Helen Quinn

Professor Emerita, Particle Physics and Astrophysics
SLAC National Accelerator Center, Stanford University

"A window on particle physics at Harvard in the early 1970’s"

Wednesday, November 17, 2021 6:30pm in Jefferson 250 
And also streamed live via Zoom (but we hope you will attend in person, with appropriate face covering)
For Zoom link, please contact Jolanta Davis

...

All lectures will be held in Jefferson 250
And also streamed live via Zoom (but we hope you will attend in person, with appropriate face covering)
For Zoom link, please contact Jolanta Davis

Monday, November 1, 4:30pm:
"Exoplanets and the Search for Atmospheric Biosignature Gases"
...

Crises & Tipping Points: From Statistical Physics to Social Sciences

As P. W. Anderson wrote in 1972 in his article "More is Different", the behavior of large assemblies of individuals (/molecules) cannot be understood by extrapolating the behavior of isolated individuals (/molecules). On the contrary, completely new behaviors, sometimes spectacular and difficult to anticipate, can appear and require new ideas and methods. The purpose of statistical physics is precisely to try to understand these collective phenomena, which do not belong to any of the underlying elementary...

Emergence of Object Representations in Brain Sensory Hierarchies

Modern quantum algorithms with provable speedups originate historically from three disparate origins: simulation, search, and factoring. Today, we can now understand and appreciate all of these as being instances of a single framework, recently created by Gilyen, Su, Low, and Weibe, based on two key ideas: (1) the transformability of singular values by quantum evolution, and (2) the nonlinearity available to process two-level quantum signals. This remarkable unified framework opens doors to new quantum algorithms, provides opportunities for quantum advantage, and introduces questions...

Nanometer-sized holes can be opened in materials in order to detect single molecules, sequence DNA and RNA or store information. Using computational means at various spatiotemporal levels, we attempt to understand the characteristics of nanopores in different materials in order to tune their detection efficiency and biosensitivity. On top of these, Machine Learning approaches have allowed us to interpret relevant experimental data and provide us with predictions on the identity of the molecules threading the pores. We discuss what we have learned so far and the relevance of our work in...