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个人简介
My research is motivated by challenges in managing and mining massive data sets, networks, and information systems. I am interested in modeling and in designing solutions which enhance performance and functionality. I prefer a principled approach and apply and develop tools from algorithms design, statistics, and optimization. I am always looking to learn more, do more, and expand my horizons.
In the span of my research career, I developed models and scalable algorithms in a wide range of areas including query processing and optimization, content search and delivery, caching, prefetching, routing, streaming, parallelization, and fundamental graph algorithms. More recently, I led the development of highly scalable algorithms for the analysis and mining of massive graphs, such as social and other interaction graphs. My work facilitates the computation of centralities, similarities, and influence measures at a scale and accuracy that were not possible before. I also led the development of sampling, sketching, and estimation techniques which optimize tradeoffs between storage size, computation, and query speed and accuracy. My work enables the leveraging of much larger data sets than previously possible.
研究兴趣
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PROCEEDINGS OF THE 42ND ACM SIGMOD-SIGACT-SIGAI SYMPOSIUM ON PRINCIPLES OF DATABASE SYSTEMS, PODS 2023pp.361-371, (2023)
NeurIPS (2023)
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