This page describes what successful project ownership looks like in the lab. The framing is adapted from Ethan Perez’s tips for empirical alignment research — a rubric built for a high-throughput empirical ML organization — modified for a methodological research environment, where correctness and understanding carry more weight than experiment volume. Companion pages: Starting a Project and Communication Rules.
By roughly the one-year mark, the goal is for you to operate as the Directly Responsible Individual (DRI) for your research projects: the person who moves the work forward, tracks progress, and identifies roadblocks. No one arrives as a DRI, and you are not expected to. In your first year, most tactical direction will come from me; the handoff is gradual, and we will discuss where you are in it explicitly during 1:1s.
The percentages below are a mental model of relative importance, not an evaluation formula.
Thoughtful implementation (45%). In methodological research, speed and understanding are in constant tension. We do not prize a massive volume of hasty experiments — a high volume of poorly designed simulations is worse than no work at all, because someone eventually has to figure out which results to trust. The expectation is a medium volume of high-quality work: implementations careful enough that you deeply understand the data, can refine the methodological approach, speed up your algorithms, and ask progressively better questions each cycle.
Troubleshooting & autonomy (25%). When code breaks or a simulation misbehaves, you take the first pass at diagnosing why. Between meetings you can independently explore logical next steps and resolve standard R and Python issues, including problems with your own local development environment. Getting unstuck is a research skill, not overhead.
Tactical direction (10%). You make sound day-to-day decisions about which analyses and evaluation metrics to prioritize. You operate well with standard check-ins, and you reach out when genuinely blocked — not before you’ve made a serious attempt, and not after you’ve burned three days on it.
Strategic vision (10%). You are beginning to spot new methodological gaps and project ideas in your research area. For some of you that area sits squarely within the lab’s core focus — adaptive interventions, causal inference for mobile health, and sequential decision-making — and for others it sits further afield. Either way, the expectation is the same: a vision that builds outward from the lab’s methodological foundation rather than floating free of it.
Your central working document — for us, the Project Landing Page, the Google Doc described in Starting a Project — stays current. Whether you share an RMarkdown file, a Jupyter notebook, or present live, your updates and plots are clean enough to digest quickly — so our 1:1 time goes to the implications of your results rather than untangling raw output or messy code. Weekly updates follow the template in Communication Rules.
You help your peers, contribute to shared lab resources, and take constructive feedback seriously. Five percent of the rubric, but disproportionate in determining whether this is a good place to do research.
Research is a stochastic decision process. We are exploring unknowns, so expect variance in timelines and be prepared to pivot when evidence points somewhere better. Jacob Steinhardt’s Research as a Stochastic Decision Process is the canonical statement of how we sequence work: attack the highest-uncertainty steps first, because they are the cheapest place to learn you are wrong.
OODA loops over strict deadlines. We prioritize rapid iteration — Observe, Orient, Decide, Act — over rigid schedules, following Ben Kuhn’s playbook for running major projects. Update your priors quickly when models fail to converge or data behaves unexpectedly; the iteration cycle, not the calendar, is the unit of progress.