Have a UROP opening you would like to submit?
Please fill out the form.
Automatic Encoding Selection
6: Electrical Engineering and Computer Science
Andreas Kipf, email@example.com
Selecting an appropriate encoding (e.g., run-length encoding) for a particular data column is an important problem in data warehousing. The goal of this UROP is to develop an automatic encoding selector that takes column statistics and workload requirements as input and recommends an encoding. Specifically, the task is to develop a cost model that predicts the compression ratio and the (de)compression speed based on the data representation.
Machine learning, deep learning, reinforcement learning (PyTorch). Experience with C++ is a plus.