Dr. Abhiraami Navaneethanathan

Marine Particulate Organic Carbon Flux Estimation: Data-Driven Insights from Heterogeneous In Situ Observations

Completion: 07 JAN 2026

PhD

Marine Particulate Organic Carbon Flux Estimation: Data-Driven Insights from Heterogeneous In Situ Observations

My PhD research focuses on better understanding the processes that influence the ocean biological carbon pump using in situ particulate organic carbon (POC) flux observations from a variety of instruments.

I am interested in how data fusion methods can be used to combine these heterogeneous data sources in order to address their sparsity and biases, and to make more accurate global POC flux estimates than what can be obtained from individual sources.

In Simple Terms...

I apply ML methods, such as Random Forests, XGBoost, Multi-Layer Perceptron models to estimate global POC fluxes from environmental variables.

Research Focus

Key Areas

Data fusion
Data biases in machine learning (ML)
ML and statistical hybrid models
In situ observations and remote sensing
Explainable AI

Methods

Bayesian hierarchical models
Random Forests
XGBoost
MultiLayer Perceptron models

Supervisors

Lead Supervisor
Professor Chunbo Luo

Department of Computer Science · Faculty of Environment, Science and Economy
Lead Supervisor
Professor Peter Challenor

Department of Mathematics and Statistics · Faculty of Environment, Science and Economy
Lead Supervisor
Professor Sabina Leonellie

Department of Sociology and Philsophy · Faculty of Humanities, Arts and Social Sciences
Lead Supervisor
Dr. B.B. Cael

Department of Geophysical Sciences · The University of Chicago

External Partners

Dr. Adrian Martin, National Oceanography Centre

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