Matt Jordan

I am a final-year PhD student in the department of Computer Science at the University of Texas at Austin, advised by Alex Dimakis . I am generally interested in security and privacy of machine learning models. Early in my PhD this meant working on provable verification methods for adversarial robustness and lipschitzness of neural networks, with side projects in contrastive learning. These days, I've shifted focus towards applications involving large scale generative models, with an emphasis on language models. Prior to my PhD I received a bachelor's degree in math and computer science from MIT and started a marketing automation startup.

I am currently on the job market and am actively seeking employment!

Publications

	Inverse Problems Leveraging Pre-trained Contrastive Representations Sriram Ravula, Georgios Smyrnis, Matt Jordan, Alexandros G. Dimakis Advances in Neural Information Processing Systems (NeurIPS) 2021. [PDF]   [Code]
	Provable Lipschitz Certification for Generative Models Matt Jordan, Alexandros G. Dimakis International Conference on Machine Learning (ICML) 2021. [PDF]   [Code]
	Quarantines as a Targeted Immunization Strategy Jessica Hoffmann, Matt Jordan, Constantine Caramanis. Preprint, arXiv:2008.08262. [PDF]
	Exactly Computing the Local Lipschitz Constant of ReLU Networks Matt Jordan, Alexandros G. Dimakis. Advances in Neural Information Processing Systems (NeurIPS) 2020. [PDF]   [Code]
	Provable Certificates for Adversarial Examples: Fitting a Ball in a Union of Polytopes Matt Jordan, Justin Lewis, Alexandros G. Dimakis. Advances in Neural Information Processing Systems (NeurIPS) 2019. [PDF]   [Code]   [Talk]
	Quantifying Perceptual Distortion of Adversarial Examples Matt Jordan, Naren Manoj, Surbhi Goel, Alexandros G. Dimakis. Preprint, arXiv:1902.08265. [PDF]   [Code]