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Professor at Northwestern CS, AI and visualization
Professor at Northwestern CS, AI and visualization with public evidence across AI research.
After two days with Claude Fable 5 the best way I can describe it is "relentlessly proactive" - here's an example where I dropped in a screenshot of a bug and it span up custom CORS Python servers and used pyobjc-framework-Quartz to capture screenshots simonwillison.net/2026/J…
Thoughts on metascientific consequences of AI-generated slides & ideas diluting the impression that speakers are commited to what they present. Science runs on personal attachment more than we admit. If it were a cake mix, how wouldn we add back an egg? statmodeling.stat.colum…
Some thoughts on the blog today on "humans are unreliable narrators too" as a common defense of AI mental-state language, and what I think it misses about the nature of reasoning or thinking or belief or intention as we understand these terms statmodeling.stat.columbia.edu/202…
More in paper, including an empirically-informed demo of how much info statistical significance and exact replication success provide for estimating true signal-to-noise ratio & effect direction: users.eecs.northwestern.edu/~jhullman/AI... Blog post: statmodeling.stat.columbia…
I couldn't make it to #IC2S2 but Huaman Sun will be presenting our work on validation of silicon samples shortly -- if you're attending check it out! Paper: arxiv.org/pdf/2602.15785
Nice post from @eytan.adar.prof on the cargo cult science of "AI-native" PhD students, who come in super productive because they perform all major parts of research with genAI. No one needs all these quick papers, and they can really hurt students down the road. eytanadar.medi…
About a year ago, I wrote skeptically about LLMs in peer review -- not because of skepticism about their inherent capabilities, but because I don't want the research community to optimize for the taste of any one person/system. What's changed since then?
More in paper, including an empirically-informed demo of how much info statistical significance and exact replication success provide for estimating true signal-to-noise ratio & effect direction: users.eecs.northwestern.edu/~jhullman/AI... Blog post: statmodeling.stat.columbia…
Using AI to support peer review seems unavoidable, but what quality checks should AI implement? We can take some lessons from metascience on the hard reward design problem that is AI review. I wrote a paper synthesizing a few points the emerging lit seems at risk of confusing. 1/
Center = Jessica Hullman. Left = members they follow (green edges). Right = members who follow them (blue edges). Top = mutual follows (orange edges, slightly larger). Drag any node to reposition; click to open that profile.
