Conor Liston — accepted AI-conference papers

2026

Contrastive Diffusion Alignment: Learning Structured Latents for Controllable Generation

Ruchi Sandilya, Sumaira Perez, Charles Lynch, Lindsay Victoria, Benjamin Zebley, Derrick Buchanan +6

ICML 2026poster

Diffusion models excel at generation, but their latent spaces are high dimensional and not explicitly organized for interpretation or control. We introduce ConDA (Contrastive Diffusion Alignment), a plug-and-play geometry layer that applies contrastive learning to pretrained diffusion latents using …

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2024

Generalizing CNNs to graphs with learnable neighborhood quantization

Isaac Osafo Nkansah, Neil Gallagher, Ruchi Sandilya, Conor Liston, Logan Grosenick

NeurIPS 2024poster

Convolutional neural networks (CNNs) have led to a revolution in analyzing array data. However, many important sources of data, such as biological and social networks, are naturally structured as graphs rather than arrays, making the design of graph neural network (GNN) architectures that retain the…

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2024

Simple and scalable algorithms for cluster-aware precision medicine

Amanda M. Buch, Conor Liston, Logan Grosenick

AISTATS 2024poster

AI-enabled precision medicine promises a transformational improvement in healthcare outcomes. However, training on biomedical data presents significant challenges as they are often high dimensional, clustered, and of limited sample size. To overcome these challenges, we propose a simple and scalable…

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