About ac297r

Computational Science and Engineering CAPSTONE PROJECT

The CSE Capstone Project course is intended to integrate and apply the skills and ideas in computational and data science that students acquire in other courses, such as data management, machine learning, statistics, and visualization.

By requiring students to complete a substantial and challenging collaborative project, the Capstone course will ensure that students are trained to conduct research and prepared for the professional world. The projects will be selected to combine the statistical, computational, and engineering challenges and social issues involved in solving complex real-world problems.

Students will be placed in groups of three to four and each group will work with the instructor, mentors and partners to identify a complex, open-ended real-world problem. Our partners are from academia, government, and the e-commerce, medical and financial industries. The student groups will understand and define the overall problem, and propose a solution. These solutions will be either in the form of a software package, a set of recommendations, or a research paper.

Students will go through the entire cycle of solving a problem in a team:

  • acquiring, organizing and processing data
  • creating and outlining solutions
  • implementing those solutions
  • communicating and defending their work

A very important component of the capstone project will be the significant feedback provided by the instructors to the students. Students will be given explicit measures for evaluation, regular feedback on all aspects of the project and its implementation, as well as opportunity for self-assessment. This will begin with the data acquisition and data exploration and extend to the design and implementation phase of the project. Students will be required to give bi-weekly updates and to attend weekly face-to-face review sessions with the instructor and/or mentors. This continuous open dialogue will ensure the achievement of the learning outcomes.

Examples of Projects

  • Predicting the number of travellers on the T from the MBTA's fare collection data
  • Predicting the spread of an epidemic from social media.
  • Correcting for the motion of a patient by matching between MRI images