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DTSTART;TZID=America/Los_Angeles:20260602T130000
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DTSTAMP:20260601T104700
CREATED:20260526T162137Z
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SUMMARY:Sheaves\, T. (CSE) - Timing Side-Channels in Commercial ReRAM: Toward ReRAM Pentimenti
DESCRIPTION: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\, unlike FPGAs\, where sophisticated on-chip sensors capable of detecting pentimenti have been well-studied\, non-invasive pentimenti recovery in commercial ReRAM must rely on measurements of observable write latency. These measurements are dominated by data-dependent structural biases that obscure any underlying defect-dynamics signal. In this dissertation\, we demonstrate that the structural and stochastic components of commercial ReRAM write latency can be decoupled and recovered through non-invasive timing analysis alone. Our results provide the reverse engineering and measurement infrastructure for future study of ReRAM pentimenti by isolating the component of programming latency sensitive to defect dynamics. \nEvent Host: Tyler Sheaves\, Ph.D. Candidate\, Computer Science & Engineering  \nAdvisor: Dustin Richmond  \nZoom: https://ucsc.zoom.us/j/92729427179?pwd=BpYLqft18YdOU0mDdQWs8erID2VcHi.1 \nPasscode: 939530
URL:https://events.ucsc.edu/event/sheaves-t-cse-timing-side-channels-in-commercial-reram-toward-reram-pentimenti/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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DTSTART;TZID=America/Los_Angeles:20260602T134500
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SUMMARY:Figuerres\, S. (ECE) - Ion Transport Mechanisms for Bioelectronics
DESCRIPTION:Ion transfer as the movement of charged species across spaces and interfaces is the basis of signaling in nearly all biological systems. My research is grounded in the idea that precise control over ion transfer enables direct manipulation of biological function. Specifically\, I focus on how ion transport can be engineered to regulate both collective behavior in microbial communities\, as well as cellular sensing through ion channels. In comparison to traditional means such as passive diffusion\, mediated ion transfer via ion pumps and ion channels creates opportunity for high precision control of biological signaling. My work centers on ion transfer as a fundamental mechanism for biological signaling and control across systems. Using bioelectronic ion pumps and mechanosensitive ion channels to precisely manipulate the movement of charged species\, I aim to investigate ion transfer at the interface of biology and electronics. \nEvent Host: Sydnie Figuerres\, Ph.D. Student\, Electrical & Computer Engineering  \nAdvisor: Marco Rolandi
URL:https://events.ucsc.edu/event/figuerres-s-ece-ion-transport-mechanisms-for-bioelectronics/
LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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DTSTART;TZID=America/Los_Angeles:20260602T140000
DTEND;TZID=America/Los_Angeles:20260602T160000
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SUMMARY:Bose\, S. (ECE) - Learning-Augmented Optimization\, Control\, and Inference in Modern Power Systems
DESCRIPTION: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 for planning and operations. Machine learning offers a promising lever here\, both as an accelerator for and proxy to traditional optimization-based problems. In this thesis\, we consider learning-based algorithms for three such problems: load restoration in islanded microgrids\, accelerated optimal power flow\, and short-term load forecasting. \nWe first address load restoration of islanded MGs containing RESs\, battery ESSs\, microturbines\, and inverter-based devices. We formulate the problem as a multi-timestep nonconvex optimization and decompose it via model predictive control (MPC). We develop novel convex relaxations of the nonconvex constraints\, including power flow\, ESS charge/discharge complementarity\, and inverter voltage-reactive power relations\, to generate approximately feasible solutions\, and then improve on them via a reinforcement learning method based on constrained policy optimization (CPO) that respects the original nonconvexity. \nWe then turn to accelerating convexified optimal power flow (C-OPF) via constraint screening\, presenting an analysis that reduces screening for certain C-OPF families to a rank-based test. Building on this\, we introduce Mixture of Gradient Experts (MoGE)\, an architecture that learns optimal dual variables from historical C-OPF solutions and combines them with the KKT conditions to eliminate likely non-binding constraints\, with a recovery step that guarantees the reduced problem’s solution matches the original’s. We demonstrate speedups on grids with up to 10\,000 buses. \nFinally\, we consider short-term load forecasting (STLF) from smart-meter data\, motivated by the role of forecasts as inputs to the optimization problems above. To address consumer-data privacy and the heterogeneity of consumption patterns\, we introduce personalization layers for federated learning (PL-FL)\, in which each client trains a model with a local personalized component and a shared aggregated component\, and extend it to a privacy-preserving variant (PPFL) that applies differential privacy to the shared component. Separately\, we present an empirical study of forecasting architectures spanning classical recurrent networks to fine-tuned time-series foundation models\, holding dataset size and parameter count constant to isolate architectural contribution. All methods are evaluated on subsets of the NREL ComStock dataset. \nEvent Host: Shourya Bose\, Ph.D. Candidate\, Electrical & Computer Engineering  \nAdvisor: Yu Zhang \nZoom: https://ucsc.zoom.us/j/93511298189?pwd=eAyDKdMirlVqYGUsbhQCccoBM9gDV6.1 \nPasscode: 462014
URL:https://events.ucsc.edu/event/bose-s-ece-learning-augmented-optimization-control-and-inference-in-modern-power-systems/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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