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Bio: I am a postdoctoral researcher in the Machine Learning Foundations group at Microsoft Research Redmond. My research interests are in designing algorithms for massive datasets and large-scale machine learning, especially in the contexts of high-dimensional metric data, fast linear algebra, and learning on data streams. My recent work is focused on harnessing the power of big data and machine learning to guide us toward better algorithm design. I received my PhD from the EECS department at MIT in September 2020, and have spent time as a research intern at Microsoft, Amazon and VMware.

Talk Title: On the Role of Data in Algorithm Design

Talk Abstract: Recently, there has been a growing interest in harnessing the power of big datasets and modern machine learning for designing new scalable algorithms. This invites us to rethink the role of data in algorithm design: not just the input to pre-defined algorithms, but also a factor that enters the algorithm design process itself, driving it in a strong and possibly automated manner. In this talk, I will describe my work on data-driven and learning-based algorithms for high-dimensional metric spaces and nearest neighbor search. In particular, I will show that data-dependence is necessary for optimal compressed representations of high-dimensional Euclidean distances, and that neural networks can be used to build better data structures for nearest neighbor search.