Building it is undergraduate. Showing it is better is graduate
Graduate computing assessment moves from whether your implementation works to whether you can evaluate it against alternatives and say what the result means. That is an experimental and writing skill, and it is rarely taught explicitly.
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Evaluation design, and reading papers critically
A graduate project needs a baseline, a fair comparison and a measure that answers the question. Students frequently compare against a weak baseline, or on a dataset that suits their method, and a reviewer notices immediately. Designing the evaluation before building is what prevents it.
Reading papers is the other shift. Undergraduate reading is for information; graduate reading is for critique. What did they actually show, what does the evaluation support, and what did they quietly not test. Most students arrive able to summarize a paper and not to assess one.
Then the writing itself. Technical papers have conventions about structure, about how results are presented, and about how strongly claims may be stated. Learning those conventions is worth doing deliberately rather than by imitation, because imitation tends to copy the surface and miss the reasoning.
- Evaluation designed before implementation starts
- A baseline that is actually competitive
- Papers read critically rather than summarized
- Claims stated at the strength the evidence supports
What we work on
Your own coursework and your own project
- Designing an evaluation that answers your question
- Technical paper structure and conventions
- Critical reading and literature review for computing
- Thesis or capstone project scoping and writing
- Presenting results honestly, including negative ones
We explain. You build and write
- We do not write code, papers or theses you will submit.
- We do not run your experiments or produce results.
- We do not fabricate data or performance figures.
- We do not complete assignments or supply solutions.
- We teach the methods and review what you produced.
In computing, reporting benchmark figures you did not obtain is fabrication and is treated far more seriously than a citation error. If a result is disappointing, the honest write-up is the better outcome. Read the full policy.
Frequently Asked Questions
Something a reasonable person would actually use for this problem, ideally a published method on the same data. Comparing against a deliberately weak baseline is the fastest way to lose a reader's trust, and reviewers and graders both look for it.
Ask what claim is being made, what evidence supports it, and what the evaluation does not cover. Look at the datasets, the baselines and whether the ablations isolate the contribution. Summarizing what the paper says is the undergraduate version of this exercise.
Report it and analyze why. A negative result with a clear explanation of the conditions under which the method does and does not help is a legitimate contribution. Tuning until you find a favorable configuration and reporting only that is the actual problem.
A thesis situates itself in a literature, states a contribution, and evaluates it against what already exists. A project builds something. Many MS programs offer both routes, and the writing demands are quite different, so it is worth knowing which you are on.
We explain concepts and work through your own code with you. We do not write code you submit, in undergraduate or graduate work. The evaluation design and the writing are usually where graduate students most need help anyway.
Tell us the project and the evaluation plan
We will tell you whether the evaluation would convince a reviewer and whether the baseline is a fair one.
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