Conducts research into automatic means of extracting useful information from sound. Speech, to extract the words, prosodics, speaker characteristics, etc.

Paper about my Galaxy Challenge solution. Classifying plankton with deep neural networks. The National Data Science Bowl. A data science competition where the goal was to classify images of plankton, has just ended. I participated with six other members of my research lab, the Reservoir lab.

I develop machine learning tools to analyze music and multimedia data. This includes recommender systems, image and audio analysis, similarity learning, cross-modal feature integration, and automatic annotation. As of Fall, 2014, I am a data science fellow at the Center for Data Science. And Music and Audio Research Lab. Previously, I was a postdoctoral research scholar in the Center for Jazz Studies. In the Computer Audition Lab.

My research focuses on machine learning techniques for sequential data. I am currently a resident at Google Brain. I recently completed a PhD in Electrical Engineering at Columbia University In LabROSA. Focused on learning-based methods for comparing sequences. The main product of my doctoral work is the Lakh MIDI Dataset. I did my undergrad at Oberlin College, where I majored in Mathematics. Colin Raffel and Daniel P.

Lasagne is a lightweight library to build and train neural networks in Theano. Lasagne is a work in progress, input is welcome. The available documentation is limited for now. The project is on GitHub. The Lasagne user guide explains how to install Lasagne, how to build and train neural networks using Lasagne, and how to contribute to the library as a developer. Where to go from here. A layer that changes the shape.