Oxford Recommendation Letter Example: Epigenetics researcher to precision public health (Score 92)

I am writing in support of the applicant's application to the MSc in Social Data Science at Oxford. I supervised their undergraduate dissertation in my research group over approximately ten months, and the observations below are drawn from that direct working relationship. My department sits within public health, and the applicant came to me with a proposal to examine epigenetic variation as a marker for population-level health disparities — an ambitious framing for a BSc project. What I want to convey here is not the ambition of the topic but what I actually watched them do with it. The first scene I would point to is a methods review meeting roughly six weeks into the project. The applicant had drafted an analysis plan that relied on a standard differential methylation approach, and I pushed back on whether the chosen pipeline adequately accounted for cell-type heterogeneity in the tissue samples they intended to use. This is a genuine technical problem in epigenomics, and many students at this stage either defer entirely to the supervisor or defend their original plan without engaging the substance. The applicant did neither. They came back to the following meeting having read three methodological papers I had not assigned, identified a reference-based deconvolution approach that was feasible within our lab's resources, and presented a revised analysis plan with a clear rationale for the trade-offs involved. I did not tell them which papers to read or which method to adopt. That kind of self-directed technical problem-solving, in response to critique rather than in spite of it, is not something I see routinely at undergraduate level. The second scene is different in character. Midway through the project, the applicant produced a policy-facing memo summarising their interim findings for a non-specialist audience — a requirement I had built into the project to test whether students could translate quantitative work across registers. The memo was directed at a public health planning context and asked: what, if anything, do these epigenetic signals tell a commissioner about precision health targeting? The applicant's draft was technically accurate but initially too dense for its intended reader. When I returned it with comments, what struck me was how they handled the revision. Rather than simply cutting jargon, they restructured the argument — leading with the population inference rather than the method — and added a brief section explicitly flagging the limitations of using epigenetic proxies for causal claims in policy settings. That last addition was their own judgment call, and it was the right one. It showed me they understood the difference between what the data could support and what a policy audience might want it to say. I should be honest about one area where I think the applicant is still developing. Their engagement with the social science literature underpinning health policy — the theoretical frameworks that sit behind precision public health as a concept — was thinner than their command of the biological and statistical methods. When I raised this in supervision, they acknowledged it directly and began reading more widely, but I would say their comfort in that interdisciplinary space was still consolidating by the time the project concluded. For a programme like Social Data Science, which asks students to move fluently between computational methods and social inquiry, this is worth naming. I do not think it is a disqualifying gap — the methodological foundation is genuinely strong — but it is an area where I expect they will need to invest early in the programme. To be direct about comparative standing: across the undergraduate cohorts I have supervised in recent years, the applicant's capacity for independent methods reasoning and their willingness to engage substantively with critique place them among the stronger students I have worked with at this stage. I am making that observation based on what I saw in supervision, not on final grades alone. The MSc in Social Data Science will ask students to apply computational and quantitative tools to social and policy questions under conditions of genuine ambiguity. Based on what I observed over ten months — the methods judgment, the revision under critique, and the emerging ability to translate analysis into policy-relevant argument — I believe the applicant is well-placed to do that work. I support this application. Please feel free to contact me directly if further information would be helpful.