George Cazenavette

I am a PhD student studying Machine Learning for Vision and Graphics at MIT's Computer Science and Artificial Intelligence Lab (CSAIL) I am extremely fortunate to be co-advised by two fantastic supervisors: Vincent Sitzmann and Antonio Torralba.

Before joining MIT, I was part of the Master of Science in Robotics program at Carnegie Mellon University supported by an NSF fellowship. During my time there, I have had the pleasure of being advised by Simon Lucey (now director of the Austrailian Institute for Machine Learning at The University of Adelaide) and Jun-Yan Zhu as well as collaborating with Alexei Efros (UC Berkeley) and Antonio Torralba (MIT).

Prior to attending CMU, I did my undergraduate studies at Louisiana Tech University where I completed degree programs in Computer Science, Mathematics, and Cyber Engineering. I'm forever grateful for the fantastic professors there that helped me get to where I am today, especially Mike O'Neal, Jonathan Walters, and Jean Gourd.

Email / Google Scholar

Publications

	Fake-Inversion: Learning to Detect Images from Unseen Models by Inverting Stable Diffusion George Cazenavette, Avneesh Sud, Thomas Leung, and Ben Usman. CVPR 2024 [Website] [Paper]
	Towards Lossless Dataset Distillation via Difficulty-Aligned Trajectory Matching Ziyao Guo, Kai Wang, George Cazenavette, Hui Li, Kaipeng Zhang, and Yang You. ICLR 2024 [Website] [Paper] [Code]
	Diffusion with Forward Models: Solving Stochastic Inverse Problems Without Direct Supervision Ayush Tewari, Tianwei Yin, George Cazenavette, Semon Rezchikov, Joshua B. Tenenbaum, Frédo Durand, William T. Freeman, and Vincent Sitzmann. NeurIPS 2023 (Spotlight) [Website] [Paper] [Code]
	Generalizing Dataset Distillation via Deep Generative Prior George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei Efros, and Jun-Yan Zhu. CVPR 2023 [Website] [Paper] [Code]
	Wearable ImageNet: Synthesizing Tileable Textures via Dataset Distillation George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei Efros, and Jun-Yan Zhu. CVPR-W 2022 [Website] [Paper] [Code]
	Dataset Distillation by Matching Training Trajectories George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei Efros, and Jun-Yan Zhu. CVPR 2022 (Oral) [Website] [Paper] [Code]
	Reframing Neural Networks: Deep Structure in Overcomplete Representations Calvin Murdock, George Cazenavette, and Simon Lucey. T-PAMI 2022 [Paper]
	Architectural Adversarial Robustness: The Case for Deep Pursuit George Cazenavette , Calvin Murdock, and Simon Lucey. CVPR 2021 (Oral) [Paper] [Video]
	Laying the Foundation to use Raspberry Pi 3 V2 Camera Module Imagery for Scientific and Engineering Purposes Mary Pagnutti, Robert Ryan, George Cazenavette, Maxwell Gold, Ryan Harlan, Edward Leggett, and James Pagnutti. JEI 2017 [Paper]

Preprints

On the Bias Against Inductive Biases

George Cazenavette and Simon Lucey

arXiv preprint
[Paper]

Course Projects

MixerGAN: An MLP-Based Architecture for Unpaired Image-to-Image Translation

George Cazenavette and Manual Ladron de Guevara

CMU Course Project
[Paper]

Education

Massachusetts Institute of Technology Cambridge, Massachusetts Ph.D. in AI and Decision Making • Sept. 2022 - Present
Carnegie Mellon University Pittsburgh, Pennsylvania M.S. in Robotics • Sept. 2019 - Aug. 2022 National Science Foundation GRFP
Louisiana Tech University Ruston, Louisiana B.S. in Computer Science • Sept. 2015 - May 2019 B.S. in Mathematics • Sept. 2015 - May 2019 B.S. in Cyber Engineering • Sept. 2015 - May 2019

Fellowships

National Science Foundation GRFP

Teaching

Carnegie Mellon University, Machine Learning Department
10-417/617 - Intermediate Deep Learning (Fall 2020) • Instructors: Ruslan Salakhutdinov and Andrej Risteski
10-707 - Advanced Deep Learning (Spring 2021) • Instructor: Andrej Risteski

Selected Coursework

Carnegie Mellon University, Machine Learning Department
10-707 - Advanced Deep Learning (Spring 2020) • Instructor: Andrej Risteski
10-725 - Convex Optimization (Fall 2020) • Instructor: Yuanzhi Li
10-703 - Deep Reinforcement Learning (Fall 2020) • Instructor: Katerina Fragkiadaki

Carnegie Mellon University, Robotics Institute
16-720 - Computer Vision (Fall 2019) • Instructor: Simon Lucey
16-726 - Learning-Based Image Synthesis (Fall 2021) • Instructor: Jun-Yan Zhu

Research Experience

Scene Representation Group, MIT CSAIL Research Assistant • Fall 2022 to Present Advisor: Vincent Sitzmann
Torralba Lab, MIT CSAIL Research Assistant • Fall 2022 to Present Advisor: Antonio Torralba
Argo AI, Pittsburgh, PA Research Intern • Summer 2022 Advisor: Simon Lucey
Generative Intelligence Lab, Carnegie Mellon University Research Assistant • Spring 2021 to Fall 2022 Advisor: Jun-Yan Zhu
Computer Vision Lab, Carnegie Mellon University Research Assistant • Fall 2019 to Fall 2022 Advisor: Simon Lucey
Interaction Lab, University of Southern California Research Intern • Summer 2018 Advisor: Maja Matarić
Innovative Imaging and Research, Stennis Space Center Research Intern • Summers 2015, 2016 Advisor: Robert Ryan

Website source from Xindi Wu