Neural Information Processing
Project Description
Recently, there is a rising interest in applying neural information processing to solve real-life problems and to model how the brain works. We will focus on highway traffic data. It shares a lot of characteristics with granular flows in physics. Large amount of traffic data is available from the Taiwan Highway System, where sensors are installed in highway segments for electronic toll collection. With the help of UROP students, we found interesting patterns of car flux and car density when congestion takes place last year. We will apply our knowledge to the prediction of traveling times under different traffic conditions using neural information processing methods.

If UROP applicants are more interested in fundamental aspects of neural computation, they can discuss with the supervisor about the individual topic. Possible areas include the use of message-passing algorithms for optimization, and the extraction of features in the intermediate layers of the neural network.
Supervisor
WONG Michael Kwok Yee
Quota
4
Course type
UROP1100
UROP2100
UROP3100
UROP3200
UROP4100
Applicant's Roles
The applicants will have chance to process and analyze the traffic data obtained from the Taiwan Highway System, and other similar data sets, and use them to make predictions.
Applicant's Learning Objectives
To extract meaningful descriptive parameters from large volume of data.

To use computational tools to solve real-life problems.
Complexity of the project
Moderate