Pedro O. Pinheiro

I am a research scientist at Deep Genomics (Toronto, Canada), where I develop machine learning algorithms for RNA biology/therapeutics and drug discovery.

Previously I was a research scientist at Element AI, working closely with people from Mila (Montreal, Canada). There I focused on computer vision and machine learning.

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.

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Research

I'm interested in machine learning and its applications, particualrly in computer vision and computational biology.

Currently I am very interested in how we can improve human health with computational models. In particular, by leveraging the vast amount of genomics data that has been generated in last years. I am working on machine learning models to untangles the complexity in RNA biology, identifies novel targets, and evaluates thousands of possibilities to identify the best therapeutic candidates.

Touch-based Curiosity for Sparse-Reward Tasks
Sai Rajeswar, Cyril Ibrahim, Nitin Surya, Florian Golemo, David Vazquez, Aaron Courville, Pedro O. Pinheiro
CoRL , 2021
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Unsupervised Learning of Dense Visual Representations
Pedro O. Pinheiro, Amjad Almahairi, Ryan Y. Benmalek, Florian Golemo, Aaron Courville
Neurips , 2020
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Reinforced Active Learning for Image Segmentation
Arantxa Casanova, Pedro O. Pinheiro, Negar Rostamzadeh, Christopher Pal
ICLR , 2020
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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
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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
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Neural Multisensory Scene Inference
Jae Hyun Lim, Pedro O. Pinheiro, Negar Rostamzadeh, Christopher Pal, Sungjin Ahn
Neurips , 2019
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Adaptive Cross-Modal Few-shot Learning
Chen Xing, Negar Rostamzadeh, Boris N. Oreshkin, Pedro O. Pinheiro
Neurips , 2019
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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
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Domain-Adaptive Single-View 3D Reconstruction
Pedro O. Pinheiro, Negar Rostamzadeh, Sungjin Ahn
ICCV (oral), 2019
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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
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Unsupervised Domain Adaptation with Similarity Learning
Pedro O. Pinheiro
CVPR, 2018
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Where are the Blobs: Counting by Localization with Point Supervision
Issam Laradji, Negar Rostamzadeh, Pedro O. Pinheiro,David Vazquez. Mark Schmidt
ECCV, 2018
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Learning to Refine Object Segments
Pedro O. Pinheiro*, Tsung-Yi Lin*, Ronan Collobert, Piotr Dollar
ECCV (spotlight), 2016
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A Multipath Network for Object Detection
Sergey Zagoruyko, Adam Lerer, Tsung-Yi Lin, Pedro O. Pinheiro, Sam Gross, Soumith Chintala, Piotr Dollár
BMVC, 2016
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Learning to Segment Object Candidates
Pedro O. Pinheiro*, Ronan Collobert, Piotr Dollar
NIPS (spotlight), 2015
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From Image-level to Pixel-level Labeling with Convolutional Networks
Pedro O. Pinheiro, Ronan Collobert
CVPR, 2015
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Phrase-Based Image Captioning
Rémi Lebret*, Pedro O. Pinheiro*, Ronan Collobert
ICML, 2015
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Recurrent Convolutional Neural Networks for Scene Labeling
Pedro O. Pinheiro, Ronan Collobert
ICML, 2014
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Forked from: Jon Barron's webiste