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Eugene Brevdo
Biography
Eugene Brevdo is a Staff SWE at Google DeepMind. His research interests span many interconnected areas:
- Optimization under uncertainty/constraints and experimental design for e.g., software systems and high throughput screening in biology.
- Software systems for training and deploying ML, Bandits, and RL models.
- Machine Learning applied to optimizing large software systems (databases, datacenter scheduling, caches, compilers like LLVM and XLA/TPU).
Eugene received his PhD in Electrical Engineering from Princeton University, where his advisers were Peter Ramadge and Ingrid Daubechies.
Education
-
PhD in Electrical Engineering, 2011
Princeton University
-
BSc in Electrical, Computer, and Systems Engineering, 2005
Rensselaer Polytechnic Institute
Experience
Staff Software Engineer
Google Brain + Google DeepMind
Jan 2022 –
Present
California
Brain Sequin (now GDM Alchemy) team.
- Focusing on protein understanding and optimization under uncertainty.
- Multi-stage peptide library design, co-optimizing cell permeability and protein binding.
- LLM models for protein function annotation and target-conditional optimization.
Staff Software Engineer
Google Brain
Apr 2017 –
Dec 2021
California
Co-TLM of the TF-Agents team (2018 - 2021).
TLM of the Brain Learned Systems Team (2017 - 2022). Clients include Spanner, Compiler, and Cloud infrastructure teams.
- Built smarter query optimizers, cache eviction algorithms, inlining and register allocation passes.
- Grew the Learned Systems team from 1 to 7 researchers and engineers.
- Aligned engagements between Brain, Technical Infrastructure, and Cloud orgs.
- Set research direction for systems and ML engineers.
Senior Software Engineer
Google Brain
Oct 2015 –
Mar 2017
California
SWE on Brain Applied Machine Intelligence team.
- Core TensorFlow maintainer.
- Developed interfaces and support for sparse and sequential input, debugged graph control flow, implemented CPU and GPU kernels; whatever needed doing.
- Founding SWE / API designer of TF Distributions (now Tensorflow Probability).
Software Engineer
Google Research
Apr 2014 –
Sep 2015
California
Hacked on DistBelief, helped opensource TensorFlow.
Software Engineer
Lifecode, Inc.
Mar 2013 –
Mar 2014
California
Built supervised learning ML pipelines for clinical diagnosis of rare diseases from NGS assays.
Senior Data Scientist
The Climate Corporation
Mar 2013 –
Mar 2014
California
I worked on two teams:
- Computational Climatology: Statistical weather forecasting in the short-to-medium-term scale (2 weeks-2 years) using a combination of techniques from climatology, machine learning/statistics, and spatiotemporal signal processing.
- Computational Agronomy: Analyzed, assimilated, and reconciled remotely sensed weather and agricultural data. Built growth forecasts for corn, sorghum, soy, and winter wheat.
Research Intern
Siemens Corporate Research
May 2008 –
Aug 2008
Princeton, NJ
Focused on applications of Compressive Sensing to inverse problems in medical imaging.
- Developed CS-based estimator for Computational Tomography with Sinogram Occlusion.
- Developed a novel CS-based reconstruction technique for Ultrasound tomography.
Featured Publications
Zhenyu Song,
Kevin Chen,
Nuikhil Sarda,
Deniz Altinbüken,
Eugene Brevdo,
Jimmy Coleman,
Xiao Ju,
Pawel Jurczyk,
Richard Schooler,
Ramki Gummadi
(2023).
HALP: Heuristic Aided Learned Preference Eviction Policy for YouTube Content Delivery Network.
20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023, Boston, MA, April 17-19, 2023.
Lyric Doshi,
Vincent Zhuang,
Gaurav Jain,
Ryan Marcus,
Haoyu Huang,
Deniz Altinbüken,
Eugene Brevdo,
Campbell Fraser
(2023).
Kepler: Robust Learning for Parametric Query Optimization.
Proc. ACM Manag. Data.
S. VenkataKeerthy,
Siddharth Jain,
Umesh Kalvakuntla,
Pranav Sai Gorantla,
Rajiv Shailesh Chitale,
Eugene Brevdo,
Albert Cohen,
Mircea Trofin,
Ramakrishna Upadrasta
(2023).
The Next 700 ML-Enabled Compiler Optimizations.
CoRR.
Xinfeng Xie,
Prakash Prabhu,
Ulysse Beaugnon,
Phitchaya Mangpo Phothilimthana,
Sudip Roy,
Azalia Mirhoseini,
Eugene Brevdo,
James Laudon,
Yanqi Zhou
(2022).
A Transferable Approach for Partitioning Machine Learning Models on Multi-Chip-Modules.
Proceedings of Machine Learning and Systems 2022, MLSys 2022, Santa Clara, CA, USA, August 29 - September 1, 2022.
