Research > Applied Probability & Stochastic Analysis
Research in applied probability at Caltech focuses both on the development of analytical techniques for the study of random phenomena as well as the application of probability theory to study the evaluation, design, and control of systems that have some form of inherent randomness. Particular areas of interest to our group includes applications in the areas of communication networks, networked control systems, statistical inference, algorithm design, and uncertainty quantification.
Recent Research Talks
Congestion and Competition in the Cloud - Adam Wierman 2/28/13
NIPS 2012 Tutorial - Joel Tropp 2/15/13
A geometric theory of phase transitions in convex optimization - Joel Tropp 7/25/13
ACM 116. Introduction to stochastic processes and modeling.
EE/Ma/Ca 126 ab. Information theory.
ACM/Ma 144. Probability theory.
CS 147. Network performance analysis.
CS 150. Probability and algorithms.
CS/SS 152. Introduction to data privacy.
CS/CNS/EE 155. Machine learning and data mining.
CS/CNS/EE 156ab. Learning systems.
ACM 216. Markov chains and discrete stochastic processes.
ACM 217. Topic: Concentration inequalities.
ACM 217. Topic: Stochastic differential equations.
ACM 217. Topic: Bayesian updating and inference.
ACM 218. Statistical inference.