Please register here to join us for the CS Colloquium Series 2020-21 via Zoom. Once you register, you will receive a recurring Zoom link. You only need to register once to be able to attend any of the seminars. 

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An important property of those algorithms that are typically used in practice is broad applicability—the ability to solve problems across diverse domains. However, the default, out-of-the-box performance of these algorithms can be unsatisfactory, with slow runtime, poor solution quality, and even negative long-term social ramifications. In practice, there is often ample data available about the types of problems that an algorithm will be run on, data that can potentially be harnessed to fine-tune the algorithm’s performance. We therefore need principled approaches for using this data to obtain strong application-specific performance guarantees.

In this talk, I will give an overview of my research that provides practical methods built on firm theoretical foundations for incorporating machine learning and optimization into the process of algorithm design, selection, and configuration. I will describe my contributions across several diverse domains, including integer programming, clustering, mechanism design, and computational biology. As I will demonstrate, these seemingly disparate areas are connected by overarching structure which implies broadly-applicable guarantees.