Aseem Raj Baranwal
I am an AI scientist at XTX Markets.
My doctorate was on statistical foundations for learning on graphs and high-dimensional probability, co-advised by Prof Kimon Fountoulakis and Prof Aukosh Jagannath at the Cheriton School of Computer Science, University of Waterloo.
Before that, I did my M.Math. at UWaterloo with Jeffrey Shallit on algorithmic number theory and combinatorics on words, and my undergrad at IIT Jodhpur.
Fun fact: my Erdős number is 2 via Jeffrey Shallit.
Recent Posts
-
Stein's paradox
Posted on:In three or more dimensions, the sample mean is dominated everywhere by a shrinkage estimator. The geometric reason is the Gaussian shell: noise pushes you outward, and pulling back is uniformly better. A precursor of ridge regression and most modern regularization.
-
Nearest neighbor breaks in high dimensions
Posted on:In high dimensions, all pairwise distances become essentially equal. Nearest and farthest neighbor are no longer meaningfully different. A short geometric tour of the curse of dimensionality.
-
High-dimensional Gaussians live on a sphere
Posted on:The bell-curve picture says Gaussian samples live near the mean. In high dimensions that picture is catastrophically wrong: almost all the mass lies in a thin spherical shell at radius √d. Density and mass are not the same thing.
-
Central Limit Theorem - why sums become Gaussian
Posted on:A geometric look at the central limit theorem. Adding random variables is convolving their densities. Convolution smooths. Watch a Bernoulli, a die roll, or a bimodal distribution become Gaussian as you slide the number of summands.