created by and
about people.”
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10. World Economic Forum 2013
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Yesterday
• Definition of personal data is
predetermined and binary
• Individual provides legal
consent but not truly engaged
• Policy framework focuses on
minimizing risk to individual
Today
• Definition of personal data is
contextual and dependent on
social norms
• Individual engaged and
understands how data is used
and value created
• Policy needs to focus on
balancing protection with
innovation and economic
growth
10
11. Further Privacy Thoughts
• Will people voluntarily give up their data if they can see a
personal or societal benefit?
• Are norms/expectations changing with generations?
• What are technical fixes for multi-level privacy/
classification?
• What is the optimal level of privacy for studies of interest?
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12. Can we table privacy for the duration of
the workshop?
• Deserves serious, devoted conversation
• We should be leaders in this conversation
• Will need to specifically address as projects develop
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13. What Makes it Big Data?
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Data Characteristics
• Multi-sourced
• Observational
• Noisy
• Multi-purposed
14. Multi-Sourced Data
Learning and development occurs within context
• Learner - including past experiences & mental processes
• Learning situation and content
• Department and institutional environment
• Local, state, and national education systems
• Political and economic factors
Information communication technology opens opportunity to
capture meta data and provenance of the information
Challenge: integration and interpretation of data captured
under such varied circumstances
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17. Observational Data / Convenience Sample
• Can come from every stakeholder, source, or technology
that interacts with the learner
• Little discrimination on what is captured
• Key strokes, eye movement, content accessed, test scores and
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testing attempts, etc.
• On-demand data from multiple systems
– Social networks, school records, work history, medical records,
extramural activities, etc.
Presents opportunity to study the learning processes as it
naturally occurs
Challenge: manage biases and target group coverage
18. Social and Decision Analytics Laboratory
Meanwhile, if the quantity of
information is increasing by
2.5 quintillion bytes per day,
the amount of useful
information almost certainly
isn’t. Most of it is just noise,
and the noise is increasing
faster than the signal.
Nate Silver, 2013
Challenge: uncertainty quantification
Noisy data
19. Multi-Purposed Data
Data reuse for multiple purposes
• Macro-level: regional, state, national, and international
• Meso-level: institution-wide
• Micro-level: individual learners, cohorts, and groups
An opportunity to more fully use data
Challenge: coherent aggregation across the levels
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Source: Buckingham Shum, S. (2012)
20. Analytics Basics
Four key stages in application of analytics:
• Stage A – Extraction and reporting of inputs, outputs,
responses, links, transfers, transactions, and other data
• Stage B – Analysis and monitoring of learner, teacher,
operations, and other performance
• Stage C – What-if decision support
• Stage D – Modeling and simulation
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21. Key Questions for Education Analytics
Past
Present
Future
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Descriptive
What happened?
What is happening
now?
What will happen?
Historical Reporting
(Stage A)
Assessment
Reporting: Alerts
(Stage B)
Extrapolations: Alerts
(Stage B)
Associative
Inference
What variables best
explain what
happened?
What interventions
seem reasonable?
What is the best/worst
that can happen?
Relationships and
Modeling (Stage B)
Options (Stage C)
Optimization,
Simulation (Stage D)
22. Learning Analytics Dashboards
Example Blackboard Dashboard for Student Feedback
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23. A Major Missing Component
Integration of theory into analytics
• Education, pedagogy, learning, and development
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This eclectic approach is both a
strength and a weakness: it
facilitates rapid development and
the ability to build on established
practice and findings, but it – to
date – lacks a coherent
articulated epistemology of its own.
Clow, 2012
If we are helping people make
decisions, then learning analytics
is a moral and ethical endeavor.
Being able to predict is not a high
enough standard, we must
understand why and how before
we can ethically recommend.
Atkisson, 2011
24. Learning Disposition Theories
Dimensions Positive characteristics of learners Negative characteristics
1 Adapted from Buckingham Shum and Deakin Crick, 2012, op. cit.
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Changing &
Learning
Energy to learn and improve their
minds
Static and satisfied with current knowledge
Critical
Curiosity
Desire to learn and explore areas of
knowledge
Passive and not curious
Meaning
Making
Links current knowledge with material
to be learned
Accumulating with minimal linking attempted
Dependence &
Fragility
Resilient, persevering, and challenge-seeking
Avoids challenges/risks and is overwhelmed
by mistakes
Creativity Visualizes many perspectives and use
imagination.
Limited in perspective and bound to rules
Learning
Relationship
Balances social and private aspects of
learning
Either too dependent on others or too
isolated
Strategic
Awareness
Self-aware and tries different learning
strategies
Robotic
25. Five Stages in Application of Analytics
• Stage A – Extraction and reporting of inputs, outputs,
responses, links, transfers, transactions, and other data
• Stage B – Analysis and monitoring of learner, teacher,
operations, and other performance
• Stage C – What-if decision support
• Stage D – Modeling and simulation
• Stage E – Combined education, pedagogy, learning,
development, statistical modeling, and decision support
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26. Key questions for education analytics
Past
Present
Future
Social and Decision Analytics Laboratory
Descriptive
What happened?
What is happening
now?
What will happen?
Historical Reporting
(Stage A)
Assessment
Reporting: Alerts
(Stage B)
Extrapolations: Alerts
(Stage B)
Associative
Inference
What variables best
explain what
happened?
What interventions
seem reasonable?
What is the best/worst
that can happen?
Relationships and
Modeling (Stage B)
Options (Stage C)
Optimization,
Simulation (Stage D)
Causative
Inference
How and why did it
happen?
What interventions
are prescribed?
What does theory
suggest can happen?
Theory-based
Relationships and
Modeling (Stage E)
Recommendations
(Stage E)
Theory-based
Predictions,
Optimization,
Simulation (Stage E)
27. Big Data Analytics Framework – Focus on Purpose
• Learning Analytics
– To manage the courses, learning experiences, and learner
progress to maximize education effectiveness
• Academic Analytics
– To manage institutions and maximize the institution’s operational
efficiency
• Evaluative Analytics
– To assess institutional compliance with regulations and evaluate
the outcomes of educational programs
• Comparative Analytics
– To assess the relative standing and effectiveness of local, state,
regional, national, and international educational systems
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28. What are Methodological Issues?
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New methods and tools
are needed to ensure
• Data quality
• Representativeness
• Replication
• Reproducibility
• Characterization of
noisy data
• Managing biases
– Selection bias
– Measurement bias
National Research Council 2013
29. Big Data Education/Workforce Analytics Conclusions
• Moving past tracking with large-scale data analytics
“What you have is a thermometer with no theory of action
behind it. If I have a fever, nothing here is going to tell me
how to deal with the fever. All it’s going to do is tell me I have
a fever.”
- Mark Schneider, AIR, 2013
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30. Goals for the Workshop
• Imagine a different world
• Look for synergistic capabilities to build partnerships
• Assess opportunities to integrate multiple sources of data
and approaches to comprehensively understand
education/workforce issues
• Incorporate theory into our thinking
• Propose prototype projects to work on together to set the
stage for future projects
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31. Virginia Tech’s Social and Decision Analytics Lab
• SDAL joins Virginia Bioinformatics Institute
• Central to “Information Biology” theme
– Study of massively interacting systems, from molecular to
social phenomena
• Collaboration across VT and beyond
– Embrace VBI mantra of transdisciplinary team science
Sallie Keller, Professor of Statistics and Director,
Social and Decision Analytics Laboratory,
Virginia Bioinformatics Institute at Virginia Tech
Sallie41@vbi.vt.edu
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