#IAmNYAS: Dmitry Storcheus

You likely use machine learning every day without knowing it-but what is it? Machine learning is a subfield of computer science that involves developing mathematical algorithms that discover knowledge from specific data sets, and then "learn" from the data in a way that allows predictions to be made. Applications like search engine results, illness prediction in medical diagnosis, credit card fraud detection, and stock market analysis rely on machine learning; even Netflix and Amazon use machine learning to predict other content and consumables that may be of interest to the users.

Academy Member Dmitry Storcheus is an engineer at Google Research NY, where he specializes in the research and implementation of dimensionality reduction. Learn more about what sparked Dmitry's interest in machine learning, as well as those who have inspired him along the way.

What initially drew you to the field of machine learning?

I was drawn to the field because of the remarkable power of machine learning tools to learn and forecast patterns in data. I remember an article from 2011 about scientists from Stanford who were able to use machine learning to study breast cancer with their algorithm (called C-Path) using microscopic images. They reported that the algorithm was more accurate than human doctors in predicting survival, which was amazing for me at that time. The success of machine learning combined with its mathematical rigor inspired me to conduct research in this field.

Who has been your biggest science inspiration?

There are two wonderful scientists that greatly inspire me in my research: Mehryar Mohri and Corinna Cortes. Dr. Mohri was my research advisor at the Courant Institute of Mathematical Sciences, and he taught me how to approach machine learning fundamentally and introduced me to the academic community. His deep theoretical understanding of machine learning and his strong commitment to work with students has always been a great inspiration for me. Dr. Cortes is well known for her Support Vector Machines contribution and she is the head of Google Research NY, where I currently work. The unique research model that she built and that we use, balancing theory and practice to make better products for Google users, is very inspiring to me.

How do you see machine learning evolving in the future?

Machine learning algorithms will definitely become more scalable-the amount of data that we have is growing rapidly and the tools have to be able to handle that. Also, I think that machine learning will spread into other areas beyond computer code.

What are some of the biggest challenges in machine learning right now?

The first one is regarding supervised versus unsupervised methods. While unsupervised methods have greater flexibility, the supervised ones can be fine-tuned to achieve better accuracy, so there is a tradeoff. Recently I published a paper co-authored with Mehryar Mohri and Afshin Rostamizadeh that makes a point for using supervised dimensionality reduction, since it has favorable learning guarantees. Particularly, we show that the generalization error of a hypothesis class that includes learning a linear combination of kernels that define projection jointly with a classifier has a favorable bound.

The second challenge is "Can kernel machines match deep neural networks in accuracy?" So far we have seen great progress by wonderful scientists, such as Fei Sha and Le Song, who were able to use kernel approximations to match deep neural networks in accuracy on speech datasets and provide theoretical justification of their results. This work is still in progress, and I think it will be raising widespread discussions in the next couple of years.

Why did you become a Member of the New York Academy of Sciences?

I was first introduced to the New York Academy of Sciences when I was invited to give a talk at the 9th Annual Machine Learning Symposium at the Academy. My talk was very well received by the Members of the Academy, and they awarded me with an Honorable Mention. I think that the Academy is doing a wonderful job of connecting researchers all over the world at conferences and other venues. Moreover, being its member gives me a chance to interact with the leading Machine Learning scientists not only from America, but also from the world as well.

The 11th Annual Machine Learning Symposium will be held March 3, 2017 and will feature Keynote Presentations from leading scientists in both applied and theoretical machine learning.

Read more #IAmNYAS profiles here.