Most evaluations stop at the average effect. I'm interested in where the average falls short.
Which populations does a program miss? What unintended consequences does it produce? And what needs to change in order to act on what the evidence shows? I've spent my career applying that lens to questions of education, opportunity, and the systems that shape people's life trajectories.
My background resists easy categorization, which I've come to think is a feature rather than a bug. I hold a BS in Symbolic Systems and a BA in Comparative Studies in Race and Ethnicity from Stanford. My undergraduate training combined computational approaches to cognition and decision-making with the study of inequality, institutions, and social change. Much of my later work has been driven by the intersection of those perspectives. After Stanford I spent several years doing substantive work before doctoral study: founding what became Stanford's First-Generation and Low-Income Student Success Center, working in college access and nonprofit organizations, and collaborating on research at the intersection of economics and education policy. I then went on to doctoral work in Sociology at Harvard, where I've worked alongside social and cognitive psychologists, industrial-organizational psychologists, biodemographers, and social-science geneticists.
The throughline across all of it is a specific set of questions: where does the average effect fall short? Which populations does a program miss? Who experiences unintended consequences, and why? I care about getting this right because average effects can mask real harm, and the people most likely to be missed are usually the ones with the least margin for error. At its core, my work is about helping people and organizations make better decisions with imperfect information.
Much of my work has focused on education and social mobility: college access, student success, the psychosocial costs of moving between social worlds, and the institutional environments that shape whether people thrive or struggle. But the methods and frameworks I use travel well, and I'm drawn to any context where evidence can meaningfully improve how resources and opportunities are directed.
I thrive on variety and collaboration — I've always had many things running at once and I do my best work that way. What I'm looking for now is a role that offers that richness within a single organizational home, rather than across multiple simultaneous appointments.
As Co-President of Stanford's First-Generation and Low-Income Alumni Network, led strategic planning, governance, and operations for a 2,000+ member national organization. Revised and updated the organization's bylaws and developed standing rules to supplement them. Redesigned the needs assessment process from scratch, focusing on social science rigor and data quality, then worked persistently through board retreats and strategic planning sessions to ensure members actually engaged with the findings and translated them into organizational goals. Secured $20K in funding, built consistent grant evaluation procedures, and led large-scale programming and stakeholder partnerships across the Stanford alumni and student ecosystem.
Founded and piloted the institutional program that became Stanford's First-Generation and Low-Income Student Success Center. Conducted surveys, focus groups, peer-institution benchmarking, and social-belonging research to identify the specific barriers affecting students' academic transitions, financial navigation, and sense of belonging. Synthesized findings into a 100+ page report for university leadership and developed programming — including a week-long orientation — later adopted by peer institutions.
Examined whether and for whom upward mobility actually improves health — with particular attention to the groups and contexts where the expected benefits don't materialize or come at a cost. Drawing on Add Health, the Health and Retirement Study, and the Wisconsin Longitudinal Study, this work investigates how race, sex, and class origins moderate the relationship between mobility and psychological distress; how psychosocial stressors mediate those effects; and how the university setting itself shapes health disparities among those who traveled farthest to get there. Received 11 grants totaling over $200,000, including the NSF Graduate Research Fellowship.
Led research operations for a large-scale randomized intervention designed to improve selective college enrollment among high-achieving, low-income students. Managed a team of up to 16 staff across intervention design, survey administration, data systems, and grant compliance. Oversaw three simultaneous national surveys, managed $6M+ in funding from IES, Gates, Spencer, and Smith Richardson foundations, and rebuilt major survey and tracking operations in-house when a $1.1M vendor contract was discontinued mid-project.
As a Data Science for Social Good Fellow, developed a machine learning framework with four U.S. school districts to identify students at risk of not graduating on time. Built interpretable models using up to 16 years of longitudinal student data, with a deliberate focus on actionable risk categories rather than demographic proxies — so that interventions could target individual needs rather than group membership. Designed a generalizable pipeline in SQL and Python for cross-district analysis and temporal model validation. Presented at Bloomberg's Data for Good Exchange.
As an instructor for Harvard's General Education course on philanthropy and the nonprofit sector, worked most directly with students on the applied components: evaluating nonprofit organizations, assessing theories of change and evidence bases, and making funding recommendations through the Philanthropy Lab. Guided student teams from problem identification through lean canvas development, nonprofit selection, and final funding pitches. Also taught and facilitated applied learning in Psychology courses on well-being and social impact, and served as Head Teaching Fellow for courses in Psychology and Sociology enrolling up to 450 students.
