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I'm an associate professor affiliated with DAWN, Statistical Machine Learning Group, and SAIL (bio). I work on the foundations of the next generation of machine-learning systems. These systems draw on ideas from machine learning, databases, and theory, and our group is active in all areas. We build software to more deeply understand the challenges and opportunities of these systems. Our latest project is Snorkel, our code is on github, and there are blog posts about our work.
New stuff:- In SIGMOD19, with folks at Google we talk about lessons learned from Snorkel applied at organizational scale in DryBell.
- In AAAI19, Snorkel folks talk about Training Complex Models with Multi-Task Weak Supervision. We've seen this is a new and exciting way to build machine learning software.
- In Radiology Jan 19, 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!
- Tri, Avner, and Jian try explain why how low-precision random Fourier features generalize better than Nystrom features in the same amount of memory in AIStats19--this result surprising is since, if you measure by feature count the reverse is true!.
- Checkout great workshops run in part by our students Relational Representaton Learning at NeurIPS, and learning with 2nd Learning with Limited data and Graph Representation Learning at ICLR19.
- Beliz, Albert, and Fred learn embeddings in mixed product spaces (hyperbolic, spherical, and euclidean) in ICLR19.
- Alex and Braden's thoughts on the role of massive multitask learning and weak supervision in Software 2.0 in CIDR19.
- Paroma's thoughts about automating weak supervision in Reef in VLDB19
The DeepDive (one pager) project was commercialized as Lattice. As of 2017, Lattice is part of Apple. Our work on architectural changes for converged analytics and machine learning is commericialized as SambaNova Systems. To validate our ideas, we continue to build systems that we hope change the way people do science and improve society. This work is with great partners in areas including paleobiology (Nature), drug repurposing, genomics, material science, and the fight against human trafficking (60 minutes, Forbes, Scientific American, WSJ, BBC, and Wired). Our work is supporting investigations. In the past, we've worked with a neutrino telescope (IceCube Science cover and our modest contribution) and on economic indicators.
A messy, incomplete log of old updates is here.
Christopher Ré
Email: chrismre at cs.stanford.edu
Email: chrismre at cs.stanford.edu
Department of Computer Science
Stanford University
353 Serra Mall
Stanford, CA 94305-9025
Stanford University
353 Serra Mall
Stanford, CA 94305-9025
