Tony A. Ginart

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About Me

Greetings, fellow denizen of the interwebs! My name is Antonio (I usually go by Tony). 

I am currently at Salesforce AI Research working on generative AI.

Before, I worked on building reliable and useful LLM agents at Dialect AI. We were in YCombinator's S22 batch.

In the summer of 2022, I finished my PhD at Stanford University. I was fortunate to be advised by Prof. James Zou in the Stanford Laboratory for Machine Learning, Genomics, and Health. I was gratefully supported by a Stanford Bio-X Graduate Fellowship. Get in touch via twitter (@tginart).

When I'm not writing code, cleaning datasets, or (failing at) proving theorems, you can probably find me tumbling down shredding the ski slopes or hero calling at the poker table. 

I am a full-stack ML researcher who is passionate about building the next generation of machine intelligence. I've led a variety of state-of-the-art projects in machine learning, resulting in papers published in top AI venues (for example, NeurIPS), contributions to open-source projects with thousands of users (such facebookresearch/dlrm and huggingface/accelerate), and contributions to production systems serving billions. 

Research Interests

I am broadly interested in artificial intelligence, cybernetics, and information science & engineering. My doctoral research is on theory and algorithms for large-scale machine learning. I work on making ML systems more efficient, scalable, secure and easier to deploy. 


Publications and Manuscripts

A.A. Ginart, L. van der Maaten, J. Zou, C. Guo. SubMix: Practical Private Prediction for Large-Scale Language Models. Preprint, 2022.

A.A. Ginart, M. Zhang, J. Zou. MLDemon: Deployment monitoring for machine learning systemsInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2022. Challenges in Deploying and Monitoring Machine Learning Systems @ ICML, 2021.

A.A. Ginart, M. Naumov, D. Mudigere, J. Yang, J. Zou. Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems. International Symposium on Information Theory (ISIT), 2021. PeRSonAl @ ISCA, 2020. Github.

A.A. Ginart, E. Zhang, Y. Kwon, J. Zou.  Competing AI: How does competition feedback affect machine learning?.  International Conference on Artificial Intelligence and Statistics (AISTATS), 2021. CoopAI @ NeurIPS, 2020. Press: Stanford HAI. 

S. Feizi,  F. Farnia, T. Ginart, D. Tse. Understanding GANs in the LQG Setting: Formulation, Generalization and Stability. IEEE Journal on Selected Areas in Information Theory, 2020.

A.A. Ginart, M. Y. Guan, G. Valiant, J. Zou. Making AI forget you: Data deletion in machine learning. Advances in Neural Information Processing Systems (NeurIPS), 2019. Spotlight. Github. Press: The Register, IEEE Spectrum.  

A.A. Ginart*, J. Hui*, K. Zhu*, I. Numanagic, T.A. Courtade, S.C. Sahinalp, D.N. Tse. Optimal compressed representation of high throughput sequence data via light assembly. Nature Communications, 2018. Github.

A.A. Ginart, S. Das, J.K. Harris, R. Wong, H. Yan, M. Krauss, P.A. Cavazos-Rehg. Drugs or Dancing? Using Real-Time Machine Learning to Classify Streamed "Dabbing" Homograph Tweets. IEEE International Conference on Healthcare Informatics (ICHI), 2016.