

Shan Jiang


Shan Jiang is a PhD student studying social networking and applied data mining at Northeastern University’s Khoury College of Computer Sciences, advised by Christo Wilson. Previously he received a bachelor’s degree in information management and information systems from Beijing University of Posts and Telecommunications. He is a member of the Social Networks Group and is currently working on a project to understand users’ mobility patterns by analyzing pricing and availability data produced by carpool applications such as Lyft and Uber.
I am a PhD student advised by Christo Wilson. My research tries to explore the social and economic aspects of online systems using data-driven methods.
While smart devices are changing our lives using their powerful algorithms, few have worried of their potential problems: Are they secure? Are they private? Are they discriminative, or even are they reasonable? My research tries to answer those social and economic concerns of current algorithm-driven web services and mobile applications.
I am now trying to analyze availability and pricing data from carpool applications such as Uber and Lyft to understand users’ mobility patterns.
The research project attracts me for its direct application values and its novelty. In a way, our research is real-world data driven, resulting more intuitive and interesting discoveries that can be easily adopted by industry or government; in another, our research is highly interdisciplinary and little has been done before.
I aim to be a research or data scientist in industrial labs, if not in academia.
Shan Jiang is a PhD student studying social networking and applied data mining at Northeastern University’s Khoury College of Computer Sciences, advised by Christo Wilson. Previously he received a bachelor’s degree in information management and information systems from Beijing University of Posts and Telecommunications. He is a member of the Social Networks Group and is currently working on a project to understand users’ mobility patterns by analyzing pricing and availability data produced by carpool applications such as Lyft and Uber.
I am a PhD student advised by Christo Wilson. My research tries to explore the social and economic aspects of online systems using data-driven methods.
While smart devices are changing our lives using their powerful algorithms, few have worried of their potential problems: Are they secure? Are they private? Are they discriminative, or even are they reasonable? My research tries to answer those social and economic concerns of current algorithm-driven web services and mobile applications.
I am now trying to analyze availability and pricing data from carpool applications such as Uber and Lyft to understand users’ mobility patterns.
The research project attracts me for its direct application values and its novelty. In a way, our research is real-world data driven, resulting more intuitive and interesting discoveries that can be easily adopted by industry or government; in another, our research is highly interdisciplinary and little has been done before.
I aim to be a research or data scientist in industrial labs, if not in academia.