I am a PhD student at Princeton working on statistical machine learning
(as part of the Artificial Intelligence and Machine Learning research group),
advised by Ryan Adams,
in the Laboratory for Intelligent Probabilistic Systems.
In 2018, I completed my MSc with David Duvenaud at the
Vector Institute for Artificial Intelligence, while a student in Machine Learning group at the University of Toronto.
I completed my BSc (2016) at the University of British Columbia in Statistics and Computer Science.

I spent summer 2016 working in Mark Schmidt's Machine Learning Lab where I developed unsupervised learning algorithms for a Matlab machine learning toolbox. I spent fall 2017 working with Ferenc HuszĂˇr on improving black-box optimization methods for general non-differentiable functions. During summer 2018, while an intern at Microsoft Research Cambridge, I collaborated on a novel class of deep generative models for understanding and programming information processing in biological systems. As of summer 2019, I am an intern at Google Brain in San Francisco, working with Durk Kingma on a better understanding of representation learning in deep generative models.

More broadly, I am motivated in my research to push forward a theoretical understanding of deep learning, in support of improving robustness and reliability of deep statistical models, while exploring how new affordances in deep generative models can improve existing practices in scientific discovery and engineering design.

Research

Curriculum Vitae

Email: roeder@princeton.edu

I spent summer 2016 working in Mark Schmidt's Machine Learning Lab where I developed unsupervised learning algorithms for a Matlab machine learning toolbox. I spent fall 2017 working with Ferenc HuszĂˇr on improving black-box optimization methods for general non-differentiable functions. During summer 2018, while an intern at Microsoft Research Cambridge, I collaborated on a novel class of deep generative models for understanding and programming information processing in biological systems. As of summer 2019, I am an intern at Google Brain in San Francisco, working with Durk Kingma on a better understanding of representation learning in deep generative models.

More broadly, I am motivated in my research to push forward a theoretical understanding of deep learning, in support of improving robustness and reliability of deep statistical models, while exploring how new affordances in deep generative models can improve existing practices in scientific discovery and engineering design.

Research

Curriculum Vitae

Email: roeder@princeton.edu

Accepted for publication and short oral at ICML 2019: arXiv link; poster link

Submitted to ICLR 2018 workshop track.

Accepted as a contributed talk at the Deep Reinforcement Learning Symposium, NIPS 2017.

I gave a talk on the paper at the University of Cambridge in November, 2017

Accepted for publication at ICLR 2018

A short version of the paper was published at NIPS 2016's Advances in Approximate Bayesian Inference workshop

The full length version of the paper was published at NIPS 2017

Andrew Miller wrote a great blog post exploring the key ideas of the paper.

I merged multiple code bases from many graduate student contributors into a finished software package, and added a variety of new unsupervised learning algorithms including sparse autoencoders, Hidden Markov Models, Linear-Gaussian State Space Models, t-Distributed Stochastic Neighbour Embedding, and Convolutional Neural Networks for image classification.

Download package