Dean’s Blog: What Do We Want to Be Recognized for in 10 Years?

What Do We Want to Be Recognized for in 10 Years? 

As the first School of Data Science in the U.S., we have an almost unique opportunity to address this question from a foundational perspective. Other institutions are looking for our leadership. Do we grow in an ad hoc way loosely around all that data science encompasses or do we try to be more purposeful?

As a 3-year-old School of Data Science (SDS) at the University of Virginia (UVA), we have spent considerable time defining what data science means to us. It is embodied in our agreed upon model encompassing the domains of data science. This has proven very powerful. As a biologist, I liken it to a taxonomy. It provides a common frame of reference from which we can all work, even if over time it requires modification as new knowledge is acquired. Beyond a taxonomy, we have defined for ourselves a set of guiding principles – quality, openness, transparency, responsibility, etc. And beyond that a possible subset of disciplines to which we will apply data science–biomedicine, environment, democracy, digital humanities, business, etc. This framing is amazingly broad. Recognizing we can’t do everything, is this level of framing enough? I don’t think so.

The taxonomy is the foundation, the guiding principles, the guardrails, and disciplines represent one possible path. But where does this path go? We have an obvious sense of where we want the path to lead – to diverse, career ready, competent leaders who become valuable contributors to society; to research that positively impacts society and serves our communities. This is what all higher education institutions strive for. Will this make us stand out?

In a more constrained and predefined environment, like a university department, charting the path is easier. Hire the best faculty that work in a specific field and let them go at it as teachers and researchers. The breadth of data science, the opportunities afforded by interdisciplinarity, and pressures in the marketplace make the path to greatness for the nation's first School of Data Science not as clear.

Should our path focus on faculty that provide a complement of methods expertise? Certainly, this is critical, but without grounding in a discipline it sounds more like a computer science department. Nevertheless, we need, at the coarsest level, and the one that occupies most of our thinking to date, the following (non exclusive) methodologists that align to our Domains of Data Science:

• Systems – data engineers, cybersecurity experts, software experts, network experts
• Design – visualization experts, user-interface experts, dissemination experts, human-computer interaction experts more broadly.
• Analytics – machine learning experts, statisticians, applied mathematicians
• Value – Data governance experts, ethicists, policy experts, lawyers

How then should we think about the disciplines to which the domains are applied? Note, I have not mentioned scientific disciplines above, but have intentionally broadened the scope beyond STEM to include humanities and the social sciences. Acknowledging this breadth makes us a university within a university constrained only by the data-driven nature of our scholarship. In short, too broad to be manageable and be able to lead in higher education. So how do we lead?

One approach would be to select a set of disciplines and ignore the rest. This seems at odds with our desire to be a School Without Walls. A better approach would recommend we align ourselves with our University’s path to greatness. It is where our School’s leadership will be most responsive and, with a catchphrase of being "Great and Good," it is hard to argue. Here, timing is on our side. Around the time the School was formed, the University of Virginia welcomed a new president, provost, and COO who embarked on the 2030 Plan, a strategic plan for the next ten years. Collectively, we have developed a School of Data Science strategic plan which lays out goals and objectives that map to the larger University plan. What does it mean to “align ourselves with the University’s path to greatness? ”How does that impact what we will be recognized for in ten years? 

Answering this question requires an understanding of what we are already doing. Here is my incomplete list of what we are doing and how we are having impact:

• School
  • A diverse, equitable, and inclusive environment.
  • A training ground for future faculty and staff leaders in data science and beyond.
  • Professional support to achieve career goals for faculty and staff.
• Education
  • High-quality undergraduate and graduate programs recognized by our peers and our students.
  • Innovative pedagogy that supports the above.
  • Alumni who are contributors  and leaders in responsible data science.
  • Application of data science to improve education, from K-12 through lifelong learning.
  • K-12 on ramps that serve as a champion and advocate of underrepresented populations in data science.
• Research
  • Biomedicine and Health Science
    • Contributions to multi-scale modeling of biological systems.
    • Early stage drug discovery with an emphasis on rare genetic childhood diseases and pandemic response.
    • A better understanding and treatment of neurological and non-neurological diseases.
    • Improvements to community-based healthcare with an emphasis on reducing health disparities.
    • Mobility and the treatment of disabilities.
  • Digital Democracy and Humanities
    • Various contributions to a better understanding of the human condition
    • Cybersecurity
    • The influence of technology on democracy, business, environment and national security.
  • Environment
    • Contributions to environmental resilience with emphasis on our own environs.
• Community (as defined by the Inclusive Excellence Plan) engagement which leads to:
  • Contributions to smart cities with emphasis on equality and sustainability.
  • Advances in the use of civic technology to improve people’s lives.
  • Minority workforce development.
  • Advocates for a more just and equitable society.
  • Greater open scholarship and the means to measure those contributions.

Questions for discussion:

• Is this list the right way to think about the overarching question of “What Do We Want to be Recognized for in 10 Years”? 
• Is it possible to be recognized for all of the above? 
• If not, how should we prioritize and why? 
• Can we coalesce our thinking to come up with a collective recognition goal such as:
  • A leader in responsible data science.
  • A leader in equity and justice through data science.
  • A leader of societal change through data science.
  • A leader in creating a sustainable future through data science
• What are we missing in our thinking?

These are difficult questions but exciting to be at a point in our evolution where it makes sense to try and answer them in a meaningful way. I look forward to that discussion. Onwards.