Publications

Multiomic studies at a single cell and spatial resolution are powerful approaches to define molecular and cellular landscape of the human kidney and understand etiology of failed or successful repair in acute or chronic injury. We expand KPMP AtlasV1 with clinicopathological correlations and maps of immune-fibroblast-vascular niches with insights into AKI-CKD transition.

A central question in deep learning is to understand the functions learned by deep networks. What is their approximation class? Do the learned weights and representations depend on initialization? Previous empirical work has evidenced that kernels defined by network acti- vations are similar across initializations. For shallow networks, this has been theoretically studied with random feature models, but an extension to deep networks has remained elu- sive. Here, we provide a deep extension of such random feature models, which we call the rainbow model. We prove that rainbow networks define deterministic (hierarchical) kernels in the infinite-width limit. The resulting functions thus belong to a data-dependent RKHS which does not depend on the weight randomness. We also verify numerically our mod- eling assumptions on deep CNNs trained on image classification tasks, and show that the trained networks approximately satisfy the rainbow hypothesis. In particular, rainbow net- works sampled from the corresponding random feature model achieve similar performance as the trained networks. Our results highlight the central role played by the covariances of network weights at each layer, which are observed to be low-rank as a result of feature learning.

We present nifty-ls, a software package for fast and accurate evaluation of the Lomb–Scargle periodogram. nifty-ls leverages the fact that Lomb–Scargle can be computed using a non-uniform fast Fourier transform (NUFFT), which we evaluate with the Flatiron Institute NUFFT package (finufft). This approach achieves a many-fold speedup over the Press & Rybicki method as implemented in Astropy and is simultaneously many orders of magnitude more accurate. nifty-ls also supports fast evaluation on GPUs via CUDA and integrates with the Astropy Lomb–Scargle interface. nifty-ls is publicly available at https://github.com/flatironinstitute/nifty-ls/.

The unusual structure and symmetry of low-energy states in twisted transition metal dichalcogenides leads to large in-plane spin-exchange interactions between spin-valley locked holes. We demonstrate that this exchange interaction can stabilize a gapped spin-liquid phase with a quantized spin-Chern number of three when the twist angle is sufficiently small and the system lies in a Mott insulating phase. The gapped spin liquid may be understood as arising from spinon pairing in the DIII Altland-Zirnbauer symmetry class. Applying an out of plane electric field or increasing the twist angle is shown to drive a transition respectively to an anomalous Hall insulator or an in-plane antiferromagnet. Recent experiments indicate that a spin-Chern number three phase occurs in twisted MoTe2 at small twist angles with a transition to a quantum anomalous Hall phase as the twist angle is increased above a critical value of about 2.5∘ in absence of applied electric field.

The unusual structure and symmetry of low-energy states in twisted transition metal dichalcogenides leads to large in-plane spin-exchange interactions between spin-valley locked holes. We demonstrate that this exchange interaction can stabilize a gapped spin-liquid phase with a quantized spin-Chern number of three when the twist angle is sufficiently small and the system lies in a Mott insulating phase. The gapped spin liquid may be understood as arising from spinon pairing in the DIII Altland-Zirnbauer symmetry class. Applying an out of plane electric field or increasing the twist angle is shown to drive a transition respectively to an anomalous Hall insulator or an in-plane antiferromagnet. Recent experiments indicate that a spin-Chern number three phase occurs in twisted MoTe2 at small twist angles with a transition to a quantum anomalous Hall phase as the twist angle is increased above a critical value of about 2.5∘ in absence of applied electric field.

The two-dimensional Yukawa-Sachdev-Ye-Kitaev (2d-YSYK) model provides a universal theory of quantum phase transitions in metals in the presence of quenched random spatial fluctuations in the local position of the quantum critical point. It has a Fermi surface coupled to a scalar field by spatially random Yukawa interactions. We present full numerical solutions of a self-consistent disorder averaged analysis of the 2d-YSYK model in both the normal and superconducting states, obtaining electronic spectral functions, frequency-dependent conductivity, and superfluid stiffness. Our results reproduce key aspects of observations in the cuprates as analyzed by Michon et al. (arXiv:2205.04030). We also find a regime of increasing zero temperature superfluid stiffness with decreasing superconducting critical temperature, as is observed in bulk cuprates.

Recent experiments reported the realization of a heavy Fermi liquid in AB-stacked MoTe

Inspired by a recent quantum computing experiment [Y. Kim et al., Nature (London), 618, 500–5 (2023)], we study the emergence of confinement in the transverse field Ising model on a decorated hexagonal lattice. Using an infinite tensor network state optimized with belief propagation we show how a quench from a broken symmetry state leads to striking nonthermal behavior underpinned by persistent oscillations and saturation of the entanglement entropy. We explain this phenomenon by constructing a minimal model based on the confinement of elementary excitations. Our model is in excellent agreement with our numerical results. For quenches to larger values of the transverse field and/or from nonsymmetry broken states, our numerical results display the expected signatures of thermalization: a linear growth of entanglement entropy in time, propagation of correlations, and the saturation of observables to their thermal averages. These results provide a physical explanation for the unexpected classical simulability of the quantum dynamics.

From the Golgi apparatus to endosomes, organelles in the endomembrane system exhibit complex and varied morphologies that are often related to their function. Such membrane-bound organelles operate far from equilibrium due to directed fluxes of smaller trafficking vesicles; the physical principles governing the emergence and maintenance of these structures have thus remained elusive. By understanding individual fission and fusion events in terms of active mechano-chemical cycles, we show how such trafficking manifests at the hydrodynamic scale, resulting not only in fluxes of material -- such as membrane area and encapsulated volume -- but also in active stresses that drive momentum transfer between an organelle and its cytosolic environment. Due to the fluid and deformable nature of the bounding membrane, this gives rise to novel physics, coupling nonequilibrium forces to organelle composition, morphology and hydrodynamic flows. We demonstrate how both stable compartment drift and ramified sac-like morphologies, each reminiscent of Golgi-cisternae, emerge naturally from the same underlying nonequilibrium dynamics of fission and fusion.

Machine learning based surrogate models offer researchers powerful tools for accelerating simulation-based workflows. However, as standard datasets in this space often cover small classes of physical behavior, it can be difficult to evaluate the efficacy of new approaches. To address this gap, we introduce the Well: a large-scale collection of datasets containing numerical simulations of a wide variety of spatiotemporal physical systems. The Well draws from domain experts and numerical software developers to provide 15TB of data across 16 datasets covering diverse domains such as biological systems, fluid dynamics, acoustic scattering, as well as magneto-hydrodynamic simulations of extra-galactic fluids or supernova explosions. These datasets can be used individually or as part of a broader benchmark suite. To facilitate usage of the Well, we provide a unified PyTorch interface for training and evaluating models. We demonstrate the function of this library by introducing example baselines that highlight the new challenges posed by the complex dynamics of the Well. The code and data is available at https://github.com/PolymathicAI/the_well.