Research

Multimedia Big Data Mining and Deep Learning

Research areas: Data Science, Machine Learning, Deep Learning

Published 20+ high quality papers on multimedia big data mining and deep learning

Integrated a bootstrapping approach for imbalanced data classification problem in neutral networks
Proposed an idea to feed low-level features to CNNs for saving training time
Proposed a scalable classifier ensemble framework assisted by a set of judgers to integrate the outputs from multiple classifiers for multimedia big data classification on Spark
Explored and utilized inter-concept correlations among concepts for information retrieval
Engaged in several different directions in big data analysis and proposed some useful ideas to help other researchers and enhance the performance of neutral networks

Built an experimental big data analytics environment

Installed Hadoop on one master node and two slave nodes, with two works on each node
Installed a NoSQL database, HBase and stored multimedia big datasets like images
Deployed Spark on the environment, which can run in standalone or yarn model
Used Caffe to exact features in different layers, tune existing models, and classify images
Used Theano to train different kinds of neutral networks with different configurations
Deployed DL4J on Spark to effectively train neutral networks by multiple workers
Tested most popular deep learning tools and fed them with different types of data

Developed a website for concept detection and information retrieval using big data techniques

Research areas: Image Processing and Computer Vision

Research areas: Web Log Management and Software Development

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