Braden Hancock
AI Research Leader | Co-founder @ Snorkel AI
I am an AI research leader who specializes in taking innovative research all the way to production. In my 15 years of AI research experience, I have had the opportunity to publish research at Stanford, Google, Facebook, MIT, Johns Hopkins, AFRL, and BYU, and to co-found and lead Snorkel AI, an AI platform company with $135M raised and a $1B valuation.
Research interests:
generative AI, LLMs, machine learning systems, weak supervision, multi-task learning (MTL), transfer learning, training data creation, learning from natural language, information extraction, natural language processing (NLP)


  • Nov 2023: Snorkel AI included on the 2023 Fortune 50 AI Innovators list.
  • July 2023: Snorkel AI included on the 2023 LinkedIn 50 Top Startups list.
  • July 2023: Snorkel AI called out by Sundar Pichai in the Q2 Google earnings call as a partner for Generative AI.
  • July 2023: Snorkel AI included on the 2023 NatSec100 list for top venture-funded defense and dual-use startups."
  • May 2023: Delivered closing keynote at TiEcon 2023 on "GPT-You: The Next Phase of Foundation Models."
  • Apr. 2023: Snorkel AI included on the 2023 Forbes AI 50 list recognizing the most promising private companies in AI.
  • Mar. 2023: Snorkel AI included on the 2023 Forbes America's Best Startup Employers list.
  • Mar. 2023: Participated in NVIDIA GTC panel with Jonathan Cohen (NVIDIA VP), Percy Liang (Stanford prof), Ori Goshen (AI21 founder) and Yejin Choi (UW prof).
  • Jun. 2022: Snorkel AI named a 2022 Gartner Cool Vendor in AI Core Technologies.
  • May 2022: Participated in an O'Reilly AI Superstream on NLP in Production.
  • Apr. 2022: Presented keynote at "Trustworthy AI: a Practical Roadmap for Government" event.
  • Apr. 2022: Presented at the Investing in Ethical AI event by BGV and the Ethical AI Governance Group.
  • Mar. 2022: Interview with Jack Cassel at NASDAQ about the state of enterprise AI.
  • Mar. 2022: Snorkel AI included for the second year on the Enterprise Tech 30 list—"the best enterprise tech startups"—according to top VCs.
  • Mar. 2022: Snorkel AI included on the inaugural Data 50 list—"the world's top data startups"—by Andreessen Zorowitz (a16z).
  • Feb. 2022: Published a blog post on Making Automated Data Labeling a Reality in Modern AI.
  • Feb. 2022: Presented on the power of programmatic labeling at the Enterprise AI summit.
  • Nov. 2021: Hosted Snorkel Science Talk with DeepMind researcher Abi See on AI facts and myths.
  • Aug. 2021: Snorkel AI announced $85M Series C funding at $1B valuation (Fortune, VentureBeat, SV Business Journal, etc.).
  • Jun. 2021: Interview on how Snorkel makes AI app development look more like software development on The Pulse of AI podcast.
  • May 2021: Forbes listed Snorkel's programmatic labeling approach as one of the top game-changing technologies of the past few years.
  • Apr. 2021: Snorkel AI announced the release of Application Studio (TechCrunch, VentureBeat).
  • Apr. 2021: Hosted Snorkel Science Talk with Explosion AI co-founder and CEO Ines Montani on industrial-strength NLP.
  • Mar. 2021: Hosted Snorkel Science Talk with Google research scientist and blogger Sebastian Ruder on measuring NLP progress.
  • Feb. 2021: Snorkel AI was featured on the Enterprise Tech 30 list and
  • Feb. 2021: Hosted Snorkel Science Talk with Hugging Face co-founder and CSO Thomas Wolf on productionizing ML research.
  • Dec. 2020: Interview about Snorkel Flow on the Practical AI podcast.
  • Dec. 2020: Invited talk on making ML practical with Snorkel at Open Core Summit 2020.
  • Jul. 2020: Snorkel AI came out of stealth and announced Series A funding, featured by Forbes, Greylock, GV, and others!


