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SUMMARY:Burbano\, L. (CS) - Security of autonomous decision-making agents: From control systems to embodied AI
DESCRIPTION: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 autonomous decision agents that use control systems\, RL\, and AI. We focus on the security of cyber-physical and autonomous cyber-defense systems. In particular\, we study how an attacker can compromise decision-making agents. \nFor control systems\, this dissertation studies the existence of backdoor attacks against control systems that rely on data and proposes a defense strategy against the sensors of control systems. \nFor reinforcement learning\, we study the security of autonomous cyber-defense (ACD)) agents that automatically respond to attackers’ actions in a network. While previous works focus on creating agents\, we study an adversary who compromises the agent’s own infrastructure\, manipulating the information it observes to steer the network toward an attacker-chosen state. We also propose a defense strategy that focuses on determining if an attacker is compromising the ACD. \nFinally\, we study the security of embodied AI\, where CPS rely on large vision-language models (LVLMs) for decision-making. We propose a novel attack that can cause an agent to make unsafe decisions by presenting a well-designed textual sign via the visual modality. While previous attacks against neural network-based algorithms rely on creating adversarial patches without semantic meaning\, in this work\, we exploit the fact that LVLMs can understand text. \n  \nEvent Host: Luis Burbano\, Ph.D. Candidate\, Computer Science  \nAdvisor: Alvaro Cardenas \nZoom: https://ucsc.zoom.us/j/92373119649?pwd=BLFQMrGkOxJVXnjrJhXqudN1iciZAn.1 \nPasscode: 160434\n   
URL:https://events.ucsc.edu/event/burbano-l-cs-security-of-autonomous-decision-making-agents-from-control-systems-to-embodied-ai/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260625T130000
DTEND;TZID=America/Los_Angeles:20260625T140000
DTSTAMP:20260627T000852
CREATED:20260622T225613Z
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SUMMARY:BME/Genomics Seminar: Supervised and Unsupervised DeepGene Finding and Genome Foundation Models
DESCRIPTION:Presenter: Mario Stanke\, Professor of Bioinformatics\, University of Greifswald \nDescription: 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 evidence-based pipelines such as BRAKER. Genome foundation models can automatically learn annotation-relevant embeddings from unannotated training genomes. I will also present Vipsania\, the unsupervised wife of Tiberius. Vipsania is a genome foundation model that learns hidden Markov models to find gene structures from naked genomes using a BERT-style masked language model objective. Finally\, I will report on ongoing efforts to use phylogenetic teaching signals from whole-genome vertebrate alignments to train a genome foundation model comparatively. \nKeywords: hidden Markov model layer\, linear recurrent unit\, continuous-time Markov chains on trees \nBio: Mario Stanke studied mathematics and computer science at the University of Göttingen and UCBerkeley\, and received his Dr. rer. nat. from the University of Göttingen. He completed a postdoctoral fellowship in the Haussler lab at UC Santa Cruz in 2006–2007. He has been a Professor of Bioinformatics at the Institute of Mathematics and Computer Science at the University of Greifswald since 2010. \nHosted by: Genomics Institute \nLocation: E2-599 (limited space) \nZoom: https://ucsc.zoom.us/j/95380317295?pwd=0HbwSYKRQqyCtBcPXGfoB0tPOsA16V.1
URL:https://events.ucsc.edu/event/bme-genomics-seminar-supervised-and-unsupervised-deepgene-finding-and-genome-foundation-models/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260615T130000
DTEND;TZID=America/Los_Angeles:20260615T150000
DTSTAMP:20260627T000852
CREATED:20260609T215214Z
LAST-MODIFIED:20260609T215214Z
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SUMMARY:Tang\, M. (STAT) - Bayesian Modeling and Scalable Inference for Count Time Series in Infectious Disease Surveillance
DESCRIPTION: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 capture self-exciting transmission through alternative parameterizations of the same conditional mean structure\, but they have been developed across separate software packages with model-specific inference routines\, which makes structural model comparison cumbersome in practice. This dissertation develops a unified Bayesian framework for count time series in disease surveillance\, organized around three threads. First\, a class of dynamic generalized transfer function models places the three modeling families inside a common modular state-space class built from six independent components. A hybrid variational algorithm combines sequential Monte Carlo on the latent trajectory with stochastic gradient ascent on the static parameters. Second\, a multivariate extension to spatially connected regions\, a Bayesian network Hawkes model\, jointly estimates time-varying source-specific reproduction numbers and a sparse transmission network learned from data through a regularized horseshoe prior. The observed reproduction number at each\nlocation is decomposed into a local component and an imported component. Posterior inference proceeds through a blocked Markov chain Monte Carlo sampler\, with a particle Laplace variational counterpart developed for routine refits at larger spatial scales. Third\, an R package implements the unified univariate framework through a compositional specification interface aligned with the six modular components\, with the two inference engines available behind a single entry point. The methods are illustrated through simulation studies and applications to daily COVID-19 case counts from Santa Cruz County and from ten California counties. \nEvent Host: Meini Tang\, Ph.D. Candidate\, Statistical Science  \nAdvisor: Raquel Prado \nZoom: https://ucsc.zoom.us/j/97990210796?pwd=e59WbsNrYgYSITmMw0OIT5f1SQThEN.1 \nPasscode:  479460
