Eugene Brevdo

Eugene Brevdo

Staff Software Engineer

Google Research

Biography

Eugene Brevdo is the Tech Lead/Manager of the Learned Systems group at Google Brain. His research interests span several interconnected areas:

  • Software systems for training and deploying ML, Bandits, and RL models.
  • Uncertainty, evolutionary strategies, and population-based training.
  • 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

 
 
 
 
 
Google Brain
Staff Software Engineer
Google Brain
Apr 2017 – Present California

Co-TLM of the TF-Agents team (2018 - 2021).

TLM of the Brain Learned Systems Team (2017 - now). 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.
 
 
 
 
 
Google Brain
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).
 
 
 
 
 
Google Research
Software Engineer
Google Research
Apr 2014 – Sep 2015 California
Hacked on DistBelief, helped opensource TensorFlow.
 
 
 
 
 
Lifecode, Inc.
Software Engineer
Lifecode, Inc.
Mar 2013 – Mar 2014 California
Built supervised learning ML pipelines for clinical diagnosis of rare diseases from NGS assays.
 
 
 
 
 
The Climate Corporation
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.

Projects

MATLAB Synchrosqueezing Toolbox

This toolbox implements several time-frequency and time-scale analysis methods, including the Forward/Inverse Discretized CWT, and CWT-based Synchrosqueezing.

MLGO

MLGO is a framework for integrating ML techniques systematically in LLVM. It replaces human-crafted optimization heuristics in LLVM with machine learned models.

Reverb

Reverb is an efficient and easy-to-use data storage and transport system designed for machine learning research. Reverb is primarily used as an experience replay system for distributed reinforcement learning algorithms but the system also supports multiple data structure representations such as FIFO, LIFO, and priority queues.

TensorFlow

TensorFlow is an end-to-end open source platform for machine learning.

TensorFlow Probability

A library for probabilistic reasoning and statistical analysis.

TF Agents

A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.

Featured Publications

(2021). MLGO: a Machine Learning Guided Compiler Optimizations Framework. CoRR.

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(2021). Reverb: A Framework For Experience Replay. CoRR.

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(2018). Dynamic Control Flow in Large-Scale Machine Learning. CoRR.

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(2018). Dynamic control flow in large-scale machine learning. Proceedings of the Thirteenth EuroSys Conference, EuroSys 2018, Porto, Portugal, April 23-26, 2018.

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(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.

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(2017). Deep Probabilistic Programming. 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings.

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(2017). TensorFlow Distributions. CoRR.

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(2016). TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. CoRR.

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(2009). Stylistic analysis of paintings using wavelets and machine learning. 17th European Signal Processing Conference, EUSIPCO 2009, Glasgow, Scotland, UK, August 24-28, 2009.

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(2008). Image processing for artist identification. IEEE Signal Process. Mag..

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