EeShan Bhatt, PhD

Research

• marine autonomy

  The co-evolution of artificial intelligence and autonomous sensing platforms can curate scientific datasets of higher quality and unprecedented spatio-temporal access. Heterogeneous intelligent sensing networks that can collect sustained observations and support navigation for transient mobile platforms provide organic sampling modalities for important oceanographic features at various spatial and temporal scales. The potential to displace ship-based oceanography and transition from teams of humans supervising one robot to teams of robots supervised together poses fascinating technical questions around embedded acoustics, artificial intelligence, and coordinated systems of control.

• observational oceanography

  From polar to littoral to hadal environments, there is an increasing focus on gathering interconnected data sets to understand complex mechanisms: how heat and carbon are regulated; how biological, mineral, and energy resources are stored; how human activity, including microplastics, pollution, or commerce, imbalance biogeochemical processes and ecosystems with no concern to political boundaries; the list goes on. Placing observations at the forefront of the narrative grants a realistic picture of current conditions, corroborates the physics that governs observed phenomena, and contextualizes what models can and cannot answer. I am passionate about how the next generation of "big data" oceanography will center co-ownership across local communities, collaboration across multiple stakeholders, and data discovery for all.

• sensors and systems

  Sensors onboard underwater platforms provide ample opportunity for real-time filtering, classifying, and compression. I work with standard oceanographic sensors as well as acoustic packages like modems, hydrophone arrays, and vector sensors, in pursuit of better communication and adaptive behavior underway.