Pedro O. Pinheiro

I am a member of technical staff at AMI Labs.

Previously, I was a principal research scientist at Genentech, Prescient Design team. I received a phd from École Polytechnique Fédérale de Lausanne (EPFL) and Idiap Research Institute (Switzerland), under supervision of Ronan Collobert. During my phd, I also spent time at Facebook AI Research (FAIR). Previously, I graduated in electrical engineering from Institut National des Sciences Appliquées de Lyon (INSA), in France. I am originally from sunny Fortaleza, Brazil.

Email | LinkedIn

Research

I am interested in machine learning and its applications, specifically deep learning methods for representation learning and generative modeling. Currently, I am interested in building world models that learn abstract representations of continuous, high-dimensional data. Previously, I spent some years developing AI methods to tackle complex scientific problems, such as molecular sciences and drug discovery. Before that, my work focused on computer vision applications.

Below is a list of some work that represent my research interests, style and taste. For a full list of publications, please see my Google Scholar page.

	Implicit guided generation through conditional density estimation Pedro O. Pinheiro, Pan Kessel, Aya A. Ismail, Sai P. Mahajan, Kyunghyun Cho, Saeed Saremi, Nataša Tagasovska NeurIPS, 2025 paper
	Unified all-atom molecule generation with neural fields Matthieu Kirchmeyer^, Pedro O. Pinheiro^, Karolis Martinkus, Emma Willett, ..., Richard Bonneau, Saeed Saremi NeurIPS, 2025 (^*equal contribution) paper \| code
	Score-based 3D molecule generation with neural fields Matthieu Kirchmeyer^, Pedro O. Pinheiro^, Saeed Saremi NeurIPS, 2024 (^*equal contribution) paper \| code
	Structure-based drug design by denoising voxel grids Pedro O. Pinheiro, Arian Jamasb, Omar Mahmood, Vishnu Sresht, Saeed Saremi ICML, 2024 paper \| code
	3D Molecule generation by denoising voxel grids Pedro O. Pinheiro, Joshua Rackers, Joseph Kleinhenz, Michael Maser, Omar Mahmood, Andrew Martin Watkins, Stephen Ra, Vishnu Sresht, Saeed Saremi NeurIPS, 2023 paper \| code
	DyAb: sequence-based antibody design and property prediction in a low-data regime Joshua Yao-Yu Lin, Jennifer L. Hofmann, Andrew Leaver-Fay,..., Pedro O. Pinheiro,..., Andrew Watkins, Kyunghyun Cho, and Nathan C. Frey mAbs Journal, 2026 paper
	An RNA foundation model enables discovery of disease mechanisms and candidate therapeutics Albi Celaj, Alice Jiexin Gao, Tammy T.Y. Lau, Erle M. Holgersen, ..., Pedro O. Pinheiro, ..., Brendan J. Frey bioRxiv, 2023 paper
	Unsupervised learning of dense visual representations Pedro O. Pinheiro, Amjad Almahairi, Ryan Benmalek, Florian Golemo, Aaron Courville NeurIPS, 2020 paper
	Touch-based Curiosity for Sparse-Reward Tasks Sai Rajeswar, Cyril Ibrahim, Nitin Surya, Florian Golemo, David Vazquez, Aaron Courville, Pedro O. Pinheiro CoRL , 2021 paper \| code
	Reinforced active learning for image segmentation Arantxa Casanova, Pedro O. Pinheiro, Negar Rostamzadeh, Christopher J. Pal ICLR, 2020 paper \| code
	Combining Citizen Science and Deep Learning for Large-Scale Estimation of Outdoor Nitrogen Dioxide Concentrations Scott Weichenthal, Evi Dons, Kris Hong, Pedro O. Pinheiro, Filip JR Meysman Environmental Research, 2020 paper
	Adaptive cross-modal few-shot learning C Xing, N Rostamzadeh, B Oreshkin, Pedro O. Pinheiro NeurIPS, 2019 paper \| code
	Extending the spatial scale of land use regression models for ambient ultrafine particles using satellite images and deep convolutional neural networks Kris Hong, Pedro O. Pinheiro, Laura Minet, Marianne Hatzopoulou, Scott Weichenthal Environmental Research, 2019 paper
	Unsupervised Domain Adaptation with Similarity Learning Pedro O. Pinheiro CVPR, 2018 paper
	Learning to refine object segments Pedro O. Pinheiro, Tsung-Yi Lin, Ronan Collobert, Piotr Dollár ECCV, 2016 paper \| code
	Learning to segment object candidates Pedro O. Pinheiro, Ronan Collobert, Piotr Dollár NIPS, 2015 paper \| code
	From Image-Level to Pixel-Level Labeling With Convolutional Networks Pedro O. Pinheiro, Ronan Collobert CVPR, 2015 paper
	Recurrent convolutional neural networks for scene labeling Pedro O. Pinheiro, Ronan Collobert ICML, 2014 paper