Anita Nandi is a Data Scientist at Trilateral Research.
She is co-developing Trilateral’s data-driven solution for risk assessment and human security analysis in the public sector (STRIAD). She is currently working on the DASA-funded project HAMOC, which analyses human security, as well as being involved in the INNOVATE UK-funded project CESIUM, which leverages machine learning techniques to better prevent and combat child exploitation.
Anita has over 6 years of experience of working with large datasets in a range of different contexts. She has worked on projects in the fields of physics, epidemiology, statistics and medicine. Her main interests lie in building statistical or machine learning models to address a problem, while understanding the context and biases of the datasets involved and how to account for these factors in the modelling.
Anita has a PhD in Experimental Particle Physics from University of Oxford. Her thesis involved analysing huge datasets from the Large Hadron Collider at CERN (producing about 1 billion collisions per second) to understand the structure of the universe. This involved applying machine learning algorithms to classify particles of interest and building statistical models to make measurements of particle behaviour.
She also worked as a postdoctoral researcher in epidemiology with the Malaria Atlas Project based at the Big Data Institute, University of Oxford. This work involved working within a multidisciplinary team researching spatial modelling techniques, including Bayesian spatial modelling and machine learning algorithms, to improve malaria risk predictions for future research and policy decisions.
Anita has authored several peer-reviewed publications in the fields of particle physics, epidemiology, statistics and medicine. She also writes a blog containing articles on various scientific topics.
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