Google is always looking to work with bright computer scientists, and many of them can be found at universities.
Through our Visiting Researcher program, both Google and Academia benefit as exciting ideas and research challenges are shared. In doing so, Google's world-class computing infrastructure is utilized to explore new projects at industrial scale, helping universities to be well-equipped to train the next generation of computer scientists to do long-term research.
My research aims at modeling both content and interpersonal information in natural language communication, with applications to health, education, and social media. Visiting Google Research NYC has been a great learning experience, and an opportunity to collaborate with world-leading researchers on natural language related projects that can have real world benefits.
The Google Brain team is a unique place to perform cutting edge systems research to continuously meet the demands of AI computations and enable more breakthroughs. I am excited to have contributed towards addressing this problem while working on the MLIR project since its conception.
I prefer things that have a lot more impact, and influence on people — that's actually one of the reasons why I came to Google. It's a place that right now has enormous leverage to do things.
Machine Learning is increasingly becoming one of the biggest consumers of computational resources. As a systems researcher, I wanted to gain first-hand experience with how leading-edge ML is being developed and used at the largest scales.
Google is pushing the limits of ML and systems to support it, and has amazing group of people engaged in that work. It's a fast, open, collaborative environment that makes you feel at the center of this wave of change.
My research goals are to improve the performance, energy efficiency, and reliability of computer systems. I am excited at the opportunity to work with world-leading engineers at Google on efficient warehouse-scale computing infrastructure.
At Google I'm working on explainable AI in the context of conversational recommender systems. Specifically, I'm developing recommendation approaches that can reveal to users how their preferences are generally understood, expose the reasoning behind the recommendation mechanism, and provide users with a direct and meaningful way to revise the system’s understanding of their preferences.
Vallor began working with Google Cloud in July 2018 as a consulting AI Ethicist assisting with the implementation of Cloud review processes for our AI Principles. While continuing to support these processes in Cloud, in December 2018 she expanded her work at Google as a Visiting Researcher, joining Global Affairs and serving as one of our Advanced Technology Advisors. She also has worked extensively with EngEDU to develop a series of workshops, presentations, and self-service training modules on tech ethics designed for a variety of audiences at Google.
While visiting Google NYC, I am generally interested in understanding and improving optimization for deep learning and hope to obtain interesting empirical and theoretical results that will carry over to future research.
Google Brain is an exceedingly interesting place where the frontiers of deep learning are being shaped continuously. My goal here is to engage in design of novel neural network architectures that can reason about programs, find and fix bugs in them, and ultimately synthesize programs automatically.
For someone like me with a strong interest in privacy in the electronic society, there was no better place to visit than Google, whose useful if not indispensable services have a tremendous impact on society in terms of privacy issues.
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