Tony Ginart

PhD Candidate at Stanford University

Tony Ginart is a Graduate Student Researcher who has recently completed his PhD in Electrical Engineering at Stanford University. His research primarily focuses on large-scale machine learning systems, with a particular interest in the deployment and efficiency of these systems. He was advised by Professor James Zou and received a Stanford Bio-X Graduate Fellowship during his studies.1 3

Education and Research

Bachelor's Degree: Bachelor of Science in Computer Engineering from Washington University in St. Louis, graduated summa cum laude in 2017.
Master's Degree: Master of Science in Electrical Engineering from Stanford University, completed in 2020.
PhD: Doctor of Philosophy in Electrical Engineering from Stanford University, completed in 2022. His dissertation involved the efficient deployment of machine learning systems and included work on privacy-preserving prediction protocols for large-scale language models.1 2

Professional Experience

Tony has held various positions, including:

Co-Founder of Dialect, an AI company focused on developing reliable language model agents, which was part of the YCombinator S22 batch.1 2
Graduate Student Researcher at Stanford University from September 2017 to June 2022, where he conducted significant research in machine learning.1 3
Research Intern at Facebook, where he worked on memory-efficient architectures for deep neural networks and privacy-preserving methods for language models.1 2

Contributions and Publications

Tony has contributed to several notable projects and publications in the field of machine learning, including:

Research on data deletion in machine learning and efficient algorithms for large-scale systems, resulting in publications in prestigious conferences such as NeurIPS and AISTATS.2 3

Languages

He is proficient in English and has professional working proficiency in Spanish.1

Tony Ginart is recognized for his innovative approach to machine learning and his contributions to the field, making him a notable figure in the intersection of AI and engineering.