I'm an associate professor in the Stanford AI Lab (SAIL) affiliated with DAWN and the Machine Learning Group (bio). Our lab works on the foundations of the next generation of machine-learned 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). For the sake of transparency, I do my best to list companies I advise or invest in here, many of which involve former members of the lab.
- Albert and team released code for our work state space models. Albert's first tweet storm is a good overview for now.
- Some resources for a budding community in Data-Centric AI and a blog post about it.
- In NeurIPS21, we share some of our results on sequence modeling, sparsity, NAS and introduce two benchmarking projects.
- Albert and team continue to make progress on linear state space layers.
- Beidi and Tri continue to more deeply understand low-rank and sparsity (code)
- Led by friends at CMU, we rethink NAS for diverse tasks.
- Arjun introduces a very exciting MRI dataset with clinical labels description and code
- Avanika and Piero make a new benchmarking toolkit around Ludwig.
- 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 self-supervision on medical images:
- Recent Talks
- Recent Software 2.0 Overview at HAI
- Thanks, NeurIPS! Our Test-of-time Award talk for Hogwild! is on YouTube
- A quick overview of video our work on Hidden Stratification.
- A narrated version of Overton, our high-level framework for machine learning built at Apple. (pptx|YouTube) and the paper.
- MLSys 20 keynote talk (pdf|pptx) 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.