bookshelf
Mathematics is not about numbers, equations, computations, or algorithms: it is about understanding.
– William Paul Thurston
Published works
Recommended reading
Books that have shaped my thinking at the intersection of topology, data science, and machine learning.
Computational Topology
Herbert Edelsbrunner & John Harer — The foundational textbook on computational topology, covering simplicial complexes, homology, and persistent homology.
Elementary Applied Topology
Robert Ghrist — A beautifully illustrated introduction to applied topology, from sensor networks to robotics to data analysis. Free PDF on the author's page.
Topology and Data
Gunnar Carlsson — The seminal paper that launched topological data analysis as a field. Essential reading for anyone working at the intersection of topology and ML. Bull. AMS, 2009.
Reinforcement Learning: An Introduction
Richard Sutton & Andrew Barto — The definitive textbook on reinforcement learning, covering bandits through deep RL and policy gradient methods. Free PDF on the author's site.
Topology for Computing
Afra Zomorodian — Bridges discrete mathematics and algebraic topology with algorithms, covering homology computation and persistence. Cambridge University Press, 2005.
