- Intro. Physics and statistics (a) where theory and experiment meet, (b) divergent world views, (c) underlying probabilistic nature of the world: Bell's theorem and random number generation.
- The calibration of a few photon detector. (a) What is a Transition Edge Sensor? What needs to be calibrated? (b) The K-means algorithm as maximum likelihood. (c) Adaptation of the K-means algorithm to Poisson statistics: a new maximum likelihood objective function: PIKA. (d) Application of PIKA to calibration of an attenuator at near-ideal quantum efficiency.
- Monte Carlo and importance sampling: a rapid algorithm to determine scattering parameters for an optical medical phantom.
- Tomography: (a) early application of a Bayesian method to integrated circuit interconnect tomography, (b) scattering, Monte Carlo and tomography: algorithmic developments - the moving expanding window and a hybrid standard and Forced-Fixed Detection Monte Carlo, (c) quantifying the uncertainty of the Response Evaluation Criteria in Solid Tumors (RECIST).
Organizers: Niranjan Ramachandran, Dio Margetis, and Leo Koralov