Yingjie Miao,
Xingyou Song,
John D. Co-Reyes,
Daiyi Peng,
Summer Yue,
Eugene Brevdo,
Aleksandra Faust
(2022).
Differentiable Architecture Search for Reinforcement Learning.
International Conference on Automated Machine Learning, AutoML 2022, 25-27 July 2022, Johns Hopkins University, Baltimore, MD, USA.
Andreea Gane,
Maxwell L Bileschi,
David Dohan,
Elena Speretta,
Amélie Héliou,
Laetitia Meng-Papaxanthos,
Hermann Zellner,
Eugene Brevdo,
Ankur Parikh,
Maria J Martin,
Sandra Orchard,
Lucy J Colwell
(2022).
ProtNLM: Model-based Natural Language Protein Annotation.
Mircea Trofin,
Yundi Qian,
Eugene Brevdo,
Zinan Lin,
Krzysztof Choromanski,
David Li
(2021).
MLGO: a Machine Learning Guided Compiler Optimizations Framework.
CoRR.
Albin Cassirer,
Gabriel Barth-Maron,
Eugene Brevdo,
Sabela Ramos,
Toby Boyd,
Thibault Sottiaux,
Manuel Kroiss
(2021).
Reverb: A Framework For Experience Replay.
CoRR.
Yuan Yu,
Martín Abadi,
Paul Barham,
Eugene Brevdo,
Mike Burrows,
Andy Davis,
Jeff Dean,
Sanjay Ghemawat,
Tim Harley,
Peter Hawkins,
Michael Isard,
Manjunath Kudlur,
Rajat Monga,
Derek Gordon Murray,
Xiaoqiang Zheng
(2018).
Dynamic control flow in large-scale machine learning.
Proceedings of the Thirteenth EuroSys Conference, EuroSys 2018, Porto, Portugal, April 23-26, 2018.
Ashish Vaswani,
Samy Bengio,
Eugene Brevdo,
François Chollet,
Aidan N. Gomez,
Stephan Gouws,
Llion Jones,
Lukasz Kaiser,
Nal Kalchbrenner,
Niki Parmar,
Ryan Sepassi,
Noam Shazeer,
Jakob Uszkoreit
(2018).
Tensor2Tensor for Neural Machine Translation.
Proceedings of the 13th Conference of the Association for Machine Translation in the Americas, AMTA 2018, Boston, MA, USA, March 17-21, 2018 - Volume 1: Research Papers.
Dustin Tran,
Matthew D. Hoffman,
Rif A. Saurous,
Eugene Brevdo,
Kevin Murphy,
David M. Blei
(2017).
Deep Probabilistic Programming.
5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings.
Joshua V. Dillon,
Ian Langmore,
Dustin Tran,
Eugene Brevdo,
Srinivas Vasudevan,
Dave Moore,
Brian Patton,
Alex Alemi,
Matthew D. Hoffman,
Rif A. Saurous
(2017).
TensorFlow Distributions.
CoRR.
Martín Abadi,
Ashish Agarwal,
Paul Barham,
Eugene Brevdo,
Zhifeng Chen,
Craig Citro,
Gregory S. Corrado,
Andy Davis,
Jeffrey Dean,
Matthieu Devin,
Sanjay Ghemawat,
Ian J. Goodfellow,
Andrew Harp,
Geoffrey Irving,
Michael Isard,
Yangqing Jia,
Rafal Józefowicz,
Lukasz Kaiser,
Manjunath Kudlur,
Josh Levenberg,
Dan Mané,
Rajat Monga,
Sherry Moore,
Derek Gordon Murray,
Chris Olah,
Mike Schuster,
Jonathon Shlens,
Benoit Steiner,
Ilya Sutskever,
Kunal Talwar,
Paul A. Tucker,
Vincent Vanhoucke,
Vijay Vasudevan,
Fernanda B. Viégas,
Oriol Vinyals,
Pete Warden,
Martin Wattenberg,
Martin Wicke,
Yuan Yu,
Xiaoqiang Zheng
(2016).
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems.
CoRR.
Gaurav Thakur,
Eugene Brevdo,
Neven S. Fuckar,
Hau-Tieng Wu
(2013).
The Synchrosqueezing algorithm for time-varying spectral analysis: Robustness properties and new paleoclimate applications.
Signal Process..
Sina Jafarpour,
Gungor Polatkan,
Eugene Brevdo,
Shannon M. Hughes,
Andrei Brasoveanu,
Ingrid Daubechies
(2009).
Stylistic analysis of paintings using wavelets and machine learning.
17th European Signal Processing Conference, EUSIPCO 2009, Glasgow, Scotland, UK, August 24-28, 2009.
Ella Hendriks,
Igor J. Berezhnoy,
Eugene Brevdo,
Shannon M. Hughes,
Ingrid Daubechies,
Jia Li,
Eric O. Postma,
James Z. Wang
(2008).
Image processing for artist identification.
IEEE Signal Process. Mag..