Sonification Lab Research and Design Studio:
Multimodal User Interfaces, Displays, and Applications

Home Page & Syllabus (Updated January, 2024)

NOTE: If you wish to register for CS8803 SRD, please email Dr. Walker with (1) your resume; and (2) any preferences for projects or types of activities that you would prefer to work on. Dr. Walker will add you to his off-line waitlist, and let you know if/when you are able to register.


Bruce N. Walker


Room 230, Psychology Building (Coon Building)


(404) 894-8265


Course web page:

Office Hours:

After class and by appointment



8-9:15am, Thursdays only, throughout the semester


Virtual Meetings ONLY, via MS Teams (link below)

In-person meeting (only when required) in JS Coon Psychology Building
Room 217
Note that Oscar may report a different room.

MS TEAM for class:

Team Code: s2bivwx

FINAL PRESENTATION (via TEAMS, in final exam period):

Use same Teams meeting link we use for the regular Thursday check-ins. See above.

Course Description

The Sonification Lab Research and Design Studio is a semester-long, 3-credit course, which aims to bring together small, agile, interdisciplinary teams of students to tackle research and design challenges related to the research going on in the GT Sonification Lab. This often involves VR, AR, sonification, driving research, assistive technologies, and multimodal, not-traditional user interfaces.

Teams of two students (with a mix of skills and background) will work on a semester-long project that is part of, or in support of, the research being done in the Sonification Lab.

The approach is a sort of "middle ground" between (a) a typical semester-long project course, in which typically teams of four students tackle a major project; and (b) an independent study where one student interacts directly with a professor and does a project alone. The R&D Studio approach, on the other hand, allows for more students to get research experience, without 10x the overhead for the professor that would come from a bunch of 8903 or similar special projects. Also, the studio course format allows us to teach some material that is not usually covered in either a research project-only course, or a typical class.

Class Meetings/Lab Time (see above for the exact day/time)

There are typically 8-12 teams signed up for the course in a given semester, and all of the students will come together for one required meeting per week (i.e., if there were 8 pairs, then all 16 people would show up at the scheduled meeting time). During that weekly group meeting, there will be regular check-ins, in which the teams will briefly present what they have been working on, what progress they have made, what challenges or successes they have had, and what they will be doing for the next couple weeks. There will also be periodic lectures on background topics, demos or lab-like explanations of research or development techniques, or discussions of project issues. Topics will be relevant to HCI, Human Factors, User Experience, research, etc., and meetings will be led by one of the students in the course, and/or a Sonification Lab graduate student, and or a researcher or the instructor (Prof. Walker). These skills will be useful in completing the research/development project, but also generally useful in your career. They will complement the skills and topics being covered in other coursed like CS/PSYC 6750 Intro to HCI, CS/PSYC 6755 HCI Foundations, or PSYC 6023 Research Methods for HCI courses.

Project Structure

The team will work directly with a PhD student and/or research scientist, but also closely with Professor Walker. The team will be required to thoroughly define the problem, to a deeper level than the topic is specified at the start of the semester. They will then search for prior work, related projects, products, even patents, before moving ahead. Often there will be prior work (and perhaps code) already within the Sonification Lab archives. The team will then carefully and thoroughly scope out the project and put together a proposal document that describes their blend of research and development, including a thorough plan for evaluation and iteration, and the division of effort among the team members. They will then work on completing the project.

Deliverables and Milestones

The main deliverable in the course is the completion of the project that you set out to do! In order to help ensure a successful project, there will be several milestones along the way:

  1. Project Proposal, including a plan to divide up the work amongst the two students, an overview of what has already been done, and what the steps will be;
  2. Draft Tech Report, describing the background, the current project, and the progress made;
  3. Page describing the project, with links to video(s), images, prototypes, research papers, etc.;
  4. Video of the project (brief, 1-2 minutes);
  5. Final Tech Report, with a comprehensive report of teh work, including code, prototypes, data, analyses, conclusions, next steps, etc.
  6. Final Project Presentation (in final exam slot)
  7. All code uploaded to in an appropriate repository
  8. All project files, source files (e.g., Blender files) uploaded to MS Teams team for the CS 8803 SRD, in a suitable channel

Note 1: In addition to these milestones and deliverables, each semester one (or sometimes two) very promising projects will be invited to present their work during the GVU Demo Day, in the Sonification Lab display area in TSRB.

