INSTRUCTIONS - PROFESSORS: Please print and review this form. Complete or correct the sections, as applicable, from "Supervisor's Name" to "Ethics, safety, and training". Please sign and date near the bottom ("Supervisor's signature").
INSTRUCTIONS - STUDENTS: You may receive this form by email, or you may download it from www.mcgill.ca/science/research/ours/396 after it has been posted. Either way, print and review this form. Complete or correct the sections, from "Student's Name" to "Student's Level", and sign ("Student signature). Ask your supervisor to sign her/his section near the bottom. Take it to the department corresponding to the course number; this may or may not be your own department. Do not register for a '396' course on Minerva until you receive departmental permission. Have a discussion with your supervisor about time/work expectations, keeping in mind that this is a 3-credit course (roughly, 10 hours per week for 12 weeks). Remember that a '396' course is an elective.
INSTRUCTIONS - DEPARTMENTS: After the unit chair/director/designate approves (or not) this project, please notify student. If approved, please give student permission to register on Minerva, and fax this form (with signatures) to the Office for Undergraduate Research in Science at 514-398-8102. The Office for Undergraduate Research in Science will later post the project online at www.mcgill.ca/science/research/ours/396/listing, indicating whether the project is open for students to apply or taken.
QUESTIONS OR FEEDBACK? Contact Victor Chisholm by email, or phone 514-398-5964
Supervisor's Name: Derek Ruths
Supervisor's Email: derek.ruths [at] mcgill.ca
Supervisor's Phone: 514-398-7079
Supervisor's Website: http://www.ruthsresearch.org
Supervisor's department: Computer Science
Course number: COMP 396 (Computer Science)
Term: Fall 2011-2012
Project start date: Wednesday, September 1, 2011
Project end date: Tuesday, December 6, 2011
Project title: The Utility of Neighbor Properties in Demographic Inference
Project description: In this project, the student will evaluate the extent to which attributes of a Twitter user's neighborhood provides signal which can be used to infer specific demographic features of the Twitter user. These features will include gender and political orientation. As part of this project, the student will participate in data set design and construction, training machine learning algorithms on the data sets, and evaluating the ML algorithm performance.
Prerequisite: 1 term completed at McGill + CGPA of 3.0 or higher; or permission of instructor.
Grading scheme (The final report must be worth at least 50% of final grade): Final grade shall be based on research performance as evaluated by the research supervisor (50%) and the final written research report (minimum 10 pages) graded by the supervisor and the course coordinator or the coordinator's delegate (50%)
Other project information:
Project status - This project is: Taken; however students may contact the professor to discuss other possible '396' projects this term.
How students can apply: N/A; this project is filled.
Ethics, safety, and training: Supervisors are responsible for the ethics and safety compliance of undergraduate students. Which of the following, if any, is involved? None of the above (NEITHER animal subjects, nor human subjects, nor biohazardous substances, nor radioactive materials, nor handling chemicals, nor using lasers)
Student's McGill ID:
Student's Email (first.last [at] mail.mcgill.ca):
Student's Level (U0 / U1 / U2 / U3):
Student's signature - I have not applied for another '396' course in this term:
Supervisor's signature - I give my permission for the student identified above to register for this project under my supervision:
Unit chair/director/designate's name:
Unit chair/director/designate's signature - I certify that this project conforms to departmental requirements for 396 courses: