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Pulled in from source.

I am broadly interested in theory-driven deep learning applications. 

Currently I'm working on improving generative models, particularly diffusion models, under the manifold hypothesis.

I completed my PhD at the Faculty of Computer Science at the Technion focused on deep learning and statistical methods for high-resolution medical images and molecular measurements. I then worked as a postdoctoral research assistant where I found myself drawn towards generative models due their strong theoretical backing. This led me to develop a new class of generative models -- Generative Topological Networks -- which I will continue to explore alongside other generative approaches. 

For the most up-to-date information, best to visit: https://alonalj.github.io/ or find me on linkedin.

[e.g. educational background, biographical details]

[areas of research interest]

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

I make Machine Learning, Causal Inference & Bayesian Optimisation work in the real world.

2017 BA Philosophy, Politics, Economics @ University of Exeter, UK

2019 PhD Computer Science (incomplete), graduated MSc Computer Science @ Syracuse University, USA

2022 Spotify Research UK (during PhD at UCL)

2024 PhD Foundational Artificial Intelligence (Google Deepmind Scholar, £125k) @ University College London, UK

Theoretical

• Causal Inference
• Partial Identification
• Active Learning
• Bayesian Optimisation

Applied

• Efficient Experimentation in Pharma, Semiconductors and Materials
• Experimentation for Price Impact Models
• Causal Inference for Common Disease 
• Causality for Molecule Discovery

Working between both the Department of Statistics and Mathematical Institute on projects contributing to the Research Excellence Framework submission.

Leading and supporting on projects to troubleshoot and improve PSS operations and workflows.

I am a Florence Nightingale Bicentenary Fellow in Computational Statistics and Machine Learning at the University of Oxford, jointly affiliated with the Department of Statistics and the Leverhulme Centre for Demographic Science (LCDS). I completed a PhD in Statistics at the University of Washington, advised by Adrian E. Raftery and Carlos Cinelli. Prior to UW, I studied math and physics at the University of Cambridge and Northwestern University. I am originally from Chicago, Illinois.

My research focuses on development of methods and applications of (primarily Bayesian) statistics to inform decision-making in the health and social sciences. I am interested in tackling problems in

• causal inference
• model selection and hypothesis testing
• nonparametric and high-dimensional statistics
• design and analysis of experiments
• modeling of complex data (e.g., hierarchical, spatiotemporal, mechanistic, and infectious disease models)