Pablo Hennings Email & Phone Number | AI Perception Engineer: computer vision, deep learning, autonomous driving. Recent skills: Pytorch, Python, C/C++, OpenCV, Docker, Git. At NVIDIA, I work on synthetic data solutions for deep learning teams working on autonomous driving. Previously, at LeddarTech I was part of a team of engineers working to deliver machine perception solutions to next-generation solid-state LiDAR platforms. Recently, my focus has been on machine perception in level-5 autonomous vehicles, including design of LiDAR-only solutions using a variety of relevant deep learning approaches. Earlier, as a data scientist, I designed applications that process multimedia at scale from social networks for sentiment analyses. By processing video, images, speech and text, I addressed the problem of how artificial intelligence understands human expression and emotion, to improve our own social interaction. I also worked in the area of genomic data science, where my work combined computational biology with machine learning engineering. I designed and implemented machine learning analyses to study the footprint of genomic variations in the human epiginome and related molecular structures. With the results of this work, drug discovery labs prioritized the rather costly study of new biomarkers, thus streamlining precision medicine. Previously, I worked on cancer research, contributing to the analysis of next-generation sequencing (NGS) data from prostate cancer patients using machine learning to mine genomes for structural variations associated with cancer. The contribution of this work was included in a variety of Nature journals. Earlier, after my PhD, I worked on biomedical computer vision, also called bioimaging, with a focus on mathematical models for images and multiresolution analysis. During my PhD studies in Carnegie Mellon University, I worked on face recognition, super-resolution and image processing, machine learning and pattern recognition projects, including automatic speech recognition. My PhD work was featured in MIT Technology Review (A Face-Finding Search Engine) and other media outlets.

Pablo Hennings

AI Perception Engineer: computer vision, deep learning, autonomous driving. Recent skills: Pytorch,...
Orlando, Florida, United States

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Pablo Hennings’s Email pablo.hennings@leddartech.com

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Pablo Hennings’s Location Orlando, Florida, United States

Pablo Hennings’s Current Industry Nvidia

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Work Experience

Nvidia

Senior Software Engineer - DRIVE Sim

2022-05-01 00:00:00 - Present

Leddartech Mastering Lidar Sensor Technology

Senior AI Software Engineer - Perception

2020-01-01 00:00:00 - 2022-04-01 00:00:00

Huawei Canada Research Centre

Senior AI Software Engineer - Autonomous Machine Perception, Deep Learning

2016-12-01 00:00:00 - 2019-12-01 00:00:00

Sysomos

Data Scientist - Computer Vision and Machine Learning

2016-02-01 00:00:00 - 2016-11-01 00:00:00

Mount Sinai Hospital Toronto

Research Scientist - Machine Learning Epigenomics

2014-02-01 00:00:00 - 2015-11-01 00:00:00

Ontario Institute For Cancer Research

Cancer Research Scientist - Genomic Data Science

2011-07-01 00:00:00 - 2013-11-01 00:00:00

University Of Pittsburgh School Of Medicine

Research Associate - Machine Learning for Computational Genomics

2009-07-01 00:00:00 - 2011-06-01 00:00:00

Carnegie Mellon University

Researcher, Consultant - Biomedical Image Recognition

2008-10-01 00:00:00 - 2011-05-01 00:00:00

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FAQ About Pablo Hennings

Pablo Hennings is located at
Orlando, Florida, United States

Pablo Hennings is a AI Perception Engineer: computer vision, deep learning, autonomous driving. Recent skills: Pytorch, Python, C/C++, OpenCV, Docker, Git. At NVIDIA, I work on synthetic data solutions for deep learning teams working on autonomous driving. Previously, at LeddarTech I was part of a team of engineers working to deliver machine perception solutions to next-generation solid-state LiDAR platforms. Recently, my focus has been on machine perception in level-5 autonomous vehicles, including design of LiDAR-only solutions using a variety of relevant deep learning approaches. Earlier, as a data scientist, I designed applications that process multimedia at scale from social networks for sentiment analyses. By processing video, images, speech and text, I addressed the problem of how artificial intelligence understands human expression and emotion, to improve our own social interaction. I also worked in the area of genomic data science, where my work combined computational biology with machine learning engineering. I designed and implemented machine learning analyses to study the footprint of genomic variations in the human epiginome and related molecular structures. With the results of this work, drug discovery labs prioritized the rather costly study of new biomarkers, thus streamlining precision medicine. Previously, I worked on cancer research, contributing to the analysis of next-generation sequencing (NGS) data from prostate cancer patients using machine learning to mine genomes for structural variations associated with cancer. The contribution of this work was included in a variety of Nature journals. Earlier, after my PhD, I worked on biomedical computer vision, also called bioimaging, with a focus on mathematical models for images and multiresolution analysis. During my PhD studies in Carnegie Mellon University, I worked on face recognition, super-resolution and image processing, machine learning and pattern recognition projects, including automatic speech recognition. My PhD work was featured in MIT Technology Review (A Face-Finding Search Engine) and other media outlets.

Pablo Hennings currently works in the Nvidia |

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Pablo Hennings has prior
experience in the following industries:
Leddartech Mastering Lidar Sensor Technology | Huawei Canada Research Centre | Sysomos | Mount Sinai Hospital Toronto | Ontario Institute For Cancer Research | University Of Pittsburgh School Of Medicine | Carnegie Mellon University

Pablo Hennings has the following professional experience:
Nvidia | Leddartech Mastering Lidar Sensor Technology | Huawei Canada Research Centre | Sysomos | Mount Sinai Hospital Toronto | Ontario Institute For Cancer Research | University Of Pittsburgh School Of Medicine | Carnegie Mellon University