Samuel J. Yang
Samuel J. Yang is a research scientist on the Google Accelerated Science team. Prior to that, he got his Ph.D. from Stanford University, working on computational imaging & display and computational microscopy for systems neuroscience applications.
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Google
Speech Intelligibility Classifiers from 550k Disordered Speech Samples
Katie Seaver
Richard Cave
Neil Zeghidour
Rus Heywood
Jordan Green
ICASSP, Icassp submission. 2022 (2023)
Discovery of complex oxides via automated experiments and data science
Joel A Haber
Zan Armstrong
Kevin Kan
Lan Zhou
Matthias H Richter
Christopher Roat
Nicholas Wagner
Patrick Francis Riley
John M Gregoire
Proceedings of the Natural Academy of Sciences (2021)
Applying Deep Neural Network Analysis to High-Content Image-Based Assays
Scott L. Lipnick
Nina R. Makhortova
Minjie Fan
Zan Armstrong
Thorsten M. Schlaeger
Liyong Deng
Wendy K. Chung
Liadan O'Callaghan
Anton Geraschenko
Dosh Whye
Jon Hazard
Arunachalam Narayanaswamy
D. Michael Ando
Lee L. Rubin
SLAS DISCOVERY: Advancing Life Sciences R\&D, 0 (2019), pp. 2472555219857715
It's easy to fool yourself: Case studies on identifying bias and confounding in bio-medical datasets
Arunachalam Narayanaswamy
Anton Geraschenko
Scott Lipnick
Nina Makhortova
James Hawrot
Christine Marques
Joao Pereira
Lee Rubin
Brian Wainger,
NeurIPS LMRL workshop 2019 (2019)
In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images
Eric Christiansen
Mike Ando
Ashkan Javaherian
Gaia Skibinski
Scott Lipnick
Elliot Mount
Alison O'Neil
Kevan Shah
Alicia K. Lee
Piyush Goyal
Liam Fedus
Andre Esteva
Lee Rubin
Steven Finkbeiner
Cell (2018)
Assessing microscope image focus quality with deep learning
D. Michael Ando
Mariya Barch
Arunachalam Narayanaswamy
Eric Christiansen
Chris Roat
Jane Hung
Curtis T. Rueden
Asim Shankar
Steven Finkbeiner
BMC Bioinformatics, 19 (2018), pp. 77
