Lead the research component of an aquarium app dev team focused on minimizing risk from hacking, social engineering and privacy data exposure, while maximizing functionality tied to aquarium sensors including a camera. Apply collegiate-level research and analysis to document aquarium fish species cross-compatibility as the baseline dataset for automated species identification using vision processing software. Consult on fish health as well as aquaculture and automating the recognition of these conditions while minimizing data privacy risks.

This project has been designed as a tiger-team to attack our client's concerns about malicious use of aquarium data in a consumer mobile app, including video feed, and reducing the risk of bad recommendations while increasing the app's functionality. The intern will apply 1-2 years of fish conservation undergraduate study to building a robust dataset of fish species identification as the baseline for automated vision recognition, then build additional components to include cross-species compatibility, habitat recommendations and recommended aquarium population. Balance a trade-off between video camera resolution, field of view, vision recognition and data privacy Work with a team of software and project engineers to build data structures that support large-scale integration of this research into a consumer mobile-application. Supervise the construction of research tanks and video camera data feeds that minimize potential for privacy compromises, social engineering or hacking.  As the data set grows, provide recommendations and observations on aquaculture and identify additional opportunities. Present findings to a panel of aquarium industry experts who have significant data protection and privacy concerns, making the business case that the risk has been mitigated while the app functionality has been maximized.