Dr. Weiwei Cheng

A Bar Raiser is a skilled interviewer on the hiring committee who acts as a steward of the interview process. It has three responsibilities: Assess candidates for the specific role and for long-term success; Make sure there is an accurate and fair assessment of the candidate with every member of the interview loop; Guide and help with the preparation of the interview.

Leading research initiatives on machine learning and natural language processing for teams across Amazon including Alexa, search, marketplace

Working on machine learning and natural language processing projects for teams across Amazon including consumer electronics, customer reviews, Kindle

Main research interests: machine learning, data mining, preference learning, ranking, multi-label classification. Working also on project “Learning by Pairwise Comparison for Problems with Structured Output Spaces” funded by German Research Foundation (DFG)

Consulting service for Asian students

Improving machine learning with large-scale ontological knowledge, with applications of tweet classification and movie recommendation.

Analyzing and mining IT-related audit issues. Audit issue categorization is a crucial task of Deutsche Bank that is conducted on a daily basis. It was done manually and was a very time-consuming process. I have developed an automated classification system that is capable to accurately classify a large amount of issues at any moment. For more than 4000 issues, the implemented system needs less than 100 seconds. Such task normally takes a person several months to finish.

Efficient preference operators in very large database systems. Skyline (http://bit.ly/6gpb5y) is a recent proposed preference operator and has gained notable popularity in the database research. The usefulness of the skyline is, however, weakening with respect to an increasing dimensionality. In this work we have proposed a machine learning method to rank the outputs of a skyline, so that the preferences of the users can be represented in a more faithful way.

Geographic information system for electricity distribution management

Artificial intelligence, machine learning, data mining, and pattern recognition. Special interests in: ranking, preference learning, multi-label classification, etc.

Data and Knowledge Engineering. Master’s thesis: Interactive ranking of skylines using machine learning techniques. GPA: 4.0/4.0 (with highest distinction + best graduate award)

Bachelor’s thesis: Retail analysis and development in Zhengzhou

Bachelor’s thesis: Database systems for personnel management