In theoretical computer science, algorithm design is centered around worst-case optimization, where results focus on provable guarantees for how well an algorithm performs on its worst input. For example, if the objective of an algorithm is to complete a task as quickly as possible, then we measure how quickly an algorithm runs on its most time-consuming input. While this has led to deep structural insights in important problems, many of these algorithms are not useful in practice. More problematically, researchers have been disincentivized to understand more about problems whose worst-case is well-understood. I plan to shift my attention for my post-doctoral work to designing algorithms that go beyond worst-case analysis for scheduling and resource allocation problems.
|Effective start/end date||9/1/21 → 8/31/23|
- Computing Research Association, Inc. (2021 CIF-Northwestern-10//2127309)
- National Science Foundation (2021 CIF-Northwestern-10//2127309)
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