Scientific

This tutorial will introduce you to the theory and practice of ggplot2. I'll introduce you to the rich theory that underlies ggplot2, and then we'll get our hands dirty making graphics to help understand data. I'll also point you towards resources where you can learn more, and highlight some of the other packages that work hand in hand with ggplot2 to make data analysis easy.

You will have the opportunity to practice what you learn, so please bring along your laptop, with the latest version of R installed. Make sure that your version of ggplot2 is up-to-date by running install.packages("ggplot2").

To get the most out of the course, I'd recommend that you're already comfortable with R: you know how to get your data into R, you've done some graphics (base or lattice) in the past, and you've written an R function.

Symplectic topology can be thought as the mathematical versant of String theory: they were born independently at the same time, the second one as a fantastic enterprise to unify large-scale and low-scale physics, and the first one to solve classical dynamical problems on periodic orbits of physical problems, the famous Arnold conjectures. In the 80's, Gromov's revolutionary work opened a new perspective by presenting symplectic topology as an almost Kähler geometry (a concept that he defined), and constructing the corresponding theory which is entirely covariant (whereas algebraic geometry is entirely contravariant). A few years later, Floer and Hofer established the bridge between the two interpretations of Symplectic topology, the one as a dynamical theory and the one as a Kähler theory. This bridge was confirmed for the first time by Lalonde-McDuff who related explicitly the first theory to the second by showing that Gromov's Non-Squeezing Theorem is equivalent to Hofer's energy-capacity inequality.

Nowadays, Symplectic Topology is a very vibrant subject, and there is perhaps no other subject that produces new and deep moduli spaces at such a pace ! More recent results will also be presented.

What protects your computer password when you log on, or your credit card number when you shop on-line, from hackers listening on the communication lines? Can two people who never met create a secret language in the presence of others, which no one but them can understand? Is it possible for a group of people to play a (card-less) game of Poker on the telephone, without anyone being able to cheat? Can you convince others that you can solve a tough math (or SudoKu) puzzle, without giving them the slightest hint of your solution?These questions (and their remarkable answers) are in the realm of modern cryptography. In this talk I plan to survey some of the mathematical and computational ideas, definitions and assumptions which underlie privacy and security of the Internet and electronic commerce. We shall see how these lead to solutions of the questions above and many others. I will also explain the fragility of the current foundations of modern cryptography, and the need for stronger ones.No special background will be assumed.

Large scale production of very heavy oil is gaining momentum because of the decline of easy to produce reservoirs, the increasing oil demand and subsequent rising oil price, which makes such resources more economical. Considering the pressure on the oil market and our still very heavy dependence on oil, this move to heavy oil production seems inevitable. Typically, heavy oil reservoirs are stimulated thermally. Injecting steam that is generated at the surface is not always viable or desirable. An alternative technique for production is In-Situ Combution (ISC) where a steam drive is generated in the reservoir itself. In this process, (enriched) air is injected in the reservoir. After ignition a combustion front develops in-situ that burns a small percentage of the oil in place and slowly moves through the reservoir producing steam along the way. A side benefit of this process is that the heat thus generated often cracks the oil into heavy, undesirable components (the "guck") that stay behind in the reservoir and lighter, more valuable components that can be brought up to the surface. Performance prediction of ISC projects is rather tricky and poses many computational challenges. In this talk I'll discuss our work in ISC simulation, which is centered around the design of upscaling methods for kinetics and critical reservoir heterogeneities supported by laboratory experimentation.

Bootstrap percolation, one of the simplest cellular automata, can be viewed as an oversimplified model of the spread of an infection on a graph. In the past three decades, much work has been done on bootstrap percolation on finite grids of a given dimension in which the initially infected set A is obtained by selecting its vertices at random, with the same probability p, independently of all other choices. The focus has been on the critical probability, the value of p at which the probability of percolation (eventual full infection) is 1/2.

The first half of my talk will be a review of some of the fundamental results concerning critical probabilities proved by Aizenman, Lebowitz, Schonman, Cerf, Cirillo, Manzo, Holroyd and others, and by Balogh, Morris, Duminil-Copin and myself. The second half will about about the very recent results I have obtained with Holmgren, Smith, Uzzell and Balister on the time a random initial set takes to percolate.