Cutting-edge techniques improve our way of visualizing the subsurface with seismic data, thus enhancing our ability to understand the Earth’s interior. AI has transformed seismic data analysis, elevating the role of deep learning in seismology. In this talk, I will outline my contributions to improving seismic monitoring and subsurface imaging with AI. I will first present the Phase Neural Operator (PhaseNO) for earthquake monitoring and seismic phase picking. PhaseNO measures the arrival times of P- and S-waves from continuous seismic data simultaneously across input stations with arbitrary geometries. By leveraging the spatial-temporal information, PhaseNO outperforms single-station AI algorithms by significantly detecting more earthquakes and enhancing measurement accuracy. Additionally, I will show how deep neural networks can overcome the complexities in seismic imaging by being trained to generate seismic waves. These waves, although not directly recorded, are essential for imaging the Earth’s interior. I will provide case studies on full-waveform inversion with activesource seismic data and seismic interferometry with environmental noise. In summary, these AI methods are powerful complements to traditional computational methods and hold significant promise for accelerating energy transition and mitigating geohazards.

A central theme in modern condensed matter physics is the search for zero-temperature phases of matter. Recent years have witnessed remarkable progress in this direction, leading to both experimental discoveries of new quantum phases and the development of theoretical frameworks for classifying and characterising them. Alongside symmetry, the topology of quantum many-body wavefunctions is key to understanding certain quantum phases, which are called “topological phases” of matter. In this talk, I will begin by surveying the current landscape of such phases and then discuss the exciting recent discovery of a novel class of systems – “fractons” – that fall outside existing paradigms and constitute a new frontier for theorists and experimentalists alike. The defining feature of these systems is that they host quasi-particles which are either entirely immobile or have severely restricted mobility. Through toy models, I will show that this property endows fracton phases with glassy dynamics and discuss implications for quantum information storage and processing. Finally, I will discuss an experimentally feasible setting where the slow dynamics associated with these exotic phases can be observed.

Visiting Professor, ÎÞÓÇÊÓƵ

How gravity affects light propagation can be explained by the principle that one cannot locally distinguish gravitational field from uniform acceleration. This is a consequence of the equivalence of inertial and gravitational mass. In an accelerated frame, the rectilinear motion of light appears curvilinear. In general, this uniform acceleration will be different at different points. In other words, unlike special relativity, the proper time and length measured by observers at rest in the same coordinate system but at different locations will differ. In static gravitational fields (not changing with time and produced by a non-rotating source), light rays follow Fermat’s variational principle of least (stationary) time. According to this principle, light ray trajectories are the geodesics of the associated Fermat metric. For curved space, the metric gives the distance between two infinitesimally separated points, and geodesics is the generalization of straight lines in flat space. Additionally, if the Fermat metric can be written in isotropic coordinates, light propagation can be mimicked by an optical medium in ordinary optics with an appropriate index of refraction. If two static gravitational fields share identical light rays, then the associated Fermat metrics are called projectively equivalent. This property, however, does not imply that the lengths and angles measured by observers in these fields will be identical. I will show that the projective equivalence between two Fermat metrics, when viewed in isotropic coordinates, corresponds to the refractive indices, which are proportional. This is analogous to ordinary geometric optics: Snell’s law remains invariant if refractive indices are rescaled by the same constant. I will discuss its implications for quasi-Newtonian approximations applied to cosmological observations. I will also discuss part of Inq Soncharoen’s thesis work on finding cylindrically symmetric static gravitational fields that share identical light rays.

In graphene devices, the electronic drift velocity can easily exceed the speed of
sound in the material at moderate current biases. Under this condition, the
electronic system can efficiently amplify acoustic phonons, leading to the
exponential growth of sound waves in the direction of the carrier flow. In this
talk, I will discuss our findings about how such phonon amplification can
significantly modify the electrical properties of graphene devices. We observe a
superlinear growth of the resistivity in the direction of the carrier flow when the
drift velocity exceeds the speed of sound, resulting in a sevenfold increase over a
distance of 8 µm. The resistivity growth is observed at carrier densities away from
the Dirac point and is enhanced at cryogenic temperatures. These observations
are explained by a theoretical model for the electrical amplification of acoustic
phonons — reaching frequencies up to 2.2 THz — where the wavelength is
controlled by gate-tunable transitions across the Fermi surface. These findings
provide a route to on-chip high-frequency sound generation and detection in the
THz frequency range.

There are increasing demands for robust, rapid-response computer-vision systems; however, complex images are difficult to process in real-time, especially when subtle or small features are pertinent. The visual systems of flies may serve as a model for real-time computer vision systems, since flies are capable of filtering extraneous information amid variable background conditions. Here, we develop a reliable, high-speed image processing pipeline based on the fly visual response that involves optical preprocessing, sparse sampling, and feed-forward neural networks. We show that the optical encoding from corneal nanostructures could offer essential computing functions associated with enhanced visual acuity and polarization sensitivity. Until recently, there was virtually no work related to hypothetical optical preprocessing in fly eyes from corneal nanostructures. Instead, corneal nanostructures were associated with anti-glare and hydrophobic functions; head movements or microsaccades were associated with the optical acuity of insects. Experimentally, we develop a mostly-air corneal coating that enables imaging resolutions higher than the sampling spacing typically avails. Numerically, we connect the function of these coatings with simple machine-learning neural networks. Our work points to opportunities for drone cameras and the high-speed distillation of features for edge computing applications.

Detections of neutron stars in binaries through gravitational waves offer a novel way to probe the properties of extremely dense matter. In this talk I will describe the properties of the signals we have observed, what they have already taught us, and what we expect to learn in the future. I will also discuss how information from gravitational waves can be combined and compared against other astrophysical and terrestrial probes of neutron star matter to unveil to the properties of the most dense material objects that we know of.

Multinary nitrides, i.e. nitrides with two or more cation species, are a vastly under-explored materials space with potential applications including tandem PV, water-splitting, solid-state lighting, and more. This talk will focus on the use of transmission electron microscopy (TEM) in discovery and characterization of novel ternary nitrides. Specifically, we will study how TEM can be used alongside high-throughput growth of Zn2SbN3 to evaluate crystallinity and phase purity. Additionally, we will examine the anion bonding environment in ZnSnN2 alloyed with zinc oxide. Previous work on this material system indicates that the band gap can be tuned by altering the local bonding around the anion species. This effect has been observed on a millimeter scale, but these measurements represent spatial averages over hundreds of film grains and thus may be misleading. Here, we use electron energy-loss spectroscopy (EELS) to resolve local ordering on a nanometer scale for the first time, and discuss the implications of these results for device integration.

Ellen Ferranto, “Using first principles of x-ray diffraction to identify structure of potential superconductor Na(1-x)RhO2”

Catherine Phillips, title TBA

Chris Ranlett, “Electrical resistivity measurements of the Lithium Purple Bronze”

Kevin Kim, “Extending CORE-V Wally:  A Configurable RISC-V Processor”

Tanvi Krishnan, “Trigger Simulation Analysis in the ICARUS Neutrino Detector”

Kaeshav Danesh, “An Exploration of the Transverse Field Ising Model”

Lucas Grandison, “Minimizing Error in the Electrical Characterization for Graphene Field-Effect Transistor Sensors”