Audience: Undergraduate Students
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Carrión, H. (CSE) – Deep Learning Algorithms for Medical Image Representation Learning and Understanding
AI-assisted clinical decisions in medicine, and particularly in dermatology, demand fine-grained understanding across diverse skin tones, body sites, and disease types, yet expert-annotated datasets are scarce, demographically imbalanced, and almost devoid of rare presentations. This dissertation develops four deep learning systems for this low-label, low-coverage regime. We introduce HealNet, which learns wound healing stages from…
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Sheaves, T. (CSE) – Timing Side-Channels in Commercial ReRAM: Toward ReRAM Pentimenti
Recently, a class of non-invasive hardware side-channel attacks has been discovered in field-programmable gate arrays (FPGAs). These attacks extract remnants of prior users’ activity that persist as transistor defect states within reconfigurable routing resources. These remnants are known as FPGA Pentimenti. Resistive random-access memory (ReRAM) is a compelling candidate for pentimenti-like attacks beyond FPGAs. However,…
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Kim, C. (CSE)- Toward Adaptive Graph Processing and Fault-Tolerant Agentic Inference on Heterogeneous Distributed Systems
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 differ significantly in CPU throughput, memory capacity, and network bandwidth, while modern distributed GPU clusters supporting agentic LLM inference must recover large amounts of runtime…
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Oh, S. (CSE) – Efficient Instruction Supply for Datacenter Processors
Modern datacenter CPUs lose 25–66% of execution cycles to instruction-delivery stalls. This bottleneck persists, despite the recent trend towards accelerators and GPUs, as there is continuing demand by applications that only execute on CPUs. Two workload classes dominate today’s datacenter execution cycles: hyperscale server software (databases, build systems, and content stores), whose large instruction footprints…
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Qureshi, A. (ECE) – ISoC: A Universal Impedance Spectroscopy Instrument-on-Chip in SKY130 130 nm CMOS
Electrochemical impedance spectroscopy (EIS) is the workhorse measurement behind lithium-ion battery diagnostics, biosensing, and corrosion science — yet no integrated circuit has ever delivered the complete capability of a benchtop analyzer on a single die. This dissertation presents ISoC, the first universal Impedance Spectroscopy instrument-on-chip. Designed in SkyWater 130 nm CMOS process, ISoC supports all…
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Weber, Z. (ECE) – Sustainable Bioinspired Polymer–Mineral Composites for Adaptable Repair in Conservation Applications
Every year, tens of thousands of tons of plaster-based materials are used in restoration and conservation applications, many of which are derived from non-renewable sources and discarded at the end of their service life. Here, we introduce a biodegradable, bio-derived composite based on chitosan and calcium carbonate that is composed of simple, widely available constituents…
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June Slugs and Steins with Distinguished Professor Andrew Fisher
Opportunities to enhance groundwater recharge with net metering and levee setbacks As climate change, population growth, and changing land use put increasing pressure on groundwater supplies, communities are searching for smarter and more sustainable ways to manage water. One promising approach is “managed recharge” — guiding stormwater and excess surface water back into underground aquifers…
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SOAR— Los Mejicas Grupo Folklórico
Come enjoy Los Mejicas’ 54th Anniversary Spring Show: Aqui Estamos y No Nos Vamos, Con Amor a Mi Mexico on Friday, May 29th and Saturday, May 30 at the UCSC Theater Arts Mainstage. — ADMISSION – Doors open at 7:00 p.m., and the show begins at 7:30 p.m. – Please note that seating is not…
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Harsh, B. (CSE) – SUPERSCALAR, MULTIPLE TAKEN BRANCH PREDICTOR
This work addresses improvements in branch prediction mechanism to support high perfor- mance processors. The state of the art aims to balance the prediction latency and prediction accuracy using multi level correcting predictors [27]. Prior published work focusses on scalar designs and prediction accuracy improvement for hard to predict branches employing tailor made, non generic…
