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I am a research scientist at DeepMind in London. I earned my PhD in computer science from UC Berkeley in 2019 where I was advised by Trevor Darrell as part of BAIR. Previously, I spent a wonderful year in Cambridge, MA as a research scientist at Adobe.
I believe in DIY science and open tooling for research and engineering.
Before Berkeley, I earned dual degrees in computer science (artificial intelligence concentration) and psychology at UMass Amherst advised by Erik Learned-Miller. I take my coffee black. |
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I'm interested in computer vision and machine learning, in particular the reconciliation of visual structure with end-to-end learning, plus dynamic inference by adaptive model complexity and computation. See my scholar page for a full list of projects. Selected Projects |
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Fully convolutional networks are machines for image-to-image learning and inference.
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Caffe is a deep learning framework made with expression, speed, and modularity in mind. The deep learning shift was in part a sea change on the wave of open science and toolkits, including Caffe and its Model Zoo. |
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Tent ⛺️ helps a model adapt itself to changing conditions ☀️ 🌧 ❄️ by updating on new and different data during testing without altering training or requiring more supervision. Tent adapts by test entropy minimization: optimizing the model for confidence as measured by the entropy of its predictions. |
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Composing structured Gaussian filters with free-form filters, and learning both, optimizes over filter size and shape alongside content.
In effect this controls the degree of locality:
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Most methods for test-time adaptation update the source model by (re-)training on each target domain. We update the target data instead, and project all test inputs toward the source domain with a generative diffusion model. Our input updates help on small batches, data in dependent orders, or on data with multiple corruptions. |
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Models trained for different types of robustness can be merged by taking linear combinations of their parameters to achieve different types of robustness during testing. |
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Adaptive test-time defenses alter inference by iteratively updating the input x or parameters 𝜃 of the model to improve robustness to adversarial attack. Or do they? Our careful case study shows that more updates are needed to improve on the robustness of adversarial training. |
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Infinite mixture prototypes adaptively adjust model capacity by representing classes as sets of clusters and inferring their number. This handles both simple and complex few-shot tasks, and improves alphabet recognition accuracy by 25% absolute over uni-modal prototypes. |
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Extracting a latent task representation from local supervision allows for non-local propagation within and across images with quick updates for real-time interaction. (Note: this subsumes our ICLRW'18 paper on conditional networks). |
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Deepening aggregation, the iterative and hierarchical merging of features across layers, improves recognition and resolution. |
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Loss is where you find it. With self-supervision for representation learning, experience without reward need not be so unrewarding for reinforcement learning. |
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Adaptively computing layers according to their rate of change improves the efficiency of video processing without sacrificing accuracy. |
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Area Chair: CVPR (2021, 2023), ICCV (2021), NeurIPS (2023).
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Graduate Student Instructor, CS188 Fall 2013
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