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个人简介
My research goal is to design, analyze, and implement novel machine learning algorithms that take advantage of modern hardware to enable learning on and mining of massive datasets. With the increasing availability of many-core processors, general-purpose graphics processing units, and solid-state drives, we are witnessing a hardware revolution. Existing machine learning algorithms do not take advantage of these emerging computing paradigms and hence do not scale to the increasingly common massive, distributed datasets. To develop the next generation of machine learning algorithms, we need to develop new models as well as new systems-aware, efficient, scalable optimization algorithms. Four major deliverables of my research agenda include: (1) new models for learning from massive datasets (2) new optimization algorithms--specialized for machine learning--that can exploit recent advances in hardware, (3) novel theoretical analysis for the new models and optimization algorithms, and (4) open-source tools for large scale machine learning. In addition, I am committed to dissemination of these ideas via publications, lectures at machine learning summer schools, and graduate and undergraduate level classes.
研究兴趣
论文共 143 篇作者统计合作学者相似作者
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PAKDD (3)pp.73-85, (2023)
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Vihan Lakshman,Choon Hui Teo, Xiaowen Chu,Priyanka Nigam, Abhinandan Patni, Pooja Maknikar,SVN Vishwanathan
arxiv(2021)
arxiv(2020)
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PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19) (2019): 379-388
KDD'17: PROCEEDINGS OF THE 23RD ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (2018): 1813-1821
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