What Excites Me
I love learning. I do this by diving into intricacies, asking lots of questions (even if I can’t answer them!), and trying to neatly organize all the moving parts in my head. This approach allows me to gain new insights into problems, and I love sharing those insights with others.
During the 10 years from 2011-2021, I obtained a solid theoretical training in Mathematics, Statistics, and Computer Science. Since I have always liked the idea of peeking over the “shoulder’s of the giants”, I am excited whenever I get to use technical knowledge in these areas to bring new insights into real-world problems.
My favorite branch of mathematics is called Probability Theory. Isn’t it marvelous that there can even be a rigorous theory of “chance”? Apart from providing tools to compute probabilities, this theory also shows how there is often structure amid chaos, especially in large systems. For instance, during my PhD, I showed that a system with multiple queues (think: supermarket checkout lanes) and a random assignment of jobs (called the power-of-d choices) approximately follows a Stochasic Differential Equation when the number of queues is large.
Currently, I am interested in the foundations of Machine Learning and Statistics. Particularly my work is tackling the problem of performing robust and interpretable Bayesian inference in a principled way.