{Machine, Deep} Learning.
Software {architecture, infrastructure}.
Building AI systems @ {VAI, Berkeley Lab}.

Present

I love building and automating. I spend a large portion of my time training deep learning models.

I'm a founder at Vai Technologies, where we are building software to help organizations and enterprises make sense of and leverage large volumes of text using deep learning.

I am also a visiting researcher at Lawrence Berkeley National Laboratory (LBNL) where I work on generative modeling in the natural sciences. I've published a few papers on learning aspects of high energy particle collisions using Generative Adversarial Networks. I'm also an advisor at The Hive and handful of startups.

Past

Previously, I've held positions at Enlitic, SLAC National Accelerator Laboratory, and the European Organization for Nuclear Research (CERN), where I was part of the ATLAS collaboration. Further back, I was a graduate student at the Stanford Institute for Computational and Mathematical Engineering (ICME), where I worked on character-level language models and recurrent convolutional architectures for text understanding. I earned my undergraduate degree in Applied Mathematics at Yale, where I worked on manifold learning and mathematical contract theory.

Organizational services

We help you determine the organization infrastructure necessary to build your products.

SERVICES one

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SERVICES two

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Interests.

A subset of things I like to build, tinker with, and research

Deep Learning

I design novel deep architectures, particularly for NLP and sometime CV. I'm interested in end-to-end learning from characters. I also like thinking about cloud deployment strategies for DL. I like Keras, but sometimes I pretend I understand TF.

Recommender Systems

I spend a lot of time thinking about how to make recommender systems better. I've been experimenting with some end-to-end approaches, but I mostly spend time automating subsets of the collaborative + content driven pipeline

Infrastructure

Machine learning is great in a vacuum, but it looses real-world oomf when your brilliant models are confined to an Jupyter Notebook. I use Docker + Vagrant for my deployments.

Predictive Analytics

More generically, we extract insights from data that you've collected. Even if you don't have a specific goal in mind, we can help you find insights you never knew were there.

Contact

I'm on Twitter @lukede0
I write code on GitHub at /lukedeo
I'm on LinkedIn at /in/lukedeo
You can email me at lukedeo@ldo.io

...or, you can send me a message here

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