Physics and Astronomy Colloquium 2018-2019

Fall 2018 and Winter 2019:  Thursdays, 3:30-4:30 pm

Spring 2019:  Thursdays, 3:30-4:30

1-434 Physics and Astronomy (map)

Reception from 3:15 p.m.
(unless otherwise posted)

For more information, contact Yaroslav Tserkovnyak


Fall 2018


Thursday, October 4, 2018, 3:30-4:30 p.m.

The life and death of turbulence

Nigel Goldenfeld

University of Illinois


Turbulence is the last great unsolved problem of classical physics. But there is no consensus on what it would mean to actually solve this problem. In this colloquium, I propose that turbulence is most fruitfully regarded as a problem in non-equilibrium statistical mechanics, and will show that this perspective explains turbulent drag behavior measured over 80 years, and makes predictions that have been experimentally tested in 2D turbulent soap films. I will also explain how this perspective is useful in understanding the laminar-turbulence transition, establishing it as a non-equilibrium phase transition whose critical behavior has been predicted and tested experimentally. This work connects transitional turbulence with statistical mechanics and renormalization group theory, high energy hadron scattering, the statistics of extreme events, and even population biology.
 


Thursday, October 11, 2018, 3:30-4:30 p.m.

Ni Ni

University of California, Los Angeles

Abstract:  TBA


Thursday, Octoer 18, 2018, 3:30-4:30

Welcome to the Gravitational-wave Revolution

David Reitze

Executive Director

LIGO Laboratory

California Institute of Technology

The gravitational-wave detections by LIGO and Virgo in the past three years have already revealed breakthrough insights into the high energy cosmos.  Among the new knowledge revealed these detections— black holes can form in binary systems, binary black hole mergers seed the formation of more massive black holes, binary neutron star mergers produce gamma ray bursts, the heaviest elements in the periodic table likely come from the collision of two neutron stars, the radii of neutron stars can be constrained by gravitational-wave emissions, and the Hubble constant can be measured using gravitational-wave sources as standard sirens.  


Thursday, October 25, 2018, 3:30-4:30

Investigating the Quantum Measurement Process.

Humphrey J. Maris

Brown University

In quantum mechanics the state of a system is described by the wave function. It is remarkable that according to the quantum theory the wave function changes with time in two seemingly distinct ways. There is a change in time which can be calculated from the time-dependent Schrodinger equation, and also the wave function is believed to change discontinuously as a result of measurements. However, despite much effort what constitutes a measurement and how a measurement causes a change in the wave function remains unclear. I will describe  experiments in which a part of the wave function of an electron is trapped in a box with walls sufficiently thick to prevent escape by tunneling. 


Thursday, November 1, 2018, 3:30-4:30

Smadar Naoz

University of California, Los Angeles

 

Abstract:  TBA


Thursday, November 8, 2018, 3:30-4:30

The International Race For A Quantum Computer

Stephanie Simmons

Simon Fraser University

Silicon transistors, the essential building block of most modern electronic devices, cannot shrink much further without being rendered inoperable by quantum mechanics. This classical-quantum threshold in fact presents a tremendous opportunity: if we harness quantum mechanics, rather than attempt to avoid it, we could build a quantum computer. Quantum computers will open up a world of opportunities — they could accomplish certain computational tasks exponentially faster which would otherwise be forever impractical. During this lecture, Dr. Simmons will discuss various quantum computing approaches, including her own all-silicon approach, how quantum technologies will change our lives in a very fundamental way, and provide a snapshot of the accelerating worldwide race to build a prototype.

 

 

Abstract:  TBA


Thursday, November 15, 2018, 3:30-4:30

Marcos Santander

University of Alabama

Abstract:  TBA


Thursday, November 22, 2018, 3:30-4:30

 


Thursday, November 29, 2018, 3:30-4:30

Sami Mitra

APS Physical Review Letters

Abstract:  TBA


Thursday, December 6, 2018, 3:30-4:30

Managing the Complexity of Molecules: Letting Matter Compute Itself

Gregory Kovacs, M.D., Ph.D. (EE)

Chief Technology Officer, SRI International

Professor Emeritus, Stanford University

Person-millenia are spent each year seeking useful molecules for medicine, food, agriculture and other uses. Biomolecules, which are comprised of interchangeable building blocks such as amino acids, represent a near infinite number of combinatorial possibilities. As an example, antibodies, which make up the majority of the top-grossing medicines today, are comprised of 1,100 amino acids chosen from the twenty used by living things. The binding part (variable region) that allows the antibody to recognize other molecules, is comprised of 110 to 130 amino acids, giving rise to at least 10143 possible combinations. However, are apparently only about 1080 atoms in the universe, illustrating the intractability of exploring the entire space of possibility. This is just one example of biological complexity…

Machine learning (ML), artificial intelligence (AI), and “big data” are often put forth as the solutions to all problems, particularly by pontificating TED presenters giving talks dripping with hyperbole. Expecting these methods to provide intelligent de novo prediction of molecular structure and function within our lifetimes is utter rubbish. For example, a neural network trained on daily weather patterns in Palo Alto cannot develop an internal model for global weather. In a similar way, finite and reasonable molecular training sets will not magically cause a generalizable model of molecular quantum mechanics to arise within a neural network, no matter how many layers it is endowed with. Regardless of the algorithms chosen, one simply cannot yet ask a computer to “compute” a drug that cures HIV.

With that provocative preface, we turn to the notion of letting matter compute itself. Massive combinatorial libraries can now be intelligently and efficiently created and mined with appropriate molecular readouts (AKA “the question vector”) at ever-increasing throughputs presently surpassing 1012 unique molecules in a few hours. Once “matter-in-the-loop” exploration is embraced, AI, ML and other methods can be brought to bear usefully in closed-loop methods to follow veins of opportunity in molecular spaces. Several examples of mining massive molecular spaces will be presented, including drug discovery and AI-guided continuous-flow chemical synthesis – all real, all working today.

 


Winter 2019


Thursday, January 10, 2019, 3:30-4:30 p.m.

Barbara Jacak

University of California, Berkeley

Abstract:  TBA

 


Thursday, January 17, 2019, 3:30-4:30 p.m.

Machine Learning Data from Electronic Quantum Matter

Eun-Ah Kim

Cornell University

In recent years, enormous data sets have begun to appear in real-space  visualizations (scanning probes) and reciprocal-space visualizations (scattering probes) of electronic quantum matter. Increasing volume and variety of such data present new challenges and opportunities that are ripe for a new approach: machine learning. However, the scientific questions in the field of electronic quantum matter require fundamentally new approaches to data science for two reasons: (1) quantum mechanical imaging of electronic behavior is probabilistic, (2) inference from data should be subject to fundamental laws governing microscopic interactions. In this talk, I will review the aspects of machine learning that are appealing for dealing with quantum complexity and present how we implemented a machine learning approach to analysis of scanning tunneling spectroscopy data.


 

 

 

 

 

 

Past Physics and Astronomy Colloquia