Connor Jerzak's Academic Content

We train students to become full-stack researchers—and increasingly, designers of the systems that do research. Our students learn to carry projects end-to-end: from idea generation and theory to data creation, analysis, and iterative refinement across diverse subfields. Using modern AI (including agentic workflows) and scalable computation, students build reproducible pipelines that can ingest and update planetary-scale data—satellite imagery + other high-dimensional sources. But the goal isn’t tool use for its own sake: students learn to set the objectives, constraints, and evaluation standards that guide these systems through large spaces of hypotheses, while grounding results in causal inference + careful measurement. The outcome is work that can rigorously test counterfactuals and translate evidence into improvements in well-being. 

We welcome students at every stage to engage with projects—undergraduates, grad students, and those from highly non-traditional backgrounds.