David Belanger
I am a research scientist in the Cambridge, MA branch of the Google Brain Team. I recently received a PhD from UMass Amherst, where I was advised by Andrew McCallum. Before grad school, I worked on optical character recognition at BBN Technologies and before that I attended Harvard, where I researched numerical methods for simulating earthquake ruptures on rough faults. During grad school, I also interned with Sham Kakade and Dilip Krishnan. You can find links to all of my pre-Google papers david-belanger.net.
My grad school research spanned graphical models, structured prediction, and deep learning. I have applied these methods to both natural language processing and computer vision tasks. Broadly speaking, I’m interested in developing accurate machine learning methods that leverage practitioners’ expertise about the problem domain, can be fit reliably using limited data, provide fair and un-biased behavior, appropriately quantify their uncertainty, offer interpretable predictions, and can be run using limited power on widely-accessible hardware. This requires both fundamental progress in machine learning methods and also close collaboration with a variety of domain experts. Fortunately, Google provides great opportunities for both.
In my free time, I enjoy running, rock climbing, cycling, grilling, traveling, and spending time with my family.
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Google
Rethinking Attention with Performers
Valerii Likhosherstov
David Martin Dohan
Peter Hawkins
Jared Quincy Davis
Afroz Mohiuddin
Lukasz Kaiser
Adrian Weller
accepted to ICLR 2021 (oral presentation) (to appear)
Population Based Optimization for Biological Sequence Design
Zelda Mariet
David Martin Dohan
D. Sculley
ICML 2020 (2020)
Model-Based Reinforcement Learning for Biological Sequence Design
David Dohan
Ramya Deshpande
ICLR 2020 (2020)
Biological Sequences Design using Batched Bayesian Optimization
Zelda Mariet
Ramya Deshpande
David Dohan
Olivier Chapelle
NeurIPS workshop on Bayesian Deep Learning (2019)
A Comparison of Generative Models for Sequence Design
David Dohan
Ramya Deshpande
Olivier Chapelle
Babak Alipanahi
Machine Learning in Computational Biology Workshop (2019)
Critiquing Protein Family Classification Models Using Sufficient Input Subsets
Brandon Michael Carter
Jamie Alexander Smith
Theo Sanderson
Drew Bryant
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2019) (to appear)
Deep Learning Classifies the Protein Universe
Drew Bryant
Theo Sanderson
Brandon Carter
D. Sculley
Mark DePristo
Nature Biotechnology (2019)
Sequential regulatory activity prediction across chromosomes with convolutional neural networks
David Kelley
Yakir Reshef
Genome Research (2018)
