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 […]
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 […]
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 […]
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 […]
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 […]
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 […]
While the human brain learns continually, mastering new tasks without forgetting the old and adapting to unfamiliar ones from context alone, modern neural networks still lack both. To bridge the gap between biological adaptivity and modern AI, we have established foundational work on sparsity as a computational principle at three levels of neural computation, through […]
Graphics Processing Units (GPUs) have become essential platforms for parallel computing, supporting applications far beyond graphics. Central to GPU programming models is its memory consistency specification (MCS), which defines the semantics of concurrent shared-memory operations and interacts with other language features to determine security guarantees such as memory safety. Understanding whether implementations conform to an […]
A community gathering of people interested in PyTorch and the projects that use it – not an official PyTorch organization. Sponsored by Red Hat and University of California Santa Cruz Location: Engineering 2, Room 180 Food, Socializing, and Excellent talks from the PyTorch Ecosystem 5:30 – 6:30 Food and Socializing 6:30 – 7:00 Talk 1 […]
Due to their increasing complexity, autonomous decision-making agents rely on increasingly advanced algorithms, from classical control theory to reinforcement learning (RL) and, more recently, large vision-language models. While these algorithms help automate the decision-making in complex systems, they bring newer attack vulnerabilities that an adversary can exploit. In this dissertation, we study the security of […]
Presenter: Mario Stanke, Professor of Bioinformatics, University of Greifswald Description: This talk will explore recent machine learning approaches for eukaryotic genome annotation. Our supervised ab initio deep gene finder, Tiberius, correctly predicts more than four times as many human protein-coding gene structures as its father, Augustus, and in some clades, it approaches the accuracy of […]
Real-time monitoring of infectious disease outbreaks calls for statistical models that recover interpretable quantities such as the time-varying reproduction number from noisy count data, track posterior uncertainty, and run on time scales compatible with daily updates. Existing methods address these aims through separate model classes. Discretized Hawkes processes, Poisson autoregressions, and distributed lag models each […]
Edge computing and distributed AI systems increasingly operate under heterogeneous resources, dynamic workloads, and frequent failures, requiring both adaptivity and fault tolerance for efficient execution. In heterogeneous edge clusters, nodes […]
Modern software increasingly relies on distributed systems to provide accessible, scalable, and reliable services. Choreographic programming brings a global perspective to distributed system development: programmers write a single program that […]
Modern high-performance GPU kernels increasingly rely on subgroup-level execution, including subgroup-level communication, subgroup operations, and matrix operations. These features are essential for workloads such as matrix multiplication and FlashAttention, but their language-level guarantees remain difficult to reason about. Existing programming models often leave unclear which threads participate in subgroup operations, when subgroup threads are required […]
Many genetics pipelines start by aligning sequencing reads to a reference genome. Aligners attempt to find the position in the reference sequence which best matches the read sequence, but this breaks down when the reads come from a sample with variation relative to the reference. A proposed alternative, pangenome graphs, is supposed to fix such […]
As quantum computing transitions from theory to practice, understanding which algorithms suit near-term devices becomes critical. Current quantum computers are severely constrained by limited qubit counts, short coherence times, and […]
Centromeres ensure proper chromosome segregation during cell division, yet the organization and regulation of centromeric chromatin within satellite DNA arrays remain incompletely understood. Here, we leverage the complete diploid human […]
Presenter: Sai Teja Peddinti, Google Abstract: As the digital landscape expands, traditional models of threat mitigation and user support are failing to keep pace with the unprecedented security, privacy, and safety challenges. Fortunately, the rise of large language models (LLMs) offers a powerful new paradigm for defense. This talk explores how LLMs are being leveraged […]
The electric grid is essential to modern society, and recent developments such as renewable energy sources (RESs), battery energy storage systems (ESSs), and microgrids (MGs) have necessitated novel computational methods […]