AMDUG
PAST EVENTS
Date: February 19, 2010
Title: Patterns and Their Dynamics
Speaker: Bjorn Sandstede, Professor of Applied Mathematics
Abstract: There are patterns everywhere around us: animal coats, sand ripples, cloud structures and snow flakes are just a few examples. I will show a few examples of pattern-forming systems that physicists have analysed experimentally in their labs and then discuss how math can help us to understand the origin and the dynamics of two intriguing patterns, namely Turing patterns and the meandering spiral waves.
Date: November 13, 2009
Title: Some Mathematical Challenges in Neuroscience
Speaker: Matthew Harrison, Assistant Professor of Applied Mathematics
Abstract: Remarkable advances in technology allow neuroscientists to peer ino the inner workings of the brains of behaving animals. But the challenges do not end when the experiment is over. Come and hear about how mathematics is helping neuroscientists make sense of their data. I will discuss some of the mathematical and statistical tools that we recently used in order to analyze a dataset recorded from rats performing a working memory task. The focus will be multiple hypothesis testing and random generation of binary matrices. No math background is required.
Date: April 17, 2009
Title: "Mathematics of Molecular Evolution"
Speaker: Daniel Michael Weinreich, Assistant Professor of Biology
Abstract: How to genetic mutations influence protein activity? How does natural selection act at the smallest scales? Professor Weinreich will discuss some some recently developed mathematical and experimental techniques used to answer thse basic questions.
Date: February 20, 2009
Title: "Rare Events in the Financial Markets"
Speaker: Stuart Geman, James Manning Professor of Applied Mathematics
Abstract: When it comes to the prices of stocks and other "securities," it seems that rare events are never rare enough. But they are too rare for meaningful statistical study. In order to test financial models of price fluctuations, focused on excursions, I will side step the issue of small samples by declaring an event "rare" if it is unusual relative to the interval of observation. Every interval has its own rare events, by fiat, and in fact as many as we need. Different classes of models have different invariants to the timings of these "rare" events. These invariants open the door to combinatorial-type hypothesis tests, under which many of the usual models do not hold up very well. I will give evidence for very rapidly changing dynamics and discuss the implications for model building.