I am an ERC-funded postdoctoral researcher in the Department of Statistics at the University of Oxford, working with Prof. Pier Palamara.  I recently completed my DPhil in Genomic Medicine and Statistics, jointly supervised by Prof. Simon Myers and Prof. Pier Palamara.  My research focuses on developing Bayesian methods for statistical and population genetics, with applications to ancestry decomposition and genome-wide association studies. Prior to this, I earned my bachelor’s and master’s degrees in Electrical Engineering from IIT Bombay, India.

• Bayesian machine learning techniques for genome-wide association studies (GWAS).
• Genome-wide genealogies to analyze past human history, particularly "ghost" populations
• Uncertainty-aware and truth-worthy machine learning.

Loya, H., Kalantzis, G., Cooper, F. et al. A scalable variational inference approach for increased mixed-model association power. Nat Genet 57, 461–468 (2025). https://doi.org/10.1038/s41588-024-02044-7

Dupont, E., Loya, H., Alizadeh, M., Goliński, A., Teh, Y. W., & Doucet, A. (2022). Coin++: Neural compression across modalities. arXiv preprint 
https://arxiv.org/abs/2201.12904

Bezeljak, U., Loya, H., Kaczmarek, B., Saunders, T. E., & Loose, M. (2020). Stochastic activation and bistability in a Rab GTPase regulatory network. Proceedings of the National Academy of Sciences, 117(12), 6540-6549.

Loya, H., Poduval, P., Anand, D., Kumar, N., & Sethi, A. (2020). Uncertainty estimation in cancer survival prediction. arXiv preprint 
https://arxiv.org/abs/2003.08573