Mentor: Yang Xu, Engineer at LexisNexis Risk Solution

We will design a system to fetch data from REST api and store data in a database. The system is comprehensive with various functionalities such as input/output, caching, logging, etc. The system has to be highly scalable so that it could handle big amount of data.

Mentor: Omar Shehata, Graphics engineer at Cesium

Create a web app to visualize LiDAR data from self driving cars.

LiDAR is a way of obtaining an accurate 3D representation of a scene, often used in self driving cars to detect their surroundings. Lyft recently released a large dataset of sensor readings from their self driving car work: https://level5.lyft.com/dataset/

Most analysis done on data like this is done without visualizing all the data together, because it's in different formats/too large to efficiently visualize in real time. This project would involve taking this data, converting it to a standard format that Cesium's pipeline can ingest (likely using Python since it has many of the helper libraries you'll need), and then building an application to visualize it in 3D using Cesium's JavaScript library.

This would not only help people get more insight out of this particular dataset, but would provide a framework for anyone doing self driving car analysis to see their result and share their research. For inspiration you can see how people are analyzing this data in this Kaggle competition:

https://www.kaggle.com/c/3d-object-detection-for-autonomous-vehicles/discussion/109415

Mentor: Charlie Liu, Student at Yale University

Ever watched one of those YouTube statistics videos with the scrolling bar graphs? (If not, check this out: https://youtu.be/K1Fa46uRTWg) Using data visualization, sorting, and text parsing, you can create a “bar chart racer” that displays statistics in an exciting way! Not only is this a fun project to work on, but you can also use your finished product to perhaps launch your very own YouTube channel in the future :)