Maziar Sanjabi

RESEARCH SCIENTIST | MENLO PARK, UNITED STATES

Maziar is a research scientist on the AI Integrity team. AI integrity strives to make the world a better place by tackling important problems, such as hate(ful) speech, misinformation and many more, through the power of AI.

Prior to joining Facebook, Maziar was at Electronic Arts (EA) where he developed AI models for applications in computer graphics and game design. Maziar completed his PhD at the University of Minnesota, working on optimization methods for statistical signal processing and machine learning. He held postdoctoral positions at UCLA and USC where he worked on scalable AI methods. His current research interests broadly include optimization, multi-modal learning, multi-task and meta-learning, adversarial learning, generative modeling, federated learning, and fairness in machine learning.

Maziar's Publications

April 04, 2024

CORE MACHINE LEARNING

DP-RDM: Adapting Diffusion Models to Private Domains Without Fine-Tuning

Jonathan Lebensold, Maziar Sanjabi, Pietro Astolfi, Adriana Romero Soriano, Kamalika Chaudhuri, Mike Rabbat, Chuan Guo

April 04, 2024

February 20, 2023

INTEGRITY

NLP

UNIREX: A Unified Learning Framework for Language Model Rationale Extraction

Maziar Sanjabi, Aaron Chan, Hamed Firooz, Lambert Mathias, Liang Tan, Shaoliang Nie, Xiaochang Peng, Xiang Ren

February 20, 2023

July 18, 2022

UNIREX: A UNIFIED LEARNING FRAMEWORK FOR LANGUAGE MODEL RATIONALE EXTRACTION

Maziar Sanjabi, Aaron Chan, Hamed Firooz, Lambert Mathias, Liang Tan, Shaoliang Nie, Xiaochang Peng, Xiang Ren

July 18, 2022

July 13, 2022

Federated Learning with Partial Model Personalization

Lin Xiao, Abdelrahman Mohamed, Kshitiz Malik, Maziar Sanjabi, Mike Rabbat, Krishna Pilllutla

July 13, 2022