Graduate

Atom interferometers are powerful tools for both measurements in fundamental physics and inertial sensing applications. Their performance, however, has been limited by the available interrogation time of atoms freely falling in a gravitational field. I will describe our realization of an intra-cavity trapped atom interferometer with 20 seconds of coherence, which extends the interrogation time of spatially-separated quantum superpositions of massive objects by nearly an order of magnitude. I will discuss how this trapped geometry differs from traditional free-fall atom interferometers by allowing potentials to be measured by holding, rather than dropping, atoms.

Womxn in Electrical Engineering is hosting a lunch q&a with Professor Caroline Trippel

The lunch will have a roundtable format and is an excellent opportunity to ask questions about grad school, careers in academia etc.

Attendees will receive a doordash gift card to purchase lunch for the event.

If there is a high level of interest in the event, we will hold a lottery to select the attendees.

Observations of the cosmic microwave background (CMB) and the galaxy-filled sky provide images of the universe at its various stages that are sensitive to its physics from the earliest moments to recent times. These observations are key to expanding our understanding to the physics of inflation, neutrinos, dark matter, and dark energy—some of the most mysterious terrains in physics today. We will present results from current CMB and optical surveys—the South Pole Telescope & BICEP/Keck Array and the optical Dark Energy Survey, respectively—whose unprecedented sensitivities enable us to stringently test the standard cosmological model and constrain new physics. We will discuss their current limitations due to calibration uncertainty and confounding astrophysical effects. To conclude, we will look forward to the bright future of optical, CMB, and joint cosmological experiments that will be performed by the Vera C. Rubin Observatory's Legacy Survey of Space and Time and the CMB-S4 experiment.

A weekly lecture series exploring the intersections of race, racism, and scientific practice.

Race in Science: Covid-19, Racism, and Health Disparities

In this talk I will describe our system for capturing, reconstructing, compressing, and rendering high quality immersive light field video. We record immersive light fields using a custom array of 46 time-synchronized cameras distributed on the surface of a hemispherical, 92cm diameter dome. From this data we produce 6DOF volumetric videos with a wide 80-cm viewing baseline, 10 pixels per degree angular resolution, and a wide field of view (>220 degrees), at 30fps video frame rates. Even though the cameras are placed 18cm apart on average, our system can reconstruct objects as close as 20cm to the camera rig. We accomplish this by leveraging the recently introduced DeepView view interpolation algorithm, replacing its underlying multi-plane image (MPI) scene representation with a collection of spherical shells which are better suited for representing panoramic light field content. We further process this data to reduce the large number of shell layers to a small, fixed number of RGBA+depth layers without significant loss in visual quality. The resulting RGB, alpha, and depth channels in these layers are then compressed using conventional texture atlasing and video compression techniques. The final, compressed representation is lightweight and can be rendered on mobile VR/AR platforms or in a web browser.

Error models for quantum computing processors describe their deviation from ideal behavior and predict the consequences in applications. But experimental behavior is rarely consistent with error models, even in characterization experiments like randomized benchmarking (RB) or gate set tomography (GST). I show how to resolve these inconsistencies, and quantify the rate of unmodeled errors, by augmenting error models with a parameterized wildcard error model. Wildcard error relaxes the model's predictions, and the amount of wildcard error required (to reconcile the model with observed data) quantifies the rate of unmodeled errors. I'll demonstrate the use of wildcard error to augment RB and GST, and to quantify leakage.

Join us for the series keynote with Thomas M. Siebel, CEO, C3.ai and author of Digital Transformation: Survive and Thrive in and Era of Mass Extinction. Mr. Siebel writes, "The confluence of four technologies—elastic cloud computing, big data, artificial intelligence, and the internet of things is fundamentally changing how business and government will operate in the 21st century". We'll discuss the game-changing technologies driving digital transformation and provide a roadmap to seize them as a strategic opportunity.

Mainstream computing has gone through multiple epochs, and the latest is now upon us. The importance of energy and space efficiency, power density, and the integration of application-driven acceleration has made mobile computing a new driver of compute architecture—and the next generation of mainstream computing.

Program in History & Philosophy of Science

presents "The Innovation Delusion: How Our Obsession with the New Has Disrupted the Work that Matters Most"

Register for the zoom workshop at
https://stanford.zoom.us/meeting/register/tJwlduuqqzMrGtAtKOtmPhcWG0aolI0VNJN4

Welcome back everyone!