Computer Science Seminar: “Algorithms in the AI Age: Fair and Learning-Augmented,” Featuring Ali Vakilian

Join us on Monday, January 27, from 12:45–1:45 p.m. at the Stuart Building, room 113, for a Computer Science Seminar titled “Algorithms in the AI Age: Fair and Learning-Augmented” by speaker Ali Vakilian.

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: “learning-augmented” 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 “proportional representation” within clusters or centers and “equitable access” 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.

Biography

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. For more information, visit his website at http://www.mit.edu/~vakilian.