Research > Applied Probability & Stochastic Analysis

Applied Probability and 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.


Jim Beck, Fernando Brandão, Venkat Chandrasekaran, Victoria Kostina, Katrina Ligett, Houman Owhadi, Leonard Schulman, Andrew Stuart, Omer Tamuz, Joel Tropp, Chris Umans, Thomas Vidick, Yisong Yue, Adam Wierman

Related research groups & Centers > CDS, CD3, CMI, DOLCIT, RSRG, Theory group

Recent Research Talks

Related Courses

Ma 103. Introduction to probability and statistics.
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.