News

The preprint of "Statistically Valid Hyperparameter Selection: From Tuning to Guarantees" is now available on arXiv. The monograph addresses how hyperparameter selection plays a central role in the development of modern machine learning, and presents a unified framework for obtaining statistical guarantees on the selected hyperparameters.

I have started a Research Associate position at Northeastern University London within the Institute for Intelligent Networked Systems (INSI), supported by European Research Council funding. Excited to continue work on trustworthy AI and statistical inference in this new environment.

Collaborated with Oriel College, University of Oxford on a video discussing autonomous vehicle technology, addressing public questions regarding safety, regulation, and deployment ahead of the London launch of self-driving cars.

Delivered a tutorial presentation titled "From Tuning to Guarantees: Statistically Valid Hyperparameter Selection" at NeurIPS 2025. The tutorial covered the theoretical foundations of conformal and hypothesis-testing-based approaches to hyperparameter optimization with formal statistical guarantees.

Excited to announce a 3-hour tutorial at NeurIPS 2025 on moving beyond empirical methods toward statistically guaranteed hyperparameter selection. The tutorial bridges classical statistical testing with modern machine learning practice.

Competed in the University of Oxford Three Minute Thesis finals, earning the runner-up prize among eight finalists. The presentation distilled the core ideas behind statistical guarantees for machine learning into an accessible three-minute talk for a general audience.