Alongside my own research and organizational work, I've maintained a sustained practice of guiding others through the research process, from question development through data collection, analysis, writing, and publication or presentation. This spans independent student research projects, undergraduate honors theses, and cross-disciplinary writing and methods advising across career stages. The projects belong to the people I work with; the intellectual work we do together is real.
I've guided more than 44 students through full independent research cycles, from initial question through final paper, through Lumiere Education, Polygence, and independent advising, in addition to hundreds of students through research writing in course contexts. Many independent mentees have gone on to publish or present their work. Projects have ranged across the social sciences, including work on mental health access and district socioeconomic inequality, cultural responses to autism across societies, identity and belonging in online communities, stress and burnout across professional contexts, and career aspirations among young people navigating gender and cultural constraints. I've also served as primary advisor for Harvard College senior honors theses, prepared students for conference presentations, and evaluated theses as a departmental reader. I'm currently exploring how AI tools can support students in developing genuine research capability, with an emphasis on critical evaluation of evidence and independent reasoning.
Across departments and career stages, I've advised researchers and writers on research design, quantitative and qualitative methods, literature review development, argument structure, and substantive revision — working with undergraduates, graduate students, and midcareer professionals through courses, individual consultations, and the Harvard writing advising infrastructure. A mentoring document I co-developed through Harvard's Advising Project working group was adopted by the Graduate School of Arts and Sciences and peer institutions.
Most programs work for some people under some conditions — and don't for others. I specialize in identifying where effects diverge from expectations: which subpopulations are underserved, where programs produce unintended consequences, and what those patterns mean for how resources and decisions should be structured. This is the lens I bring to evaluation, research design, and strategy work.
Designing and conducting rigorous evaluations that go beyond average effects — examining heterogeneity, unintended consequences, and the conditions under which programs work or fall short for different populations. Comfortable with experimental, quasi-experimental, and mixed-methods designs.
Applying evidence to funding decisions: assessing organizational capacity and program theory, building evaluation frameworks that are both rigorous and practical, and using findings to improve — not just judge — the work being funded.
Translating complex, real-world questions into answerable research designs. Skilled across a wide range of quantitative methods — longitudinal modeling, multilevel analysis, mediation and moderation, spatial analysis, and machine learning — as well as qualitative methods including focus groups, cognitive interviewing, and inductive and deductive research design.
Helping organizations understand what their evidence is actually telling them — and building the systems, processes, and decision frameworks needed to act on it. Experienced across higher education, philanthropy, nonprofits, and research organizations.
Managing complex, multi-stakeholder projects under real constraints. Experience building teams, overseeing large budgets, running multi-site national surveys, and rebuilding operational systems mid-project when plans change.
Developing analytical capability in others — whether guiding a student through their first independent research project, advising a team on study design, or building the evaluation infrastructure an organization needs to learn from its own work.
A sustained research program examining whether and for whom upward mobility improves health — and where it doesn't. This work takes seriously that the average effect of mobility on health is not the whole story: race, sex, class origins, and institutional context all moderate who benefits, who bears hidden costs, and why. Dissertation in progress.
Upward intergenerational mobility and psychological distress in the United States
Presented at the American Sociological Association Annual MeetingLived experiences of allostatic load: Upward mobility and health among adults in elite graduate programs
Draft in progressCollaborative research with Adolfo Cuevas (NYU) examining how psychosocial stressors, childhood adversity, and educational mobility shape health outcomes in middle-aged and older adults.
Educational mobility and telomere length in middle-aged and older adults: Testing three alternative hypotheses
Biodemography and Social BiologyLongitudinal analysis of psychosocial stressors and body mass index in middle-aged and older adults in the United States
The Journals of Gerontology: Series BCumulative stress effects of childhood adversity
Presented at the Population Association of America Annual MeetingA data-driven framework for identifying high school students at risk of not graduating on time
Bloomberg Data for Good Exchange, 2015 · With Reid A. Johnson, Ruobin Gong, Anushka Anand, and Alan FritzlerI'm looking for roles where I can put rigorous analysis to work on problems that matter in an organization where varied, collaborative work is the norm rather than the exception. I'm particularly drawn to philanthropy, impact-focused research, and education or social policy, where evidence is taken seriously and used to actually change how things are done.
I'm also available for consulting engagements in program evaluation and research design. For roles in higher education, advising, and student success, see my higher education profile.
The best way to reach me is by email.