Tying and fixing a variety of AI things across Meta and the Industry -- Infra, Software, Open Source, Llama, etc.

Engineering Lead working on AI Infra, PyTorch, Generative AI

• Researcher with 52,000+ citations and an h-index of 29 in Machine Learning, computer vision, robotics• High-risk research. Leading PyTorch

• Lead for the PyTorch ML framework: technical, product, marketing. 100+ engineers.• Cross-industry partnerships with Microsoft, Amazon, Google, OpenAI, Tesla, etc.• Acquired and advise PapersWithCode

• Co-created and lead PyTorch from zero customers to industry-wide adoption. Lead engineering, management, product, marketing and partnerships• Highly-cited research in Machine Learning on Generative Adversarial Networks: Wasserstein GAN, DCGAN and LAPGAN• Highlighted research in SysML, Game AI, NLP, Computer Vision• Lead the software and hardware infrastructure decisions for the FAIR organization from early 2015 to end of 2016, including hardware and cluster decisions and software stack design• Lead maintainer of the Torch-7 deep learning framework from 2013 until it’s end. At its peak, Torch-7 was used by some of the largest industrial labs in machine learning: Facebook, DeepMind and Twitter, among tens of research labs. Received media and scientific attention for this work, being invited to give several tens of talks on this topic.• Created convnet-benchmarks, a popular benchmarking suite for deep learning. It was used from 2015 to 2017 as a gold standard by NVIDIA, AMD, Intel Nervana and many other hardware manufacturers to optimize their systems

Built machine learning and vision systems for mobile devices and music (iOS/Android)

Built various state-of-the-art machine learning systems using Deep Learning approaches. Projects include state-of-the-art results on Google Street View House Numbers (SVHN) dataset, pedestrian detection using unsupervised multi-stage feature learning and sentiment analysis on natural language data using deep neural language models.

Built systems for autonomous robot navigation involving real-time 3D registration and matching of incoming camera images. Also worked on building deep learning classifiers on histopathological data to detect cancerous and malignant tissues.

Monitoring and administration of linux servers, network and linux/windows/mac systems as well as maintaining print, mail servers and computing clusters.

Developed algorithms for navigation and balance of biped robots using a combination of vision and sonar sensors under Dr.Manuela VelosoWorked on Face Recognition problems using a dimensionality reduction approach under Dr.Fernando De La Torre