Adaptation is at the heart of graduate student Alireza Fallah’s research. Currently in his fourth year at MIT, he received his master’s degree in 2019 and is continuing for his doctorate, finding a way forward despite the significant alterations to study, work, and life caused by the COVID-19 pandemic. Supervised by Prof. Asu Ozdaglar in the Electrical Engineering and Computer Science Department, he came to LIDS after getting his Bachelor of Science in electrical engineering and mathematics from Sharif University of Technology in Tehran, Iran. He chose MIT because of the lab’s stellar reputation.
Alireza’s main interest is optimization. “What I have been working on recently is characterizing the theory of optimization algorithms used in various machine learning problems, in particular in meta learning and federated learning,” he explains. “In machine learning, the idea is you have some data set and you’re trying to train the model so that it works well on potentially unseen data…[but] the idea of meta learning is to train a meta model for lots of tasks so that it can be updated and then used for a specific task.”
In his recent work, Alireza has been designing an algorithm that takes advantage of the benefits of federated learning while keeping the personalization that meta learning can provide. He and his colleagues have found that taking a meta-learning approach to federated learning can give crucial and timely personalization.
Because meta learning is still in the early stages of research and development, there’s a lot of theory to explore in the search to discover why and how it works. “This is a challenge for me, to complete the piece of the puzzle that is missing,” Alireza says. “And I like the problem also because it has its own challenges, it’s not straightforward, but it’s not impossible to tackle. And at the same time, you get something that you can use.”
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