AAU PhD Education Initiative
As Assistant Dean, I led the design and implementation of doctoral education analytics for Duke’s engagement with the AAU. The systems I built helped catalyze policy and curriculum changes across departments and were adopted by peer institutions nationwide.
Rethinking the Purpose of PhD Education
In 2018, the Association of American Universities (AAU) launched a bold, multi-campus initiative to modernize PhD training in the United States. The premise was clear: if research universities were going to remain relevant in the 21st century, they had to reckon with a basic disconnect. While the majority of PhD graduates were building meaningful careers beyond the tenure track, most doctoral programs were still designed with only that single path in mind.
The AAU initiative aimed to change that. It challenged institutions to better understand where their students actually go, what skills they need to thrive, and how well doctoral education was preparing them for the full range of professions they enter—research, policy, teaching, industry, entrepreneurship, and beyond. The goal sounded simple: align training with reality. But in practice, it called for a fundamental shift in how graduate schools think, measure, and act.
At Duke, I was asked to lead our institutional participation. It was a natural extension of the work I was already doing as Assistant Dean of Assessment and Evaluation, where I had been designing data infrastructure to track outcomes and support continuous improvement across more than 100 programs.
But this project asked something deeper. It wasn’t just about building better reports. It was about whether we could use data to ask better questions—about preparation and purpose. Could we move from passive reporting to active reflection? Could we turn institutional research into a driver of institutional change?
From Data Silos to Strategic Insight
Like most research universities, Duke had no shortage of data on graduate students. The challenge was fragmentation. Course records lived in one system, advising logs in another, and career outcomes in yet another—if they were tracked at all. The result was a patchwork of metrics that made it hard to see the full picture, let alone act on it. I led the effort to build that connective tissue—to turn siloed information into strategic insight. Working across IT, institutional research, and graduate programs, I designed a scalable analytics framework that mapped the doctoral student lifecycle from admission to placement.
Our system made it possible to:
🔄 Track course and milestone progression by discipline, cohort, and student demographics
🧭 Visualize career outcomes not just by broad sectors, but by specific fields and identity groups
🛠️ Identify gaps in professional development access, flagging who participated, when, and in what formats
📉 Surface patterns in attrition and time-to-degree, enabling departments to intervene earlier and more effectively
We weren’t just documenting—we were creating a diagnostic lens. One that let departments ask sharper questions and imagine different futures for their programs. The data became less about accountability and more about insight—less about measurement and more about momentum.
Helping Departments See What They Could Improve
Once the tools were built, the real work began: turning data into action at the department level. I collaborated directly with faculty and program directors to interpret the findings in context—not as performance audits, but as mirrors that could reveal patterns, gaps, and new possibilities.
We weren’t handing down mandates. We were creating visibility—and giving departments the clarity and autonomy to lead.
Some examples stand out:
📊 Teaching Load Disparities
In a biomedical science program, data showed that graduate students were logging significantly more teaching hours than peers in similar disciplines. The department launched an internal review and implemented new policies to rebalance workloads and protect research time.🌍 Access Gaps for International Students
In the humanities, our data revealed that international PhD students were underrepresented in career and professional development programs. The department responded by embedding career exploration into required coursework and offering expanded resources for international trainees.🎓 Mentoring Structures
Several departments used our data to evaluate advising frequency and distribution, leading to more accessible mentoring assignments and clearer expectations for faculty.
Each insight opened the door to meaningful change. And each change made doctoral education more intentional and attuned to the realities students actually face. We didn’t just help departments improve their programs—we helped them reframe what improvement could mean.
From Campus Pilot to National Framework
As the work matured at Duke, it began to resonate beyond our campus. What started as a local response to the AAU’s call for innovation quickly became a model for how data could support—not dictate—graduate education reform. I was invited to consult with institutional research leaders at peer universities, sharing both the structure of our analytics and the strategies we used to build trust with faculty.
What caught people’s attention wasn’t just the dashboards. It was how we had embedded the data into the rhythms of academic decision-making. How we made space for departments to reflect, revise, and act with clarity.
Across the AAU network, institutions began adapting our approach to fit their own contexts. They used our model to:
Align curricula with the evolving skills and career paths valued across sectors
Track gaps in time-to-degree, attrition, and career placement across student populations
Integrate doctoral analytics into strategic planning, budget allocation, and accreditation cycles
Redefine success metrics for graduate programs, moving beyond academic job placement
This wasn’t just about data systems—it was about reimagining doctoral education itself. We helped campuses move from reporting for compliance to reflecting for change. And in doing so, we gave them a framework for continuous improvement grounded in evidence, equity, and aspiration.
Why It Still Matters
This project is meaningful because of what it revealed about the power of data when it’s used with intention, care, and clarity. In a space as tradition-bound as doctoral education, change is often slow and uneven. But this initiative proved that when institutions have the right tools—and the trust to use them—they can see themselves differently. Data became a catalyst: not for accountability in the narrow sense, but for reflection, for conversation, and ultimately for reform.
What made this moment powerful was the alignment of forces that rarely converge: institutional appetite for change, robust data infrastructure, and a genuine willingness among faculty to question inherited models. It was a rare instance where the technical, the cultural, and the strategic all pointed in the same direction. I saw firsthand how thoughtful analytics could illuminate structural inequities, surface hidden patterns, and enable leaders to act with greater clarity and conviction.
More than anything, this project reshaped how I think about the role of analytics in institutional life. It affirmed that data, when designed with empathy and intention, can do more than inform—it can build momentum. It can shift narratives. It can make abstract ideals, like equity and preparation, concrete and measurable. The experience sharpened my belief that data strategy is not just a technical function; it’s a form of leadership. And in the right context, it can move entire systems toward something more honest, more effective, and more human.
The formal announcement can be found here.
Founded in 1900, the AAU consists of leading research universities in the United States and Canada. Its member institutions award nearly half of all U.S. doctoral degrees.
