Poster Presentation Information
Photo from the Fall 2020 final poster session.
When & Where
Time: Jan 7th 10:00am-12:00pm (Please come 15 minutes earlier to setup your poster.)
Location: Q-204 (Auditorium)
For students who can not attend the poster session in person due to COVID-19 travel restrictions, you can participate remotely by recording a 3 minute presentation video and send it to a TA before Jan 7th. The TAs will help you print your poster and post it up during the event.
What to include in the poster?
Abstract: a short summary of your work (no more than 100 words)
Introduction/Motivation: why is this problem important and what is your contribution?
Method: the machine learning methodology used
Results: the dataset you use and the experimental results (tables and figures)
Conclusion/Discussion: conclude your technical/application contributions
Reference: include 2-3 important references (You can use smaller fonts for this part.)
Make your poster more engaging by using color, diagrams and images.
Include an abstract no more than 100 words.
Avoid illegible fonts in size and style.
Use succinct, short sentences and avoid using paragraphs of text on your poster. You can also skip words such as “Figure 1”, “Table 2” in the captions
Make sure diagrams and images are large enough to be viewed from a distance.
Be prepared to answer questions on your research topic.
Poster Grading Policy
During the poster event, we will walk around to score your poster. You will have 2 minutes to present your poster to us and answer questions. Prepare a short pitch for your work before hand will be helpful.
You poster will be scored with a maximum of 10 points + 1 extra credit using the following rules. Note that the score will be based on our impression after viewing the poster and listening to your short presentation.
| Category | Criteria | What it means? | Points |
| Clarity | Purpose | Is the problem and motivation clearly defined? | 2 |
| Methodology | Is the method appropriately chosen and clearly explained? | 2 | |
| Implementation | Are the experimental/numerical results clearly presented? | 2 | |
| Contributions | Technical contribution | Does it have sufficient technical content? i.e. teaches the readers some machine learning related knowledge beyond class material? | 1.5 |
| Applicational contribution | Does it show meaningful analysis on results and findings? | 1.5 | |
| Presentation | Poster Quality | Is the poster well organized and legible? Does it make good use of figures and visual representations? | 1 |
| Extra credit | Novelty | Does it propose a novel problem or novel solution? | 1 |