Applied probability has seen a revolutionary growth in research activity, driven by the information age and exploding technological frontiers. Applications include the Internet and the world wide web, social networks, integrated supply chains in manufacturing networks, the highly intertwined international economies, and so on. The common thread running through these is that they are large interconnected systems that are emergent with very little top down design to optimize them. Probabilistic methods with limit theorems as their mainstay are best suited to find structure and regularity to help model, analyze and optimize such systems. Interface of probability with optimization and control, statistics, and machine learning has been the driving force behind the emerging paradigms, techniques and mathematics to address the huge scale of problems seen in such technological and commercial applications, not to mention several in biological or physical systems.
In this two-week program on advances in applied probability (PAAP), we will have some of the leading researchers in applied probability conduct short courses in emerging areas, including:
- High dimensional computation
- Inference, modelling and learning on networks
- Limit theorems on random graphs, and
- Monte Carlo methods.
There will be about ten short courses sprinkled with research talks each day. There will be research workshops on two of the days during the program.
During the program, Prof. Peter Glynn (Stanford University) will be delivering the Infosys - ICTS Turing lectures.
Faculty and students working on Applied Probability or related area can apply. (Students should provide recommendation letters from their supervisors)