Robert Pellegrino

The paper presents an ensemble model that improves predictions of perceptual similarity between odor mixtures using a curated dataset and collaborative challenge results.

The paper shows that odor mixtures can be predicted linearly from component odorant profiles, challenging the assumption of nonlinear interactions.

The paper introduces an odor sampling bag system that improves odorant concentration control and reliability compared to traditional methods.

This study shows mice and humans use a common perceptual scale to assess odor intensity, demonstrating shared principles in stimulus evaluation.

This study develops a deep learning framework to predict odor intensity from physical properties of molecules and mixtures, overcoming limitations of traditional methods.

Pyrfume provides a unified framework to access and analyze diverse olfactory datasets across species and modalities, enabling novel analyses and benchmarks for machine olfaction.

This study characterizes retronasal olfactory habituation and its impact on flavor perception through two experiments with food and non-food odors.