
I'm an associate professor in the Stanford AI Lab (SAIL) affiliated with DAWN and the Statistical Machine Learning Group (bio). Our lab works on the foundations of the next generation of machinelearned systems.
 On the machine learning side, I am fascinated by how we can learn from increasingly weak forms of supervision and by the mathematical foundations of such techniques.
 On the systems side, I am broadly interested in how machine learning is changing how we build software and hardware. I'm particularly excited when we can blend ML and systems, e.g,. Snorkel, Overton (YouTube), or SambaNova.
While we're very proud of our research ideas and their impact, the lab's real goal is to help students become professors, entrepreneurs, and researchers. To that end, over a dozen members of our group have started their own professorships. With students and collaborators, I've been fortunate enough to cofound projects including SambaNova, Snorkel, and Factory along with two companies that are now part of Apple, Lattice (DeepDive) and Inductiv (HoloClean). I am fortunate to participate as an advisor or investor in some amazing companies including Agita Labs, AI21, Curie, DataChat, Datometry, Exotanium, MeasureMe, Opaque, OtterTune, Pixie Labs (now part of New Relic), Predibase, Ramp, Sentropy (now part of Discord), Thistle, Voltron Data, and others.
 Some resources for a budding community in DataCentric AI and a blog post about it.
 In ICML21, we describe some results on hyperbolic geometry, model evaluation, and stability.
 We continue our exploration of hyperbolic geometry showing that Busemann functions and ideal points allow for projections with higher fidelity (lower distortion) in hyperbolic space.
 In Mandoline, we think about how a user can combat distribution shift by allowing them to finely slice their data.
 In Catformer, we make transformers a bit more stable using concatenation.
 In NAACL21, a pair of papers about robustness gym a demonstration and an industry track paper comparing modern named entity linking systems.
 In ICLR2021, model patching, and selfsupervision on medical images:
 Beidi Chen and friends talk about Mongoose, a learnable LSH framework for Efficient Training. Oral.
 Karan Goel et al talk about how to patch your models to handle differences among subgroups.
 Sarah Hooper et al describe techniques to (very) weakly supervise medical image segmentation tasks.
 Recent Talks
 Recent Software 2.0 Overview at HAI
 Thanks, NeurIPS! Our Testoftime Award talk for Hogwild! is on YouTube
 A quick overview of video our work on Hidden Stratification.
 A narrated version of Overton, our highlevel framework for machine learning built at Apple. (pptxYouTube) and the paper.
 MLSys 20 keynote talk (pdfpptx) or WWW BIG for an overview of work. More articles on new group website also see github.
 My DAC Sky Talk slides are here
A messy, incomplete log of old updates is here.