David Bau Portfolio
Assistant Professor at Northeastern University (PhD, MIT) whose lab studies how deep networks internally represent and can be edited to change what they know; created ROME, a method for locating and directly editing factual associations stored inside a language model, and leads the NSF-funded National Deep Inference Fabric project.
What makes it work
A breakdown of the choices that make this portfolio stand out.
A named method that other papers now build on
ROME (Locating and Editing Factual Associations in GPT) gave the field both a technique and a reusable name, and a visible line of follow-up work (Function Vectors, Concept Sliders) shows other researchers building directly on it.
Public research infrastructure, not just papers
The National Deep Inference Fabric is shared, NSF-funded infrastructure for running interpretability experiments on large models — a contribution to the field's capacity to do research, not only a result within it.
A lab page that reads as a living research program
Listing multiple 2026 ICLR papers across distinct subtopics (symbolic reasoning, model internals, theory of mind) shows an active, multi-threaded lab rather than a single-topic research niche.
What AI Researchers can take from this
Specific, actionable tips to apply to your own portfolio — no generic advice.
Give a technique you develop a clear name — a named method is easier for the field to cite, extend, and build follow-up work on top of.
Consider building shared infrastructure (tools, compute access, datasets) for your subfield, not only publishing results that use infrastructure others built.
Keep several related research threads visibly active at once rather than one narrow topic — it signals a running lab, not a single finished project.
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