I am a PhD student in Social & Engineering Systems and Statistics at MIT working in the Clinical Machine Learning group led by David Sontag. My research focuses on building methods to improve Human-AI interaction by combining machine learning and HCI techniques. I have developed new methods that allow AI classifiers to know when to defer to humans and when to make decisions on their own (learning to defer). I have created onboarding methods to allow human decision makers to know when to effectively use AI classifiers. I’ve worked on understanding how programmers interact with Copilot and how to improve that experience.
I received my undergraduate degree in computer engineering from the American University of Beirut in 2019. In the summer of 2022 I was a resarch intern in the HAX team at Microsoft Research working on Copilot, in the summer of 2021 I interned at ASAPP where I worked on how customer service agents interact with AI suggestions. You can reach me at mozannar@mit.edu
We provide algorithms that can provably minimize the learning to defer objective and provide an experimental benchmark to study human-deferral algorithms.
We study how programmers interact with the AI code-recommendation system Copilot and develop a taxonomy of programmer activities. We also predict programmer acceptance of AI code-recommendations.
We characterize theoretically the gain from building ML classifiers that complement humans, show how do this by reducing any multiclass loss to a cost-sensitive loss and create human-label efficient algorithms based on active learning.