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 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.
Crazily enough, you've probably used a system that uses something from our lab in last few hours. This is thanks to amazing students and collaborations with
Google ads,
YouTube,
Apple, and more.
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 and
Snorkel, along with two companies that are now part of Apple, Lattice (DeepDive) and Inductiv (HoloClean). I am fortunate to participate as an advisor/investor in some amazing companies including
DataChat,
OtterTune,
Pixie Labs (now part of New Relic),
Thistle,
Ursa Computing, and others.
- Karan and Nazneen folks from Salesforce and UNC describe Robustness Gym
- In ICLR2021, model patching, and self-supervision 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 (a few images) medical image segmentation tasks.
- We're looking for great postdocs jointly with the Mobilize Center.
- 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.
- Bootleg is up! It's the successor of one of the first industrially deployed self-supervised systems (at Apple). In CIDR21
- Talk info: Apple NLU Summit, KDD Knowledge Graphs, KDD Converse, Triangle Computer Science Distinguished Lecture, JHU, MIDAS @ Michigan, Google Ads ML Keynote, Large-Scale Learning Keynote, Wisconsin MLOS, NDBC, Naver Labs. My DAC Sky Talk slides are here
- In NeurIPS 2020, memory units, hidden strat, and non-euclidean geometry.
- Albert, Tri, Stefano, and Atri describe our work understanding recurrent models and memory from first principles using orthogonal polynomials in Hippo (code). Spotlight
- Nimit, Jared, Geoff, and Albert describe how to prevent some forms of hidden stratification (blog) and an overview video
- Ines, Albert, and Vaggos describe how to solve hierarchical clustering problems with hyperbolic geometry—with provable guarantees! code
- In ICML 2020, we describe our continuing work on weak supervision and data augmentation in two papers:
- In ACL2020, we describe some of our continuing work on embeddings, compression, and geometry.
- A bunch of great collaborations in nature-family journals, clinical journals, and others
- In Journal of Biomedical Informatics, Rebecca, Jared, Ann, Siyi and Daniel Comparison of segmentation-free and segmentation-dependent computer-aided diagnosis of breast masses on a public mammography dataset
- In Science Translational Medicine, Johannes, Gill, et al describe AMELIE that how to speed up diagnosis for rare diseases.
- In BMC Bioinformatics, Emily, Russ et al describe how to Extract Chemical Reactions from Text using Snorkel
- In Cell Patterns, Jared and Alex examine how to weakly supervise text and images.
- In NPJ Digital Medicine, Khaled leads applying weak supervision to EEG for efficient seizure detection.
- In NPJ Digital Medicine, Alison Callahan and Jason A Fries led an amazing effort to apply weak supervision in device surveillance in health records or here.
- In Nature Comms, Weak supervision for Cardiac MRI videos for rare aoritc valve disorders
- In Nature Comms, the world's largest machine read GWASKB--both with help from Snorkel's ideas.
- In Radiology, Jared's paper about using deep learning in image triage: at what training set sizes do modern methods provide utility in radiology? This is collaboration with great folks in the medical school!
A messy, incomplete log of old updates is here.