Pedro O. Pinheiro

I am a research scientist at Genentech, Prescient Design team.

Previously, I was a research scientist at Deep Genomics and Element AI (where I work closely with people from Mila).

I received a Ph.D. from École Polytechnique Fédérale de Lausanne (EPFL) and Idiap Research Institute (Switzerland), under supervision of Ronan Collobert in 2017. During my phd, I also spent time at Facebook AI Research (FAIR). Previously, I graduated with a MSc. in Image and Signal Processing from Institut National des Sciences Appliquées de Lyon (INSA), in France. I am originally form sunny Fortaleza, Brazil.

Email / Google Scholar / GitHub / LinkedIn

Research

I'm interested in machine learning and its applications, particularly in computer vision, molecular sciences and drug discovery. Currently I am very interested in how we can improve human health with computational models.

	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 arXiv , 2023 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
	Unsupervised Learning of Dense Visual Representations Pedro O. Pinheiro, Amjad Almahairi, Ryan Y. Benmalek, Florian Golemo, Aaron Courville Neurips , 2020 paper /
	Reinforced Active Learning for Image Segmentation Arantxa Casanova, Pedro O. Pinheiro, Negar Rostamzadeh, Christopher 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 /
	Predicting outdoor ultrafine particle number concentrations, particle size, and noise using street-level images and audio data Kris Hong, Pedro O. Pinheiro, Scott Weichenthal Environmental International , 2020 paper /
	Neural Multisensory Scene Inference Jae Hyun Lim, Pedro O. Pinheiro, Negar Rostamzadeh, Christopher Pal, Sungjin Ahn Neurips , 2019 paper / code /
	Adaptive Cross-Modal Few-shot Learning Chen Xing, Negar Rostamzadeh, Boris N. Oreshkin, Pedro O. Pinheiro Neurips , 2019 paper / code /
	Predicting Global Variations in Outdoor PM2. 5 Concentrations using Satellite Images and Deep Convolutional Neural Networks Kris Y Hong, Pedro O. Pinheiro, Scott Weichenthal ICML Workshop , 2019 paper /
	Domain-Adaptive Single-View 3D Reconstruction Pedro O. Pinheiro, Negar Rostamzadeh, Sungjin Ahn ICCV (oral), 2019 paper /
	Extending the spatial scale of land use regression models for ambient ultrafine particles using satellite images and deep convolutional neural networks Kris Y.Hong, Pedro O. Pinheiro, Laura Minet, Marianne Hatzopoulou, Scott Weichenthal Environmental Research Journal, 2019 paper /
	Unsupervised Domain Adaptation with Similarity Learning Pedro O. Pinheiro CVPR, 2018 paper /
	Where are the Blobs: Counting by Localization with Point Supervision Issam Laradji, Negar Rostamzadeh, Pedro O. Pinheiro,David Vazquez. Mark Schmidt ECCV, 2018 paper / code /
	Learning to Refine Object Segments Pedro O. Pinheiro, Tsung-Yi Lin, Ronan Collobert, Piotr Dollar ECCV (spotlight), 2016 paper / code /
	A Multipath Network for Object Detection Sergey Zagoruyko, Adam Lerer, Tsung-Yi Lin, Pedro O. Pinheiro, Sam Gross, Soumith Chintala, Piotr Dollár BMVC, 2016 paper / code /
	Learning to Segment Object Candidates Pedro O. Pinheiro, Ronan Collobert, Piotr Dollar NIPS* (spotlight), 2015 paper / code /
	From Image-level to Pixel-level Labeling with Convolutional Networks Pedro O. Pinheiro, Ronan Collobert CVPR, 2015 paper /
	Phrase-Based Image Captioning Rémi Lebret, Pedro O. Pinheiro*, Ronan Collobert ICML*, 2015 paper /
	Recurrent Convolutional Neural Networks for Scene Labeling Pedro O. Pinheiro, Ronan Collobert ICML, 2014 paper /

Forked from: Jon Barron's webiste