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David Soergel

David Soergel

I'm currently working on making machine learning accessible to a wide audience through clean and usable TensorFlow APIs. I'm particularly interested in easing burdens associated with data management, data preprocessing, and provenance tracking--which in practice can often require more user code and engineer time than the machine learning algorithms themselves. Previously: Ph.D. in Biophysics (Berkeley 2010); founder of OpenReview.net; scientific software quality cynic; etc.
Authored Publications
Google Publications
Other Publications
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    TensorFlow.js: Machine Learning for the Web and Beyond
    Daniel Smilkov
    Nikhil Thorat
    Yannick Assogba
    Ann Yuan
    Nick Kreeger
    Ping Yu
    Kangyi Zhang
    Eric Nielsen
    Stan Bileschi
    Charles Nicholson
    Sandeep N. Gupta
    Sarah Sirajuddin
    Rajat Monga
    SysML, Palo Alto, CA, USA (2019)
    Preview abstract TensorFlow.js is a library for building and executing machine learning algorithms in JavaScript. TensorFlow.js models run in a web browser and in the Node.js environment. The library is part of the TensorFlow ecosystem, providing a set of APIs that are compatible with those in Python, allowing models to be ported between the Python and JavaScript ecosystems. TensorFlow.js has empowered a new set of developers from the extensive JavaScript community to build and deploy machine learning models and enabled new classes of on-device computation. This paper describes the design, API, and implementation of TensorFlow.js, and highlights some of the impactful use cases. View details
    TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks
    Cassandra Xia
    Clemens Mewald
    George Roumpos
    Illia Polosukhin
    Jamie Alexander Smith
    Jianwei Xie
    Lichan Hong
    Mustafa Ispir
    Philip Daniel Tucker
    Yuan Tang
    Proceedings of the 23th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, Canada (2017)
    Preview abstract We present a framework for specifying, training, evaluating, and deploying machine learning models. Our focus is to simplify writing cutting edge machine learning models in a way that enables bringing those models into production. Recognizing the fast evolution of the field of deep learning, we make no attempt to capture the design space of all possible model architectures in a DSL or similar configuration. We allow users to write code to define their models, but provide abstractions that guide developers to write models in ways conducive to productionization, as well as providing a unifying Estimator interface, a unified interface making it possible to write downstream infrastructure (distributed training, hyperparameter tuning, …) independent of the model implementation. We balance the competing demands for flexibility and simplicity by offering APIs at different levels of abstraction, making common model architectures available “out of the box”, while providing a library of utilities designed to speed up experimentation with model architectures. To make out of the box models flexible and usable across a wide range of problems, these canned Estimators are parameterized not only over traditional hyperparameters, but also using feature columns, a declarative specification describing how to interpret input data. We discuss our experience in using this framework in research and production environments, and show the impact on code health, maintainability, and development speed. View details
    Rampant software errors may undermine scientific results
    F1000 Research, vol. 3 (2015), pp. 303
    Open Scholarship and Peer Review: a Time for Experimentation
    Adam Saunders
    Andrew McCallum
    ICML 2013 Workshop on Peer Reviewing and Publishing Models (2013)
    Selection of primers for optimal taxonomic classification of environmental 16S rRNA gene sequences
    Neelendu Dey
    Rob Knight
    Steven E Brenner
    The ISME Journal, vol. 6 (2012), pp. 1440-1444