author
Co-Founder & CEO
,
Snorkel AI
Faculty, University of Washington
Alex Ratner is the co-founder and CEO at Snorkel AI, and an affiliate assistant professor of computer science at the University of Washington. Prior to Snorkel AI and UW, he completed his Ph.D. in computer science advised by Christopher Ré at Stanford, where he started and led the Snorkel open source project. His research focused on data-centric AI, applying data management and statistical learning techniques to AI data development and curation.
The latest from Alex
Blog
Snorkel AI Welcomes Devang Sachdev as Vice President of Marketing
We are inventing a new way to build enterprise AI applications. Taking a data-centric approach, we are making machine learning iterable, faster to deploy, and ultimately more practical.That is a fantastic opportunity, but it also presents one of our biggest challenges – figuring out how to bridge the gap between developers at the vanguard of machine learning and business leaders…
Blog
Snorkel AI: Putting Data First in ML Development
Today I’m excited to announce Snorkel AI’s launch out of stealth! Snorkel AI, which spun out of the Stanford AI Lab in 2019, was founded on two simple premises: first, that the labeled training data machine learning models learn from is increasingly what determines the success or failure of AI applications. And second, that we can do much better than labeling this…
Extracting chemical reactions from text using Snorkel
Enzymatic and chemical reactions are key for understanding biological processes in cells. Curated databases of chemical reactions exist but these databases struggle to keep up with the exponential growth of the biomedical literature. Conventional text mining pipelines provide tools to automatically extract entities and relationships from the scientific literature, and partially replace expert curation, but such machine learning frameworks often require a large amount of labeled training data and thus lack scalability for both larger document corpora and new relationship types. We developed an application of Snorkel, a weakly supervised learning framework, for extracting chemical reaction relationships from biomedical literature...
Research Paper
Extracting chemical reactions from text using Snorkel
Enzymatic and chemical reactions are key for understanding biological processes in cells. Curated databases of chemical reactions exist but these databases struggle to keep up with the exponential growth of the biomedical literature. Conventional text mining pipelines provide tools to automatically extract entities and relationships from the scientific literature, and partially replace expert curation, but such machine learning frameworks often…
AMELIE speeds Mendelian diagnosis by matching patient phenotype and genotype to primary literature
The diagnosis of Mendelian disorders requires labor-intensive literature research. Trained clinicians can spend hours looking for the right publication(s) supporting a single gene that best explains a patient’s disease. AMELIE (Automatic Mendelian Literature Evaluation) greatly accelerates this process. AMELIE parses all 29 million PubMed abstracts and downloads and further parses hundreds of thousands of full-text articles in search of information supporting the causality and associated phenotypes of most published genetic variants. AMELIE then prioritizes patient candidate variants for their likelihood of explaining any patient’s given set of phenotypes. Diagnosis of singleton patients (without relatives’ exomes) is the most time-consuming scenario,...
Research Paper
AMELIE speeds Mendelian diagnosis by matching patient phenotype and genotype to primary literature
The diagnosis of Mendelian disorders requires labor-intensive literature research. Trained clinicians can spend hours looking for the right publication(s) supporting a single gene that best explains a patient’s disease. AMELIE (Automatic Mendelian Literature Evaluation) greatly accelerates this process. AMELIE parses all 29 million PubMed abstracts and downloads and further parses hundreds of thousands of full-text articles in search of information…
Training Complex Models with Multi-Task Weak Supervision
Proposing a framework for integrating and modeling such weak supervision sources by viewing them as labeling different related sub-tasks of a problem, which we refer to as the multi-task weak supervision setting
Research Paper
Training Complex Models with Multi-Task Weak Supervision
Proposing a framework for integrating and modeling such weak supervision sources by viewing them as labeling different related sub-tasks of a problem, which we refer to as the multi-task weak supervision setting
The Role of Massively Multi-Task and Weak Supervision in Software 2.0
Outlining a vision for a Software 2.0 lifecycle centered around the idea that labeling training data can be the primary interface to Software 2.0 systems.
Research Paper
The Role of Massively Multi-Task and Weak Supervision in Software 2.0
Outlining a vision for a Software 2.0 lifecycle centered around the idea that labeling training data can be the primary interface to Software 2.0 systems.
Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale
This is first-of-its-kind study showing how existing knowledge resources from across an organization can be used as weak supervision in order to bring development time and cost down by an order of magnitude, and introduce Snorkel DryBell, a new weak supervision management system for this setting
Research Paper
Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale
This is first-of-its-kind study showing how existing knowledge resources from across an organization can be used as weak supervision in order to bring development time and cost down by an order of magnitude, and introduce Snorkel DryBell, a new weak supervision management system for this setting
Slice-Based Learning: A Programming Model for Residual Learning
Proposing Slice-based Learning, a new programming model in which the slicing function (SF), a programmer abstraction, is used to specify additional model capacity for each slice.
Research Paper
Slice-Based Learning: A Programming Model for Residual Learning
Proposing Slice-based Learning, a new programming model in which the slicing function (SF), a programmer abstraction, is used to specify additional model capacity for each slice.
Osprey: Weak Supervision of Imbalanced Extraction Problems Without Code
Proposing Osprey, a weak-supervision system suited for highly imbalanced data, built on top of the Snorkel framework.
Research Paper
Osprey: Weak Supervision of Imbalanced Extraction Problems Without Code
Proposing Osprey, a weak-supervision system suited for highly imbalanced data, built on top of the Snorkel framework.
