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个人简介
My data science team is addressing the fundamental challenges in improving the Amazon product catalog in a (semi-)automated fashion using machine learning. We iterate rapidly using state-of-the-art data science tools and machine learning algorithms on our database with 100's of billions of records describing products and customer shopping behavior. We use a wide range of methods suited to each problem - classification, regression, hypothesis testing, clustering, pattern mining, and deep learning. The kind of projects we take on are high risk and high reward and if successful are deployed to production in collaboration with one of our partnering teams. In addition we generate high quality data products that provide insight into which product features matter most to customers, measure the impact of catalog improvement, and estimate defect rates.
Come join my team if you have experience in machine learning and data mining or want to work with people who do. We have openings ranging from scientist with deep knowledge in machine learning to developers with experience in building systems and an interest in machine learning. Check out our recruiting video below and contact me, if you are interested!
About myself - I am an expert in machine learning and data mining with project and people management experience. I published more than 45 research papers and have given tutorials on temporal data mining at top conferences. My research and practical knowledge spans supervised methods such as predictive modeling, unsupervised methods such as clustering and frequent pattern mining, supervised and unsupervised neural networks, spatio-temporal data mining, forecasting and statistics. I worked on a diverse set of applications including music similarity, gait analysis, news clustering, financial risk scoring, bioinformatics, predictive maintenance, geothermal data, and currently retail.
研究兴趣
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ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Miningno. 3 (2019): 1-27
Lecture Notes in Computer Science (2013): 89-112
KDD (2012): 280-288
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