440 Huntington Avenue
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Le Chen is a PhD student studying computer science at Northeastern University’s College of Computer and Information Science, advised by Professor Christo Wilson. He received his Bachelor of Engineering from the School of Information and Communication Engineering at Beijing University of Posts and Telecommunications in 2010. Prior to joining Northeastern, he worked at the China International Trust and Investment Company (CITIC) Telecom International from 2010 to 2011.
His primary research interest is mining information from large-scale Web services. Much of his work focuses on data analytics and applied machine learning. In particular, he has leveraged the findings in his work to improve security, fairness and algorithm performance in sharing economy, security and privacy, and online social networks. His thesis topic is investigating fairness, personalization, and economic practice of online pricing algorithms.
- BEng, Beijing University of Posts and Telecommunications – China
- Hometown: Guiyang City, China
- Field of Study: Computer Science
- PhD Advisor: Christo Wilson
What are the specifics of your graduate education (thus far)?
I study computer science fundamentals such as advanced algorithms, system designs, computation theories, and programming language principles from the PhD core courses. For the elective courses, I dive into networks, data mining and machine learning, which form the building blocks of my current research path.
What are your research interests?
My current research interest started during my 2nd year in Northeastern University’s College of Computer and Information Science
What’s one problem you’d like to solve with your research/work?
At present, almost all of the services that people encounter have background algorithmic settings. For example, Google algorithmically decides the search result you see, Amazon leverages algorithms to decide the price and seller when you shop on their marketplace, Uber calculates the surge multiplier for you if there is a demand/supply imbalance, etc. My mission is to understand how these impactful algorithms work in order to insure they work fairly, securely, efficiently and effectively.
What aspect of what you do is most interesting?
The most exciting and challenging part of my research is to find valuable pieces from an enormous dataset. A huge dataset usually has many variables, so there is an exponential number of ways to explore the data. As a result, designing experiments and algorithms to find a short path to the valuable pieces is fun and exciting. Collecting data (especially those hard-to-get ones) in the first place is also exciting.
What are your research/career goals, going forward?
My long-term goal is to become a research leader in a tech company tackling real problems that impact millions of users on a daily basis.