Avner May

Avner May 

Research Scientist at Google NYC

Contact Information

Email: avnermay [at] cs (dot) stanford (dot) edu

About me

I am a research scientist in the speech recognition group in Google's NYC office. Prior to joining Google, I was a postdoctoral scholar working in Prof. Chris Ré's group at Stanford University. I completed my PhD in Computer Science at Columbia University in December 2017, advised by Prof. Michael Collins. Prior to my PhD, I worked for two years as a software development engineer at Microsoft, living in Seattle, WA. I graduated in 2009 from Harvard College, where I majored in Mathematics, with a minor in Computer Science. I am originally from Potomac, MD.

Research Interests

My research interests center around designing simpler, better understood, and more efficient, machine learning models. For example, during my PhD, I showed that kernel approximation methods can perform comparably to fully-connected deep neural networks on the challenging non-linear classification problems in speech recognition systems. More recently, I have worked on better understanding what makes an approximate feature representation perform well on downstream tasks, both in the context of kernel approximation methods and word embedding compression. This understanding is important for efficiently selecting among existing feature approximations or designing new ones, and for navigating the trade-offs between computation, memory, and downstream performance.

Prior to working on machine learning, I did two years of research in social network analysis, advised by Prof. Augustin Chaintreau; I studied whether social networks like Facebook or Twitter are efficient systems for delivering content of interest to their users (2011-2013).

Other Interests

I love most things that involve being active and outdoors — running, biking, snowboarding, hiking, camping, and basically anything in the mountains. During the summer of 2017 I spent 2.5 months on the Pacific Crest Trail. I am very interested in food systems and nutrition, and how they affect our health, the environment, and the well-being of animals.


* Equal contribution.


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