基本信息
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个人简介
The overarching theme of my research interests lies in algorithms, analysis, and applications of machine learning and artificial intelligence to data science. Currently, my research focuses on investigating and developing deep learning models with recurrent neural networks, convolutional neural networks, and generative neural models (VAEs,GANs) to autonomously extract and learn important patterns from massive data. The sheer volume of data can be un-structured, semi-structured, anomalous, decentralized, streaming and multivariate time series, which are collected from various domains ranging from manufacturing, cloud services, economics and finance, healthcare, and natural languages (with semantic patterns extracted from word and sentence levels). I am also particularly interested in graph mining with the focus on developing effective algorithms to discover evolving local subnetwork processes that govern and impact global properties of network instances along with the temporal dimension. Exploring anomalous patterns and clustering structures from complex, high dimensional data using spectral analyzing techniques, non-parametric models, and information theory is also of my research concern. My studies in these areas have been published in most major journals/conferences including: Machine Learning Journal, DMKD, TKDD, SIGKDD, WWW, ICDM, SDM, ICDE, ECML, PKDD, etc.
Specialities: AI and Deep Learning, Machine Learning, Data Mining
Specialities: AI and Deep Learning, Machine Learning, Data Mining
研究兴趣
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arxiv(2023)
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Proceedings of the 2021 International Conference on Management of Data (2021)
2018 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA)pp.3267-3276, (2018)
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D-Core
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