Note 2: It is required that all code, designs, electronic files, scripts, sketches, etc. be archived in the Sonification Lab archives (GitHub, DropBox, etc.). This is not factored into the points total, but students will not pass the course unless and until their project is archived.


Since this is a graduate-level course, it is assumed that students are approaching it in an adult manner, and will complete excellent projects, and not worry about grades. Having said that, grades will be assigned based on a cummulation of points. It is intended and expected that the team will work together, and share the work. It may not be a completely 50-50 split in terms of hours, but there needs to be an equitable division of effort. This must be agreed upon in the Proposal document. At the end of the semester, the team (as a pair) will receive a total score for the project; in most cases, the score will be turned directly into a final grade for each student. However, each student, as well as the supervising students, researchers, and professor will determine if the final, actual division of effort was substantially different from the proposed work plan. If you work as a team, and complete a solid project, you will both get high grades. Social loafing (letting the other student do all the work, flaking out, etc.) will be severely penalized, and could result in one student getting a much lower final grade than the other. However, we hope that will never happen. Right?! ALSO: Attendance in the weekly meeting/check-in is mandatory, and up to one full letter grade may be deducted from the final grade for poor attendance.


The efforts in this Research and Design Studio course will live on in the research of the lab. Other student teams may carry on or iterate, or extend your work in a later semester. Or the system you create may be deployed in a classroom, or used in a dissertation, or whatever. It may also spark a larger project or even a proposal for grant funding. You have to anticipate that you are part of a larger effort, and conduct your work accordingly. This includes, for example, providing all the iterations and drafts, and all the code (commented!!) and electronic files, so that the next team can pick up your work and run with it. We will require that resources are appropriately archived (code in the GT GitHub repository, other documents and data archived in Dropbox, etc.). Note that you may choose to do a follow-on project, yourself. This might be through another SonLab R&D Studio in a later semester, or by moving into a Masters Project or Masters Thesis, or even a project in another class.

Project Ideas

There will be a list of projects that are being "sponsored" (suggested, supervised) in a given semester. See separate document, via link at the top of this page. You may also propose your own project idea by discussing it with Prof. Walker before the start of the semester.

Students with Disabilities

Students needing accommodations must provide me with the Georgia Tech ADAPTS letter describing accommodations. I also ask students to email me one week prior to any exam if they plan on using testing facilities at the ADAPTS office. Further information can be obtained from the ADAPTS office (894-2564).

Academic Integrity

All students are assumed to have read the Honor Code and consented to be bound by it. Violations of the Honor Code are taken extremely seriously and will result in a failing grade for the course and referral to the Dean of Students for further action. Specific violations include (but are not limited to):

I will assume that all students enrolled in the course know and understand what constitutes academic misconduct and agree to be bound by these rules.

Policy on Use of Generative AI for class work

In this class we treat AI-based assistance, such as ChatGPT and Copilot, the same way we treat collaboration with other people: for both individual and team-based assignments, you are welcome to talk about your ideas and work with other people, both inside and outside the class, as well as with AI-based assistants.

However, all work you submit must be your own. You should never include in your assignment anything that was not written directly by you without proper citation (including quotation marks and in-line citation for direct quotes).

Including anything you did not write in your assignment without proper citation will be treated as an academic misconduct case. If you are unsure where the line is between collaborating with AI and copying AI, we recommend the following heuristics:

Heuristic 1: Never hit "Copy" within your conversation with an AI assistant. You can copy your own work into your own conversation, but do not copy anything from the conversation back into your assignment.

Instead, use your interaction with the AI assistant as a learning experience, then let your assignment reflect your improved understanding.

Heuristic 2: Do not have your assignment and the AI agent open at the same time. Similar to the above, use your conversation with the AI as a learning experience, then close the interaction down, open your assignment, and let your assignment reflect your revised knowledge.

This heuristic includes avoiding using AI directly integrated into your composition environment: just as you should not let a classmate write content or code directly into your submission, so also you should avoid using tools that directly add content to your submission.

Deviating from these heuristics does not automatically qualify as academic misconduct; however, following these heuristics essentially guarantees your collaboration will not cross the line into misconduct.