Snorkel AI
Co-founders: Alex Ratner, Chris Ré, Paroma Varma, Henry Ehrenberg
Topics: Building Snorkel Flow, the AI data development platform for the enterprise
Stanford University
Groups: Stanford Hazy Research, Stanford DAWN, Stanford NLP Group, Stanford StatsML
Advisor: Chris Ré, Dissertation: slides, paper
Topics: ML systems, LLMs, NLP, weak supervision, multi-task learning (MTL), training data curation
Fall 2018
Groups: Facebook AI Research (FAIR) - Paris
Mentors: Jason Weston, Antoine Bordes
Topics: Generative AI, improving chatbots with continual learning from interactions with users post-deployment
Summer 2017
Groups: Google Brain, Google Search - Mountain View
Mentors: Hongrae Lee, Cong Yu, Quoc Le
Topics: Reducing hallucinations in generative AI search results for semi-structured web content
MIT Lincoln Laboratory
Summers 2014-2015
Group: Computing & Analytics - Boston
Mentors: Vijay Gadepally, Jeremy Kepner
Topics: Recommender systems for DoD applications and computation on encrypted data
Johns Hopkins University
Summer 2013
Group: Human Language Technology Center of Excellence - Baltimore
Mentors: Mark Dredze, Glen Coppersmith
Topics: Natural language processing (NLP), machine learning, social media mining

Brigham Young University
Group: Design Exploration Research Group
Mentor: Chris Mattson
Topics: Multi-objective optimization, design space exploration
Air Force Research Laboratory
Summer 2011
Group: Turbine Engine Division
Mentor: John Clark
Topics: Evolutionary algorithms for optimization, turbine engine simulation

Past Projects

Snorkel AI
Building Snorkel Flow, the AI data development platform for the enterprise.
For more details on the company, visit
For more details on my role at the company, visit my LinkedIn profile.
Snorkel: Research Library
Snorkel is a system for rapidly creating, modeling, and managing training data. It is the flagship implementation of the data programming paradigm for supporting weak supervision resources. Collaborators and active users include over a forty major technical and medical organizations (e.g., Google, Microsoft, Intel, Toshiba, JPL, Alibaba, Stanford Medicine, etc.).
VLDB 2018 (oral)
"Best of VLDB"
Snorkel MeTaL: Weak Supervision for Multi-Task Learning
We extend Snorkel to multi-tasking supervision and multi-task learning (MTL). In particular, we are interested in the massive multi-task learning regime where we have large numbers of tasks and labels of varying types, granularities, and label accuracies. Using Snorkel MeTaL, we achieved new state-of-the-art scores on the GLUE Benchmark and four of its component tasks.
AAAI 2019 (oral), DEEM (SIGMOD) 2018 (oral)
Software 2.0: Learning-Centric Software Stacks
Driven by accuracy improvements and deployment advantages, many organizations have begun to shift toward learning-centered software stacks. That is, "Software 1.0" code with explicit instructions written by programmers is being replaced by "Software 2.0" code that is written in the weights of neural networks. In this paradigm, training data becomes the primary interface through which developers interact with their Software 2.0 systems. This requires a new level of scalability, control, and efficiency when it comes to generating and shaping training sets. We are exploring the ramifications of this new programming model and building the tools to support it.
CIDR 2019 (oral)
Self-Feeding Chatbots
Most of the conversations a chatbot sees over its lifetime happen after it's deployed. These conversations aren't typically useable as training data, but give the chatbot the right tools and it can learn from those too! We introduced a multi-task "self-feeding" chatbot that knows how to extract new training examples from the conversations it participates in to improve itself further after it's deployed.
ACL 2019
Generating Titles for Web Tables
We introduce a framework for generating titles for tables that are displayed out of their original context. We use a pointer-generator network, a sequence-to-sequence model that is capable of both generating tokens and copying tokens from the input (such as rare and out-of-vocab words), resulting in titles that are both relevant and readable and reducing hallucinations.
The Web Conference (WWW) 2019
Babble Labble: Learning from Natural Language Explanations
We explore collecting natural language explanations for why annotators give the labels they do and parsing these into executable functions, which can then be used to generate noisy labels for large amounts of unlabeled data. The resulting probabalistically labeled training dataset can then be used to train a powerful downstream discriminative model for the task at hand. We find that utilizing these natural language explanations allows real-world users to train classifiers with comparable F1 scores up to 100 times faster than when they provide just labels.
ACL 2018 (oral), NeurIPS 2017 Demo
Fonduer: Knowledge Base Construction from Richly Formatted Data
We introduce an information extraction framework that utilizes multiple representations of the data (structural, tabular, visual, and textual) to achieve state-of-the-art performance in four real-world extraction taks. Our framework is currently in use commercially at Alibaba and with law enforcement agencies fighting online human trafficking.
SIGMOD 2018 (oral)
A Machine-Compiled Database of Genome-Wide Association Studies (Nature Comms)
Using the multi-modal parsing and extraction tools from Fonduer and learning and inference tools from Snorkel, we construct a knowledge base of genotype/phenotype associations extracted from the text and tables in ~600 open-access papers from PubMed Central. Our system expands existing manually curated databases by approximately 20% with 92% precision.
Bio-Ontologies 2017, NeurIPS 2017 MLCB Workshop
Collective Supervision of Topic Models for Predicting Surveys with Social Media
We use topic models to correlate social media messages with survey outcomes and to provide an interpretable representation of the data. Rather than rely on fully unsupervised topic models, we use existing aggregated survey data to inform the inferred topics, a class of topic model supervision referred to as collective supervision.
AAAI 2016
Recommender Systems for the Department of Defense and Intelligence Community
With an internal committee of 20 MIT and DoD researchers, I spearheaded the construction of this report, which formalizes the components and complexities of recommender systems and surveys their existing and potential uses in the Department of Defense and U.S. Intelligence community.
MITLL Journal 2016

