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
Sort By
Google
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
D. Sculley
Rajat Monga
SysML, Palo Alto, CA, USA (2019)
TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks
Cassandra Xia
Clemens Mewald
D. Sculley
George Roumpos
Illia Polosukhin
Jamie Alexander Smith
Jianwei Xie
Lichan Hong
Mustafa Ispir
Philip Daniel Tucker
Yuan Tang
Zakaria Haque
Proceedings of the 23th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, Canada (2017)
