Computer Science Seminar by Ali Vakilian-"Algorithms in the AI Age: Fair and Learning-Augmented"
Title: Algorithms in the AI Age: Fair and Learning-Augmented
Abstract: The widespread deployment of AI across diverse applications offers promising opportunities while raising significant societal concerns. Motivated by these opportunities and challenges, this talk will focus on two key research directions at the intersection of algorithms and machine learning: 鈥渓earning-augmented鈥 algorithms and fairness in algorithms and machine learning.
The increasing reliance on automated decision-making in high-stakes domains such as hiring and criminal justice has led to substantial research on the societal and ethical implications of algorithms and machine learning. In particular, fair clustering has emerged as a critical area of interest in recent years. I will discuss my work on designing efficient clustering algorithms, a fundamental task in machine learning, under various fairness notions, including 鈥減roportional representation鈥 within clusters or centers and 鈥渆quitable access鈥 to facilities.
In the domain of learning-augmented algorithms, the goal is to leverage patterns in data to enhance the performance of classical algorithms. This approach offers a dual promise: when provided with accurate machine-learned predictions about the input, it outperforms classical algorithms, while still maintaining strong worst-case guarantees even if the predictions are adversarial. I will outline my contributions to this field, which initiated the study of learning-augmented algorithms in the streaming model and introduced such algorithms for fundamental problems, including frequency estimation and low-rank approximation.
Bio: Ali Vakilian is a Research Assistant Professor at TTIC. His research interests include fairness of algorithms and machine learning, learning-augmented algorithms, and algorithms for massive data. Ali received his Ph.D. from MIT EECS, where he was advised by Erik Demaine and Piotr Indyk. He completed his MS studies at UIUC where he was a recipient of the Siebel Scholar award. He is a recipient of the Outstanding Student Paper Highlight Award at AISTATS 2024.
In the domain of learning-augmented algorithms, the goal is to leverage patterns in data to enhance the performance of classical algorithms. This approach offers a dual promise: when provided with accurate machine-learned predictions about the input, it outperforms classical algorithms, while still maintaining strong worst-case guarantees even if the predictions are adversarial. I will outline my contributions to this field, which initiated the study of learning-augmented algorithms in the streaming model and introduced such algorithms for fundamental problems, including frequency estimation and low-rank approximation.
Bio: Ali Vakilian is a Research Assistant Professor at TTIC. His research interests include fairness of algorithms and machine learning, learning-augmented algorithms, and algorithms for massive data. Ali received his Ph.D. from MIT EECS, where he was advised by Erik Demaine and Piotr Indyk. He completed his MS studies at UIUC where he was a recipient of the Siebel Scholar award. He is a recipient of the Outstanding Student Paper Highlight Award at AISTATS 2024.