Marc Berndl
Marc Berndl has been at Google since 2005, has a Master’s Degree in Computer Science from McGill University, and is Engineering Lead for Google Accelerated Science. Marc spent eight years in Ads working on auction theory, data analysis as well as experimental design. Within GAS, Marc has established ongoing research efforts in material science, biochemistry, cell biology, and drug screening.
His current research includes predictive model semantics, solar thermal energy optimization, aptamer design, and methods of detection, localization and quantification of cellular proteins.
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Google
Longitudinal fundus imaging and its genome-wide association analysis provides evidence for a human retinal aging clock
Sara Ahadi
Kenneth A Wilson Jr,
Drew Bryant
Orion Pritchard
Ajay Kumar
Enrique M Carrera
Ricardo Lamy
Jay M Stewart
Avinash Varadarajan
Pankaj Kapahi
Ali Bashir
eLife (2023)
ProtSeq: towards high-throughput, single-molecule protein sequencing via amino acid conversion into DNA barcodes
Jessica Hong
Michael Connor Gibbons
Ali Bashir
Diana Wu
Shirley Shao
Zachary Cutts
Mariya Chavarha
Ye Chen
Lauren Schiff
Mikelle Foster
Victoria Church
Llyke Ching
Sara Ahadi
Anna Hieu-Thao Le
Alexander Tran
Michelle Therese Dimon
Phillip Jess
iScience, 25 (2022), pp. 32
Machine learning guided aptamer discovery
Ali Bashir
Geoff Davis
Michelle Therese Dimon
Qin Yang
Scott Ferguson
Zan Armstrong
Nature Communications (2021)
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)
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
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)
