Global Bathymetry: Machine Learning for Data Editing

Develop a machine learning algorithm to edit seafloor bathymetry soundings.
Type of Data: 
Time series of depth soundings collected by a sonar on a ship (e.g., time, longitude, latitude, depth, . . .)
Approximate Data Size: 
440 million records from more than 8000 month-long ship cruises
Domain Expert: 
Methods Expert: 
Methods Student Openings: 
Methods Student Funding: 
Methods Student Prerequisites: 
* python and jupyter notebooks. * a course in Probability and statistics * Familiarity with boosting * Google maps API: