Explore a 42-minute lecture on Stochastic Density Functional Theory presented by Eran Rabani from the University of California, Berkeley at IPAM's Monte Carlo and Machine Learning Approaches in Quantum Mechanics Workshop. Delve into the advantages of sDFT for studying ground-state properties of extended materials, including its linear and sub-linear scaling capabilities. Discover recent efforts to mitigate statistical fluctuations using embedding schemes and learn about the application of sDFT in describing warm dense matter at elevated temperatures. Examine topics such as stochastic orbitals, trace formula, self-averaging, energy window embedding, and finite temperature SDFT. Gain insights into the nonmetal-to-metal transition and the potential of sDFT in determining ground state structures with chemical accuracy for systems with tens of thousands of electrons.