Most optimization problems face the challenge of computing an optimum solution requiring superpolynomial time. In particular, they are classified as NP-hard problems that have no polynomial-time algorithm to date. Instead, […]
Simulation is a coinductive proof technique to assert the behavioral equivalence of computing systems that has seen fruitful application in distributed systems, concurrent process calculi, and programming languages, since the 1970’s. We have also utilized simulation in our prior work, where we formalized and proved a folklore claim that the state-based and operation-based approaches to […]
In this work, we develop numerical methods for conservation laws that explore statistical, structure-preserving, and machine-learning-based approaches, each built on top of traditional numerical solvers. First, we develop a general […]
Hardware design flows have become increasingly complex as modern chips integrate billions of transistors and rely on aggressive synthesis optimizations to meet performance, area, and power targets. While these transformations […]
Modern applications, such as data center workloads, have become increasingly complex. These applications primarily operate on massive datasets, which involve large memory footprints, irregular access patterns, and complex control and […]
Reinforcement Learning (RL) is a machine learning paradigm that trains a decision maker, or policy, by learning from interaction with an environment. The power of RL lies in its ability […]
Transient execution attacks are serious security threats in modern-day processors. Out-of-order execution compels the processor to access data that should not be otherwise perceived. Leakage of that secret information creates […]
The growing integration of Large Language Models (LLMs) into real-world applications has heightened concerns about their trustworthiness, as models may reveal private information, reproduce copyrighted content, propagate biases, or generate […]
While Large Language Models exhibit remarkable capabilities, their reliance on the standard Transformer architecture imposes prohibitive computational costs and quadratic memory complexity. To bridge the gap between biological efficiency and […]
Quantum Key Distribution (QKD) provides information-theoretic security for cryptographic key establishment, but existing protocols exhibit limited noise tolerance, restricting their applicability in practical quantum channels with finite resources. This work […]
Translation is a central and highly regulated step of gene expression, yet there are few quantitative, high-throughput tools to study translation. Existing methods such as sucrose gradients provide only bulk ribosome counts, while Ribo-Seq offers positional information in the genome but destroys long-range structure and transcript expression information. Because of these limitations, many fundamental questions […]
Transcriptomic profiling has been transformative in pediatric oncology. Pediatric cancers arise from disrupted developmental programs. Their impaired transcriptional states reflect cell lineage infidelity, aberrant differentiation, and immune-microenvironment interactions distinct from […]
Today, both individual and collective memories are increasingly mediated by digital platforms. Both are fundamentally enmeshed in platform ecosystems that orient around commercial imperatives very much at odds with community […]
Title: Understanding the advising praxes central to student success at a four-year Hispanic-Serving Research Institution Presenter: Dr. Lydia Iyeczohua Zendejas Location: Via Zoom (link provided via RSVP) Abstract: Higher education scholars increasingly recognize academic advising as a critical strategy for supporting the persistence of systemically marginalized students. Since the 1990s, UC Santa Cruz has undergone […]
GPUs are the workhorses of modern AI, widely deployed and developed by many vendors including Apple, Qualcomm, Intel, AMD, and NVIDIA. While these GPUs all offer high compute potential, programming […]
We present a comprehensive numerical investigation examining how vertical asymmetry in compressible convection affects overshooting and the transport of large-scale magnetic fields from convective to stably stratified regions. Using three-dimensional direct numerical simulations, we systematically vary the superadiabaticity and stratification of a convective layer to control the vertical asymmetry of the flow and analyze its […]
Diffusion-based text-to-image models can generate impressive images, but they largely treat an image as a single, flat output, which makes precise editing of individual elements difficult. This proposal studies layered […]
Vision encoders have demonstrated significant performance gains in visual generation and multimodal reasoning. These improvements are primarily attributed to the scaling of data, model capacity, and compute. However, this progress […]
The Huerta Center is proud to present a brown bag presentation by Graduate Student Research Awardee Valeria Alonso Blanco. She will present on a qualitative study that explores how undocumented Latinx transfer students navigate institutional support, belonging, and barriers at a four-year Hispanic Serving Institution (HSI). Findings reveal gaps between institutional commitments and student […]
Diffusion-based generative models offer a principled framework for probabilistic forecasting, but we show they suffer from a fundamental spectral collapse when applied to turbulent flows. A Fourier-space analysis of the […]