The second annual Computer Science Undergraduate Project Showcase celebrated and highlighted student achievements in computer science research. Students presented 21 posters on a range of projects from mentored research to class projects to work pursued independently. Projects showcased students' creative application of skills and concepts gained through their studies in computer science.
Faculty Judges:
- Brandon Fain
- Jeff Forbes
- Jun Yang
- Xiaowei Yang
2019 Winners
Best Research
Design Checklist by Teddy Marchildon & Matthew O’Boyle
Efficient Algorithms for Design Automation of Flow-Based Microfluidic Biochips by Aditya Sridhar with Research Advisor Krishnendu Chakrabarty
Machine Learning in Stock Market Forecasting by Tatiana Jiayi Tian with Prof. Rong Ge
Quantifying Gerrymandering: Stratified Sampling the Space of Possible North Carolina Congressional Redistrictings a Bass Connection and Department of Mathematics project by Luke Farrell and Jake Shulman
Spotlight on One of the Winners
All Showcase Entries
| Project Title |
|---|
| Challenges in Automation Pop-up Fact Checking |
| Coding the Craziness: Tenting Application |
| Coding the Craziness: Tenting Application |
| Commerce Search Engine Result Enrichment |
| Data&Technology For Fact Checking |
| Design Checklist |
| Developing Automated Tools for Political Fact-Checking |
| Developing HRI Graduate Course Project on Usability Testing |
| Edge Computing with Google ARCore |
| Efficient Algorithms for Design Automation of Flow-Based Microfluidic Biochips |
| Interpretable Image Recognition with Hierarchical Prototypes |
| JSAWAA: Algorithm & Visualizations for the Modern Web |
| Machine Learning in Stock Market Forecasting |
| Mobile Alerting Interface for Human and Drone Contraband Drops |
| Proportionally Fair Clustering Allocation |
| Quantifying Gerrymandering: Stratified Sampling the Space of Possible North Carolina Congressional Redistrictings |
| Reinforcement Learning for AR/MR Output Security |
| Sensor Logger Framework for Android |
| Time-Series Analysis for Predicting Stragglers |
| Using Machine Learning for Predicting the Clinical Outcomes of Schizophrenia |
| Using Machine Learning to Reduce Vaccine Misinformation |