Deep Learning Algorithms @ Nvidia

Lucas Liebenwein

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 ML 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.

Education

PhD, Computer Science @ Massachusetts Institute of Technology | 2018 – 2021

GPA: 5.0/5.0
Thesis: “Efficient Deep Learning: From Theory to Practice”
Advisor: Prof. Daniela Rus
Minor: Math (High-dimensional Probability)

SM, Electrical Engineering & Computer Science @ Massachusetts Institute of Technology | 2016 – 2018

GPA: 5.0/5.0
Thesis: “Contract-Based Safety Verification for Autonomous Driving“
Advisor: Prof. Daniela Rus
Major: Machine Learning

BSc, Mechanical Engineering @ ETH Zurich | 2012 – 2015

GPA: 5.86/6.0 (Valedictorian)
Thesis: “Autonomous Pairing Of Distributed Flight Array Modules” (Advisor: Prof. Raffaello D’Andrea)
Major: Robotics, Control

Experience

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

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.

Chief Architect & Founding Engineer @ OmniML (acquired) | Oct 2021 – Mar 2023

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.

Machine Learning Consultant @ Neural Magic | Jul 2021 – Oct 2021

Doctoral Researcher @ MIT Computer Science and Artificial Intelligence Lab (CSAIL) | 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.

Visiting Researcher @ TU Vienna | Jul 2020 – May 2021

Autopilot Software Intern @ Tesla | Jun 2019 – Sep 2019

Visiting Researcher @ Singapore-MIT Alliance for Research & Technology Centre | Jan 2017 – Feb 2017

Autonomous Car Intern @ nuTonomy | Dec 2015 – Feb 2016

I designed, developed, and implemented an automated velocity controller using a novel combination of traditional control techniques and machine learning.

Undergraduate Researcher @ ETH Zurich | Sep 2014 – Oct 2015

Under the supervision of Prof. Raffaello D’Andrea, I co-led the implementation of a real-time operating system for the Distributed Flight Array (DFA), developed autonomous sensing and decision-making capabilities, and conducted research on self-assembly algorithms.

Workshop Intern @ Rejlek Metal & Plastics Group | Jan 2013 – Feb 2013

I contributed to developing a novel magnetic gearbox, manufacturing prototypes, and assembling a testing facility while receiving training in operating industrial machines.

Teaching

Teaching Assistant @ ETH Zurich (Dynamics) | Jun 2015 – Nov 2015

As a TA for Prof. George Haller, I restructured and extended the course content for the “Dynamics” lecture, which is a mandatory class for second-year Mechanical Engineering students. This included writing the accompanying course text and publishing its initial version.

Teaching Assistant @ ETH Zurich (Dynamics) | Aug 2014 – Dec 2014

As a TA for Prof. George Haller’s “Dynamics” course, I co-led weekly recitations and exercises for 80-100 second-year Mechanical Engineering students.

Teaching Assistant @ ETH Zurich (Computer Science I) | Feb 2014 – Jul 2014