Lucas Liebenwein

Generative AI & DL Algorithms @ NVIDIA

Passionate about making machine learning more efficient and ubiquitously available to improve our everyday lives.

About Me

Currently, I am a Manager at Nvidia following the acquisition of OmniML, where I served as Chief Architect and Founding Engineer. At Nvidia, we are building a machine learning (ML) platform for algorithmic model optimization to enable efficient and seamless deployment of GenAI at scale.

Prior to that, I was a Ph.D. researcher at MIT CSAIL, advised by Prof. Daniela Rus, where my research was focused on efficient deep learning algorithms and autonomous driving.

Throughout my professional career, I have been passionate about making ML more easily accessible for individuals and organizations alike by bridging the gap from ML research to user-friendly and scalable AI tools and platforms.

Recent Experience

Manager, Deep Learning Algorithms @ Nvidia | Feb 2023 – Now

Following our acquisition by Nvidia, we continue to build a scalable and accessible machine learning platform enabling efficient deployment of ML models in production. Our current work primarily focuses on enabling large-scale deployment of generative AI models (GPT, Llama, Stable Diffusion, …) in production through algorithmic optimizations of the model architecture and training.

We built a scalable, accessible machine learning platform by redefining how we create and deploy deep neural networks in production. Our product is based on years of research into optimizing models for efficient deployment across a wide range of systems.

In my role at OmniML, I led the design and implementation of Omnimizer, our scalable platform for efficient ML training and deployment, while exploring state-of-the-art model optimization research for future product iterations.


PhD & SM, Computer Science @ MIT | Sep 2016 – Aug 2021

My research focused on optimizing deep neural networks for resource-constrained applications, such as robotics and cloud computing. I developed novel techniques in model compression and pruning that improve the speed-accuracy trade-off and provide theoretical insights into network design and training.

Before that, I worked on verification algorithms for safe autonomous driving and contributed to our AV research platforms (self-driving cars and wheelchairs) for testing and validation.

Lucas Liebenwein

Passionate about making machine learning more efficient and ubiquitously available to improve our everyday lives.