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Bingyu Wang

PhD Student

Bingyu Wang

Contact

Office Location

440 Huntington Avenue
472 West Village H
Boston, MA 02115

Biography

Bingyu Wang is a PhD student in the Computer Science program at Northeastern University’s College of Computer and Information Science, advised by Professor Javed Aslam. Bingyu earned his bachelor’s degree in engineering from Northwest University, in China, and he earned his master’s degree in computer science from Northeastern University.

Bingyu arrived at Northeastern in the spring of 2015 to begin his PhD program. He has been involved in a Multi-label classification problem of machine learning for more than half a year. Bingyu’s research focuses on machine learning and document classification. His research areas also include natural language processing, information retrieval, and software engineering. He is a part of the Applied Machine Learning Group at Northeastern.

Education

  • MS in Computer Science, Northeastern University
  • BS in Engineering, Northwest University – China

About Me

  • Hometown: China
  • Field of Study: Computer Science
  • PhD Advisor: Javed Aslam

What are the specifics of your graduate education (thus far)?

My PhD program started in the spring of 2015. Currently, I am taking PhD required courses. Besides, I have been involved in a Multi-label classification problem of machine learning area for more than half year.

What are your research interests?

My undergraduate major was software engineering, which is to design, implement and maintain programs to solve the problems in general. To further improve myself, my PhD research focuses more on a particular area: machine learning/document classification.

As before, I apply the same idea as a software developer and try to design, implement, and test machine learning models to solve real-world problems.

What’s one problem you’d like to solve with your research/work?

Currently, I am working on a Multi-label classification problem, which is also the one I want to solve.

What aspect of what you do is most interesting?

For the current Multi-label problem, we came up with a very simple idea, but it works much better than most existing methods.

What are your research or career goals, going forward?

Since data is becoming more and more valuable, I hope to use machine learning to make data even more valuable.