Instructor: Dr. Thuan Nguyen, Ph.D.

This class is designed to empower students with the skills and confidence to do academic research, culminating in completing an original research paper. This course emphasizes self-motivation, critical thinking, and the ability to effectively synthesize and explain complex ideas. The student will work closely with the faculty advisor, who will provide guidance and support, but the primary responsibility for defining the research questions, designing the methodology, and executing the research plan rests with the student. The culmination of the efforts in this class will be a research paper that demonstrates the mastery of research methods, the ability to analyze and interpret data, and the capacity to articulate findings clearly and compellingly. The research paper is not merely an assignment. It is a testament to the student’s intellectual growth and readiness for further accomplishments in academic research.

Course Objectives
  • Students will have opportunities to learn and research cutting-edge AI technologies.
  • Students will have opportunities to do AI research under the faculty advisor’s guidance and supervision.
  • Each student will complete one research (either individually or in small groups of 2 or 3 students).
Course Information

3.1 Academic Type and Format

AI Independent Research (ADTA 5900) classes are full-semester long (16 weeks), in-person, and letter-graded.

The class can be used as an elective to complete the MS in Advanced Data Analytics program requirements.

Additional Information:

  • A selected student can continue doing research on the subject after the semester if they choose to do so. However, only one class is counted in the program requirements.
  • One 2-hour F2F class meeting weekly, with flexible schedules, either on the Denton or Frisco campus, mainly on Friday. Attendance is mandatory.

3.2 Academic Activities: Weekly Research Reports

A student working on independent research must provide weekly reports that include:

  •  A weekly written status report via email by 5:00 PM every Friday. The weekly status report should include details of the work they have accomplished for the week and provide information about activities they will do the following week.
  •  A copy of the in-progress research paper via email by 5:00 PM every Friday, if available.
Course Requirements

To be considered for the AI Independent Research (ADTA 5900) class in the next Fall or Spring semester, the student should meet the following requirements:

  • Have a strong interest in doing AI research
  • Have a clear intention to apply the latest AI technologies to a practical field, e.g., business, information, education, engineering, sciences, etc.
  • Possess strong skills and knowledge in Python programming
  • Be able to work independently
Application and Selection

At most, five students will be selected for the AI Independent Research class in a Fall or Spring semester.

Students must apply for the class by filling out and submitting a web form (MS Word) at this link.

The application deadline for the Fall semester is July 15 and November 15 for the Spring semester.

If the student has been selected for an in-person interview, they will be contacted via email. Then, after the interview, the faculty advisor will inform the student about the final decision.

Additional Information:

  • When a student applies for a position in the AI Independent Research class, their name is entered into a candidate bank.
  • They will receive an offer email if selected for an in-person interview for the class next semester.
  • Otherwise, their name is still saved in the candidate bank, and they will be considered for the future selections. The student does not need to reapply or contact the ADTA Advising Office about the selection results, especially when the semester starts, and the advisors have a very high call volume.

If you are interested in applying for the course, you can fill out and submit the application HERE.

Questions? Please reach out to our academic advisors by emailing analytics@unt.edu.