I am a Ph.D. student in the Theory of Machine Learning Lab, advised by Nicolas Flammarion.

I focus on statistical learning theory in sequential settings where the traditional i.i.d. assumption does not hold. My research addresses challenges that arise in scenarios where data points are dependent or sampled from non-stationary distributions, such as in natural language processing or time-series analysis. By developing theoretical frameworks for these scenarios, I aim to advance our understanding of learning processes in complex, real-world environments.

Check out my webpage here for more details.