Zachary Nado

I’m Zachary Nado, a Research Engineer at Google Brain in Cambridge, MA where our team works on anything and everything related to machine learning and artificial intelligence!

I graduated from Brown University in Computer Science and Applied Mathematics in 2016 where I was a part of the Serre Lab. There I worked on various systems engineering problems for the lab, lead a team of six to design a web annotation tool for labeling and viewing machine learning data, and developed my honors thesis to replace an older computer vision pipeline for classifying mouse behavior with convolutional neural networks.

During my college summers I did two internships with Google where I worked on several search infrastructure projects, followed by an internship at SpaceX as part of their software engineering team.

I was also a member of the Brown Space Engineering team for three years where I worked on our first ever satellite, a 1U cubesat that acts as an artificial shooting star that was launched to orbit in May 2018.

Authored Publications
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    Google
Plex: Towards Reliability using Pretrained Large Model Extensions
Du Phan
Mark Patrick Collier
Zi Wang
Zelda Mariet
Clara Huiyi Hu
Neil Band
Tim G. J. Rudner
Karan Singhal
Joost van Amersfoort
Andreas Christian Kirsch
Rodolphe Jenatton
Honglin Yuan
Kelly Buchanan
D. Sculley
Yarin Gal
ICML 2022 Pre-training Workshop (2022)
Adaptive Gradient Methods at the Edge of Stability
Behrooz Ghorbani
David Cardoze
Jeremy Cohen
Justin Gilmer
Naman Agarwal
Shankar Krishnan
NeuRIPS 2022 (2022) (to appear)
Underspecification Presents Challenges for Credibility in Modern Machine Learning
Dan Moldovan
Ben Adlam
Babak Alipanahi
Alex Beutel
Christina Chen
Jon Deaton
Matthew D. Hoffman
Shaobo Hou
Neil Houlsby
Ghassen Jerfel
Yian Ma
Diana Mincu
Akinori Mitani
Andrea Montanari
Christopher Nielsen
Thomas Osborne
Rajiv Raman
Kim Ramasamy
Jessica Schrouff
Martin Gamunu Seneviratne
Shannon Sequeira
Harini Suresh
Victor Veitch
Steve Yadlowsky
Xiaohua Zhai
D. Sculley
Journal of Machine Learning Research (2020)
AutoGraph: Imperative-style Coding with Graph-based Performance
Dan Moldovan
James Decker
Fei Wang
Andrew Johnson
Brian Lee
D Sculley
Tiark Rompf
Alexander B Wiltschko
SysML (2019)
Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model
Guodong Zhang
James Martens
Sushant Sachdeva
Chris Shallue
Roger Grosse
2019 Conference on Neural Information Processing Systems (2019)