Baskin School of Engineering

While large language models can match or exceed human performance, they do so with memory and energy costs orders of magnitude greater than biological cognition. We investigate sparsity as a brain-inspired computational principle to address both. We first establish a framework for evaluating small language model construction methods, using the next-token logit distribution as a […]

A ubiquitous task in statistical practice is that of variable selection, identifying which of a large set of features are the relevant ones. As data sets with a large number of observations have become increasingly common, new theoretical and computational challenges for model selection have emerged. We consider the variable selection problem for linear models […]

As datacenters scale up and become more geographically distributed, wide-area network inter-datacenter traffic, which typically consists of data-heavy tasks, has become increasingly prevalent. Some of the noteworthy challenges raised by the coexistence and interaction between inter- and intra-datacenter traffic are the differences in their QoS requirements, link utilization, and round-trip times. To the best of […]

Medical image representation has long been dominated by voxel-grid matrices. While their inherent structure and order work efficiently for various linear transformations and provide a seamless visualization method on monitors, they fail to preserve the topology of the scan and to encode sparse information in a memory-efficient way.   The recent emergence of machine learning-based continuous coordinate-based […]

Deep learning models trained on simulation and reanalysis data can now emulate turbulent geophysical flows at a small fraction of the computational cost of numerical solvers. Their scientific utility depends on physical consistency, which for the systems studied here rests in large part on spectral fidelity, the accurate reconstruction of variance across spatial scales. This […]

Host-microbe systems are core to some of biology’s most consequential interactions, from the pathogens that drive infectious disease to symbionts affecting agricultural pest control and vector-borne disease transmission. Yet unlike the model organisms that have driven most of modern molecular biology, the microbes at the center of these interactions are rarely genetically tractable: many cannot […]

Advances in microscopy have enabled the collection of high-quality single-cell datasets, providing new opportunities to identify the mechanisms underlying complex biological processes. In this work, we develop machine-learning frameworks using single-cell temporal data with the goal of predicting and providing insights into these mechanisms. We produce frameworks for two biological systems, bacterial spore germination, the […]