A complete list of my publications can be found on my Google Scholar. Below is a list of selected publications:


Closed-form continuous-time neural networks

Ramin Hasani, Mathias Lechner, Alexander Amini, Lucas Liebenwein, Aaron Ray, Max Tschaikowski, Gerald Teschl, Daniela Rus. In Nature Machine Intelligence, 1-12, 2022.
[PDF], [Code], [BibTex], [Proceedings]

Pruning by Active Attention Manipulation

Zahra Babaiee, Lucas Liebenwein, Ramin Hasani, Daniela Rus, Radu Grosu. In arXiv preprint arXiv:2204.07412, 2022.
[PDF], [BibTex], [arXiv]

Sensitivity-Informed Provable Pruning of Neural Networks

Cenk Baykal*, Lucas Liebenwein*, Igor Gilitschenski, Dan Feldman, Daniela Rus. In SIAM Journal on Mathematics of Data Science 4, no. 1, 26-45, 2022.
[PDF], [Code], [BibTex], [Proceedings]

Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition

Lucas Liebenwein, Alaa Maalouf, Dan Feldman, Daniela Rus. In Advances in Neural Information Processing Systems 34, 5328-5344 (NeurIPS), 2021.
[PDF], [Code], [Slides], [Poster], [BibTex], [Proceedings]

Sparse flows: Pruning continuous-depth models

Lucas Liebenwein, Ramin Hasani, Alexander Amini, Daniela Rus. In Advances in Neural Information Processing Systems 34, 22628-22642 (NeurIPS), 2021.
[PDF], [Code], [Poster], [BibTex], [Proceedings]

Low-Regret Active Learning

Cenk Baykal, Lucas Liebenwein, Dan Feldman, Daniela Rus. In arXiv preprint arXiv:2104.02822, 2021.
[PDF], [BibTex], [arXiv]

Lost in Pruning: The Effects of Pruning Neural Networks beyond Test Accuracy

Lucas Liebenwein, Cenk Baykal, Brandon Carter, David Gifford, Daniela Rus. In Proceedings of Machine Learning and Systems (MLSys), 2021.
[PDF], [Code], [Slides], [Oral], [Poster], [BibTex]

Provable Filter Pruning for Efficient Neural Networks

Lucas Liebenwein*, Cenk Baykal*, Harry Lang, Dan Feldman, Daniela Rus. In International Conference on Learning Representations, 2020.
[PDF], [Code], [Slides], [Video/ICLR], [Video/Workshop], [BibTex], [Proceedings], [News Article]

Deep Latent Competition: Learning to Race Using Visual Control Policies in Latent Space

Wilko Schwarting*, Tim Seyde*, Igor Gilitschenski*, Lucas Liebenwein, Ryan Sander, Sertac Karaman, Daniela Rus. In Conference on Robot Learning (CoRL), 2020.
[PDF], [Code], [Video], [BibTex], [Proceedings], [News Article]

Machine Learning-based Estimation of Forest Carbon Stocks to increase Transparency of Forest Preservation Efforts

Björn Lütjens, Lucas Liebenwein, Katharina Kramer. In NeurIPS Workshop Tackling Climate Change with Machine Learning, 2019.
[PDF], [BibTex], [Proceedings]

Data-Dependent Coresets for Compressing Neural Networks with Applications to Generalization Bounds

Cenk Baykal*, Lucas Liebenwein*, Igor Gilitschenski, Dan Feldman, Daniela Rus. In International Conference on Learning Representation (ICLR), 2019.
[PDF], [Code], [BibTex], [Proceedings]

Sampling-Based Approximation Algorithms for Reachability Analysis with Provable Guarantees

Lucas Liebenwein*, Cenk Baykal*, Igor Gilitschenski, Sertac Karaman, Daniela Rus. In Robotics: Science and Systems XIV (RSS), 2018.
[PDF], [Video], [Slides], [BibTex], [Proceedings]

Counterexample-Guided Safety Contracts for Autonomous Driving

Jonathan A DeCastro*, Lucas Liebenwein*, Cristian-Ioan Vasile, Russ Tedrake, Sertac Karaman, and Daniela Rus. In International Workshop on the Algorithmic Foundations of Robotics (WAFR), 2018.
[PDF], [Slides], [BibTex], [Proceedings]

Compositional and Contract-Based Verification for Autonomous Driving on Road Networks

Lucas Liebenwein, Wilko Schwarting, Cristian-Ioan Vasile, Jonathan DeCastro, Javier Alonso-Mora, Sertac Karaman, Daniela Rus. In International Symposium on Robotics Research (ISRR), 2017.
[PDF], [Video], [Slides], [BibTex], [Proceedings]


Efficient Deep Learning: From Theory to Practice

Lucas Liebenwein. Ph.D. Thesis, Massachusetts Institute of Technology, 2021.
[PDF], [Slides], [BibTex], [Link]

Contract-Based Safety Verification for Autonomous Driving

Lucas Liebenwein. S.M. Thesis, Massachusetts Institute of Technology, 2018.
[PDF], [BibTex], [Link]

Autonomous Pairing of Distributed Flight Array Modules

Lucas Liebenwein. B.Sc. Thesis, Swiss Federal Institute Of Technology, Zurich, 2015.
[PDF], [BibTex]


This is a non-comprehensive list of projects I have worked on over the years. This list does not include any projects that are not part of the public domain.


A research library for pytorch-based neural network pruning, compression, and more. It contains my PhD research.

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Sustainable AI

We estimate tree-sequestered carbon with deep-learning-based algorithms on satellite and drone imagery.

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Distributed Flight Array

A distributed flying platform that is able to drive, dock with their peers, and fly in a coordinated fashion.

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Autonomous Driving

The Toyota-CSAIL Joint Research Center is aimed at furthering the development of autonomous vehicle technologies.

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The goal of this projects is to design novel data compression techniques to accelerate popular machine learning algorithms.

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A smart bottle cage that acts as a personal coach to coach professional cyclists how to stay hydrated during races.

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Lucas Liebenwein

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