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Kian Kenyon-Dean

5 accepted papers

2025

A Cross Modal Knowledge Distillation & Data Augmentation Recipe for Improving Transcriptomics Representations through Morphological Features

ICML 2025poster

Understanding cellular responses to stimuli is crucial for biological discovery and drug development. Transcriptomics provides interpretable, gene-level insights, while microscopy imaging offers rich predictive features but is harder to interpret. Weakly paired datasets, where samples share biologic…

Cited by 0SourcePDFScholar
2025

Towards scientific discovery with dictionary learning: Extracting biological concepts from microscopy foundation models

ICML 2025poster

Sparse dictionary learning (DL) has emerged as a powerful approach to extract semantically meaningful concepts from the internals of large language models (LLMs) trained mainly in the text domain. In this work, we explore whether DL can extract meaningful concepts from less human-interpretable scien…

Cited by 1SourcePDFScholar
2025

ViTally Consistent: Scaling Biological Representation Learning for Cell Microscopy

ICML 2025poster

Deriving insights from experimentally generated datasets requires methods that can account for random and systematic measurement errors and remove them in order to accurately represent the underlying effects of the conditions being tested. Here we present a framework for pretraining on large-scale m…

Cited by 6SourcePDFScholar
2024

Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology

CVPR 2024highlight

Featurizing microscopy images for use in biological research remains a significant challenge especially for large-scale experiments spanning millions of images. This work explores the scaling properties of weakly supervised classifiers and self-supervised masked autoencoders (MAEs) when training wit…

2020

Learning Efficient Task-Specific Meta-Embeddings with Word Prisms

COLING 2020main

Word embeddings are trained to predict word cooccurrence statistics, which leads them to possess different lexical properties (syntactic, semantic, etc.) depending on the notion of context defined at training time. These properties manifest when querying the embedding space for the most similar vect…