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Research Theme:
All scientific and social disciplines are faced with an ever-increasing demand to analyze datasets that are unprecedented in scale (amount of data and its dimensionality) as well as degree of corruption (noise, outliers, missing and indirect observations). Extraction of meaningful information from such big and dirty datasets requires achieving the competing goals of computational efficiency and statistical optimality (optimal accuracy for a given amount of data). My research goal is to understand the fundamental tradeoffs between these two quantities, and design algorithms that can learn and leverage inherent structure of data in the form of clusters, graphs, subspaces and manifolds to achieve such tradeoffs.
Additionally, I am investigating how these tradeoffs can be further improved by designing interactive algorithms that employ judicious choice of where, what and how data is acquired, stored and processed. The vision is to introduce a new paradigm of intelligent machine learning algorithms that learn continually via feedback and make high-level decisions in collaboration with humans, thus pushing the envelope of automated scientific and social discoveries.
All scientific and social disciplines are faced with an ever-increasing demand to analyze datasets that are unprecedented in scale (amount of data and its dimensionality) as well as degree of corruption (noise, outliers, missing and indirect observations). Extraction of meaningful information from such big and dirty datasets requires achieving the competing goals of computational efficiency and statistical optimality (optimal accuracy for a given amount of data). My research goal is to understand the fundamental tradeoffs between these two quantities, and design algorithms that can learn and leverage inherent structure of data in the form of clusters, graphs, subspaces and manifolds to achieve such tradeoffs.
Additionally, I am investigating how these tradeoffs can be further improved by designing interactive algorithms that employ judicious choice of where, what and how data is acquired, stored and processed. The vision is to introduce a new paradigm of intelligent machine learning algorithms that learn continually via feedback and make high-level decisions in collaboration with humans, thus pushing the envelope of automated scientific and social discoveries.
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论文共 389 篇作者统计合作学者相似作者
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CoRR (2023)
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CoRR (2023): 4963-4985
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arxiv(2023)
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arxiv(2022)
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2021 55th Asilomar Conference on Signals, Systems, and Computers (2021): 464-470
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