• Spectral Clustering on Graphs
    Understanding graphs by an intuitive deep dive into the graph Laplacian and showcasing its application in effectively partitioning a graph into clusters.

  • Constrained Optimization and the KKT Conditions
    A discussion on how the Lagrangian function solves the challenges introduced by the constraints in an optimization problem. Moreover, the optimality of the Lagrangian is discussed by gaining insights into the KKT conditions.
    Published in TowardsDataScience

  • Geodesic Regression
    A generalization of regression in Euclidean space to Riemannian Manifolds. Some basic concepts of Riemannian Geometry are covered before diving into the topic.
    Published in TowardsDataScience

  • Principal Geodesic Analysis
    Explanation on obtaining principal geodesics for data lying in a Riemannian Manifold, akin to Principal Component Analysis (PCA) in Euclidean space, serving as a dimensionality reduction technique for manifold-valued data.
    Published in TowardsDataScience

  • A soft Intro to General Relativity
    Offering insights into the foundational principles of Einstein's Theory of General Relativity by exploring the everyday effects of gravity from a relativistic standpoint.

  • How Interesting are Black Holes?
    A lighthearted presentation on the interesting facts about Black Holes.