QALF: Information Extraction for the Long Tail via Question Answering
We use a Question Answering (QA) model as a flexible means of converting domain expertise expressed as natural language into weak supervision resources (labeling functions, or LFs). Preliminary results suggest that with as few as a dozen user inputs (domain-relevant questions), we can quickly build first-order extractors for new relations that lack distant supervision resources.
L-dominance: An approximate-domination mechanism for adaptive resolution of Pareto frontiers
We propose a mechanism called L-dominance (based on the Lamé curve) which promotes adaptive resolution of solutions on the Pareto frontier for evolutionary multi-objective optimization algorithms.
SMO Journal, AIAA ASM 2015, Honors Thesis
Best Student Paper
Reducing Shock Interactions in a High Pressure Turbine via 3D Aerodynamic Shaping
We show that the shock wave reflections inside a turbine engine can be approximated by calculating the 3D surface normal projections of the airfoils. Using a genetic algorithm, we produce superior airfoil geometries (with respect to high cycle fatigue failure) four orders of magnitude faster than the traditional CFD-based approach.
AIAA Journal, AIAA ASM 2014
Best Student Paper
The Smart Normal Constraint Method for Directly Generating a Smart Pareto Set
We introduce the Smart Normal Constraint (SNC) method, the first method capable of directly generating a smart Pareto set (a Pareto set in which the density of solutions varies such that regions of significant tradeoff have the greatest resolution). This is accomplished by iteratively updating an approximation of the design space geometry, which is used to guide subsequent searches in the design space.
SMO Journal, AIAA MDO 2013
Usage Scenarios for Design Space Exploration with a Dynamic Multiobjective Optimization Formulation
We investigate three usage scenarios for formulation space exploration, building on previous work that introduced a new way to formulate multi-objective problems, allowing a designer to change up update design objectives, constraints, and variables in a fluid manner that promotes exploration.
RiED Journal, ASME DETC 2012
Best Paper


Stanford University
Ph.D. Computer Science (Jun. 2019)
Advisor: Chris Ré
Machine Learning Emphasis (GPA 4.00)
Brigham Young University
B.S. Mechanical Engineering, Mathematics Minor (Apr. 2015)
Advisor: Chris Mattson
Valedictorian, summa cum laude (GPA 4.00)


National Science Foundation Graduate Research Fellowship (NSF GRF)
National Defense Science and Engineering Graduate Fellowship (NDSEG)
2015 (declined for incompatibility with NSF)
Phi Kappa Phi Marcus L. Urann Fellowship
2015 (1 of 6 in USA)
Stanford School of Engineering Finch Family Fellowship
AIAA Vicki and George Muellner Scholarship
2014 (1 of 1 in USA)

Barry M. Goldwater Scholarship
AIAA Orville and Wilbur Wright Scholarship
2013 (1 of 3 in USA)
ASME Kenneth Andrew Roe Scholarship
2012 (1 of 1 in USA)
National Merit Scholarship
BYU Thomas S. Monson Presidential Scholarship
2011 (1 of 50)