Particularly focused on framework portability across devices, as well as Machine Learning optimization techniques.
I’m a pragmatic engineer in constant search for the balance between beautiful, universal solutions and impact in the real world.
Today I do this as part of the PyTorch team at Facebook
, developing a machine learning framework that powers state of the art research, and helps bring that research to products across an ever increasing number of fields.Previously, I spent 8 years at Google
also developing machine learning frameworks, like TensorFlow, that powered billions of users in Google and other companies' applications. I also participated in research applied to Google’s products, such as the Assistant, where I worked in Speech Reconition and developed technologies that power "Hey Google". Oh, and my first few months I worked on the Fonts project that I use on this site.Prior to that I spent 7 years at Appian
, where I lead a number of projects, most significantly co-authoring the SAIL (Self-Assembling Interface Layer) technology which underpins the company’s low-code platform.A suite of tools for optimizing machine learning models for deployment and execution, via easy to use and consistent APIs implementing powerful machine learning optimization techniques.
Selected work I was involved in:
Google's open source deep learning framework for on-device machine learning. It has billions of installs, from mobile phones, smart displays and speakers, to cars and wearables, powering Google's and other companies products.
Selected work I was involved in:
I worked in the Google speech team to bring speech and related technologies to work entirely on-device. I was part of the team that developed the very-low-power "hey Google" capabilities across devices, where I developed the first end-to-end system (and latest iteration of the ML model). I also built the pre-TensorFlow ML inference engine that brought a new generation of speech recognizers, text-to-speech generators, and keyboard technology on-device.
This infrastructure, along with many techniques I implemented, promoted and refined --such as quantization, sparsity, and the replacement of vanilla LSTMs for Coupled Input-Forget Gate (CIFG) variant (introduced here)-- became the foudation of newer infrastructure and production models.
US-9767410B1 ⬀, US-9372675B1 ⬀, US-9542948B2 ⬀, US-9842608B2 ⬀, US-10460735B2⬀, EP-3121809B1 ⬀, US-20200126537A1 ⬀, WO-2020092532A1 ⬀, US-9953216B2 ⬀
A more complete (and likely up to date) list at Google Scholar.
[2003 – 2005] Instituto Tecnológico y de Estudios Superiores de Monterrey
Master in Computer Science, Artificial Intelligence, Image Processing, Robotics
Summa Cum Laude
Activities and Societies: ACM Collegiate Programming Contest 🎈, RoboCup World Cup ⚽, Research assistant
[1998 – 2003] Instituto Tecnológico y de Estudios Superiores de Monterrey
Bachelor Computer Science, Artificial Intelligence
Summa Cum Laude
Activities and Societies: ACM Collegiate Programming Contest 🎈, RoboCup World Cup ⚽