Ahmad Beirami
Ahmad is a Research Scientist at Facebook AI, contributing to conversational AI research. His current research interests include reinforcement learning, meta-learning, adversarial learning, federated learning, and privacy/fairness in machine learning. He received his BS degree in 2007 from Sharif University of Technology, Iran, and his PhD degree in 2014 from the Georgia Institute of Technology, in electrical and computer engineering. Ahmad is the recipient of the 2015 Sigma Xi Best PhD Thesis Award from Georgia Tech.
Ahmad's Work
Ahmad's Publications
May 07, 2021
RESEARCH
SYSTEMS RESEARCH
Ditto: Fair and robust federated learning through personalization
Fairness and robustness are two important concerns for federated learning systems. …
Tian Li, Shengyuan Hu, Ahmad Beirami, Virginia Smith
May 07, 2021
December 11, 2020
RESEARCH
CORE MACHINE LEARNING
Federated Multi-Task Learning for Competing Constraints
In addition to accuracy, fairness and robustness are two critical concerns for federated learning systems.…
Tian Li, Shengyuan Hu, Ahmad Beirami, Virginia Smith
December 11, 2020
December 08, 2020
CONVERSATIONAL AI
NLP
Situated and Interactive Multimodal Conversations
Next generation virtual assistants are envisioned to handle multimodal inputs (e.g., vision, memories of previous interactions, and the user’s utterances)…
Seungwhan Moon, Satwik Kottur, Paul A. Crook, Ankita De, Shivani Poddar, Theodore Levin, David Whitney, Daniel Difranco, Ahmad Beirami , Eunjoon Cho, Rajen Subba, Alborz Geramifard
December 08, 2020
December 08, 2020
CONVERSATIONAL AI
NLP
Resource Constrained Dialog Policy Learning via Differentiable Inductive Logic Programming
Motivated by the needs of resource constrained dialog policy learning, we introduce dialog policy via differentiable inductive logic (DILOG).…
Zhenpeng Zhou, Ahmad Beirami Paul Crook, Pararth Shah, Rajen Subba, Alborz Geramifard
December 08, 2020
May 07, 2021
CORE MACHINE LEARNING
THEORY
Tilted Empirical Risk Minimization
Empirical risk minimization (ERM) is typically designed to perform well on the average loss, which can result in estimators that are sensitive to outliers …
Tian Li, Ahmad Beirami, Maziar Sanjabi, Virginia Smith
May 07, 2021
February 14, 2020
RESEARCH
Fair Resource Allocation in Federated Learning
Federated learning involves training statistical models in massive, heterogeneous networks. Naively minimizing an aggregate loss function in such a network may disproportionately advantage or disadvantage some of the devices. In this work, we…
Tian Li, Maziar Sanjabi, Ahmad Beirami, Virginia Smith,
February 14, 2020
