Nikita is a computational researcher working at the intersection of genetics, biomedical data science, and machine learning. With a background in computer science and software engineering, she focuses on developing data-driven methods and scalable analytical pipelines for high-dimensional biological and clinical data.

She completed her PhD at University College London, where she worked with large-scale resources such as the UK Biobank, genomics and proteomics data. Her research combines statistical genetics approaches with machine learning techniques to better understand complex disease biology and improve risk prediction.

Nikita’s work spans both causal inference and predictive modelling, with particular experience applying computational methods to complex, structured biomedical. She is especially interested in how machine learning can be used to extract meaningful patterns from large, noisy datasets and translate these into clinically useful insights.

Looking ahead, her research will increasingly focus on cardiovascular disease, with an emphasis on integrating diverse data modalities, including imaging, alongside molecular and clinical data. Her broader aim is to develop interpretable, machine learning–driven approaches that bridge robust statistical methodology with real-world clinical application