Scientific

The concept of supersymmetry, though never observed in Nature, has been one of the primary drivers of investigations in theoretical physics for several decades. Through all of this time, there have remained questions that are unsolved. This presentation will describe how looking at such questions one can be led to the 'Dodecaphony Technique'  of Austrian composer Schoenberg.

Jim Gates is a theoretical physicist known for work on supersymmetry, supergravity and superstring theory. He is currently a Professor of Physics at the University of Maryland, College Park, a University of Maryland Regents Professor and serves on President Barack Obama’s Council of Advisors on Science and Technology.

Gates was nominated by the US Department of Energy to present his work and career to middle and high school students in October 2010. He is on the board of trustees of Society for Science & the Public, he was a Martin Luther King Jr. Visiting Scholar at MIT (2010-11) and was a Residential Scholar at MIT’s Simmons Hall. On February 1, 2013, Gates received the National Medal of Science.

• http://www.kaleidoscope.net/greg/math/The%20Adinkramat.html
• https://code.google.com/p/adinkramat/
• https://www.youtube.com/watch?v=mFvtSGCFVO0

Tibshirani will review the lasso method and show an example of its utility in cancer diagnosis via mass spectometry. He will then consider testing the significance of the terms in a fitted regression, fit via the lasso. He will present a novel test statistic for this problem and show that it has a simple asymptotic null distribution. This work builds on the least angle regression approach for fitting the lasso, and the notion of degrees of freedom for adaptive models (Efron 1986) and for the lasso (Efron et. al 2004, Zou et al 2007). He will give examples of this procedure, discuss extensions to generalized linear models and the Cox model, and describe an R language package for its computation.

This work is joint with Richard Lockhart (Simon Fraser University), Jonathan Taylor (Stanford) and Ryan Tibshirani (Carnegie Mellon).

In recent years it has become increasingly clear that stochasticity plays an important role in many biological processes. Examples include bistable genetic switches, noise enhanced robustness of oscillations, and fluctuation enhanced sensitivity or “stochastic focusing". Numerous cellular systems rely on spatial stochastic noise for robust performance. We examine the need for stochastic models, report on the state of the art of algorithms and software for modeling and simulation of stochastic biochemical systems, and identify some computational challenges.

The dichotomy between sparse and dense structures is one of the profound, yet fuzzy, features of contemporary mathematics and computer science. We present a framework for this phenomenon, which equivalently defines sparsity and density of structures in many different yet equivalent forms, including effective decomposition properties. This has several applications to model theory, algorithm design and, more recently, to structural limits.