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个人简介
I am broadly interested in the topic of large-scale graph analysis. My current research involves developing scalable graph AI models for real-world applications. I also work on designing efficient and practical graph algorithms with theoretical guarantees in various Big Data models such as the streaming/distributed models. Over the past few years, I have enjoyed working on a diverse range of topics including algorithmic fairness, scalable clustering, communication complexity, arithmetic circuit complexity, and approximation algorithms. My research has been published at premier theoretical computer science conferences such as SODA, ITCS, ICALP, etc. as well as at top-tier ML/data mining conferences such as NeurIPS, KDD, VLDB, etc.
研究兴趣
论文共 25 篇作者统计合作学者相似作者
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arxiv(2022)
arxiv(2022)
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arXiv: Data Structures and Algorithms (2020): 21
arxiv(2020)
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