1. What is the goal of the course?
The goal of the course is to help students develop new research ideas, familiarize and experiment with techniques, tools, platforms and datasets and deliver an academic research paper. Perform the entire cycle from selecting a research topic, articulate a specific research question, put together a plan (Research Proposal), follow through (e.g. data collection and analysis, system design and evaluation, etc.) and finally deliver the results through an academic paper.
2. What is a detailed list of topics and papers covered in the course?
Please see here (Summer 2025 syllabus) our latest syllabus updated with the full list of papers and topics covered each week. (Overall course information is available here).
3. Are there any course prerequisites or background knowledge I should have prior to taking this course?
There are no course prerequisites.
This is a project-based course. If your project involves using a specific tool or platform, then it helps to be familiar with that tool or platform beforehand. E.g., if you plan on applying ML techniques to analyze a dataset, then it helps being familiar with these techniques beforehand.
If you plan on writing a Systematic Literature Review, then thre is no coding involved.
4. Does the course count towards a specialization?
As of now – Summer 2025 – it counts as a free elective.
5. Can I pursue my own ideas beyond the topics covered in the list?
Yes.
The list of the research areas we have in the syllabus are the areas that we cover with lectures-papers presentations. This list only serves as a starting point. The students are welcome and highly encouraged to branch out and explore from there, cutting across traditional boundaries.
6. What types of papers/research do students perform?
There are different types of publications – from short papers to full papers. Also, there are different “avenues” to explore a research question.
You’re encouraged to shape your project based on your background, interests, and goals. Here are some common approaches you can take:
- Literature Review: Conduct a systematic review of existing research – typically does not involve coding.
- Survey Study: Design and distribute a questionnaire and analyze the results to uncover trends or patterns.
- Data Analysis: Collect your own dataset or use a public one to perform exploratory or in-depth analysis.
- Learning Techniques: Apply, evaluate, or even design an AI/ML method for a dataset related to a topic.
- System Design: Build and evaluate a system (e.g., a tool, pipeline, or framework) that tackles a specific problem.
- Replication Study: Reproduce and reassess results from a previously published paper—this could include publishing new datasets, re-running experiments, or testing under different conditions, etc.
- Prototype & White Paper: Design and build a tool through a prototype that shows the core functionality and write a white paper (typically short) that explains the main technical aspects.
7. What is the workflow of the course?
The assignments in this course are designed to walk the student through the full research process through a step-by-step approach, with guidance and deadlines.
- We start with brainstorming assignments.
- The brainstorming assignments lead to clarifying the main research question, which is a more detailed write-up of the problem and the related work.
- We set up GitHub and Overleaf projects.
- Then we turn the Research Question into a detailed Proposal – technically a roadmap to the paper which includes a rough skeleton of your paper and technical approach for each section).
- Then we work through three major research milestones, where you add results and progress to your paper draft.
- Along the way, there are weekly check-ins, to help stay on track and problemsolve technical challenges coming up.
- At the end, the student puts everything together into final deliverables: project code, the paper, and a recorded presentation.
In this course, active participation with classmates is highly encouraged, as this enhances the course experience and strengthens the quality of the final paper. We have weekly discussions on EdStem where students provide preliminary feedback on each other’s topics, (that counts as students’ participation grade).
10. What type of support do students receive from the instructor?
The instructor and the TA team meets with the students individually (or as a group if they are working as a group) on a weekly basis to provide guidance through all steps of the research cycle.
11. How many hours do students typically devote to this project?
It depends on the specific project you choose to work on and how you design/approach it.
For example:
- Are you working individually or as a member of a group? – Is your project a Systematic Literature Review (which typically doesn’t involve coding), or does it include tasks e.g. data collection, analysis, building an ML/AI pipeline, evaluation, etc.?
- Are you collecting your own dataset or working with publicly available data?
- If you’re building a system, how complex is it—what components does it include?
- What’s your level of familiarity with the tools or frameworks you’ll be using?
12. If I don’t come in with my own idea, does the course provide a list of ideas I can start from?
Yes, you will have access to suggested research ideas to get inspiration from.
Also, as you start putting together the brainstorming write up (first assignment), we will be meeting with you to help you through that process.
13. What are example research projects/areas the students have worked on?