URL:https://events.ucsc.edu/event/tang-m-stat-bayesian-modeling-and-scalable-inference-for-count-time-series-in-infectious-disease-surveillance/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260609T120000
DTEND;TZID=America/Los_Angeles:20260609T130000
DTSTAMP:20260627T000852
CREATED:20260526T161617Z
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SUMMARY:Kim\, C. (CSE)- Toward Adaptive Graph Processing and Fault-Tolerant Agentic Inference on Heterogeneous Distributed Systems
DESCRIPTION: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 state under routine failures. This dissertation addresses these challenges through two systems: Zsiga\, an adaptive distributed graph processing system for heterogeneous edge clusters\, and Forte\, a fault-tolerant KV cache recovery system for distributed agentic LLM inference. \nZsiga improves connected component computation through capacity-aware graph partitioning and runtime-adaptive boundary migration\, reducing execution time by up to 90.9% while eliminating out-of-memory failures under heterogeneous resource constraints. Forte addresses KV cache recovery for long-running agentic inference workloads\, where failures can erase accumulated reasoning trajectories and tool interaction histories. Forte exploits the observation that not all KV blocks are equally critical\, introducing criticality-aware erasure coding\, domain-diverse placement\, and prioritized foreground recovery to enable efficient recovery under correlated failures. Experimental results show that Forte is the only evaluated scheme that successfully resumes execution under correlated domain failures\, reducing foreground stall by 89.7% and end-to-end recovery latency by 50.6–58.9% at 2.0$\times$ memory overhead. Together\, these systems demonstrate how adaptivity and fault tolerance can improve the efficiency and resilience of distributed systems in heterogeneous and failure-prone environments. \nEvent Host: Chaeeun Kim\, Ph.D. Student\, Computer Science & Engineering \nAdvisor: Chen Qian & Liting Hu \nZoom: https://ucsc.zoom.us/j/9863615188?pwd=kTka0aZXJ070tor1EKvrt3X6AveBRp.1 \nPasscode:  cG5SL8 \n  \n 
URL:https://events.ucsc.edu/event/kim-c-cse-toward-adaptive-graph-processing-and-fault-tolerant-agentic-inference-on-heterogeneous-distributed-systems/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260609T103000
DTEND;TZID=America/Los_Angeles:20260609T130000
DTSTAMP:20260627T000852
CREATED:20260526T194326Z
LAST-MODIFIED:20260526T194445Z
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SUMMARY:Shen\, G. (CSE) - Library-Level Choreographic Programming
DESCRIPTION:Modern software increasingly relies on distributed systems to provide accessible\, scalable\,\nand reliable services. Choreographic programming brings a global perspective to distributed\nsystem development: programmers write a single program that describes the behavior of a\nwhole system\, and a compiler projects that global description into local programs run by each\nnode. By making distributed control flow explicit\, choreographic programming can rule out\nimportant classes of errors\, including deadlocks. This dissertation investigates library-level\nchoreographic programming\, an approach that embeds choreographic abstractions in existing\nhost languages rather than implementing them as standalone languages. The central claim\nis that the library approach can retain the safety and global reasoning principles of chore-\nographic programming while taking advantage of the host language’s features\, tools\, and\necosystem. First\, we present HasChor\, a first-of-its-kind library-level choreographic program-\nming language in Haskell\, built using freer monads. Next\, we generalize the design underlying\nHasChor to algebraic effects\, giving library-level implementations in Agda and OCaml. Fi-\nnally\, we present Parkour\, a backward-compatible extension to HasChor that adds a construct\nfor expressing parallel behavior in choreographies. Together\, these systems show that chore-\nographic programming can be implemented\, generalized\, and extended at the library level\,\nmaking global programming techniques available within practical host-language settings. \nEvent Host: Gan Shen\, Ph.D. Candidate\, Computer Science & Engineering  \nAdvisor: Lindsey Kuper  \nZoom: https://ucsc.zoom.us/j/93790633483?pwd=Jg8JlISsrwjLBaQIi1KdHk36bNMIv7.1 \nPasscode: 902041 \n 
URL:https://events.ucsc.edu/event/shen-g-cse-library-level-choreographic-programming/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260605T080000
DTEND;TZID=America/Los_Angeles:20260605T100000
DTSTAMP:20260627T000852
CREATED:20260527T160819Z
LAST-MODIFIED:20260527T160819Z
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SUMMARY:Chen\, Z. (CSE) - GPU Subgroup Semantics for Portable High-Performance Kernels
DESCRIPTION: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 to execute together\, and what synchronization is implied by subgroup-level operations. This ambiguity becomes especially important in portable GPU programming\, where the same kernel may run across devices with different subgroup sizes\, compiler stacks\, browser backends\, and hardware execution behavior. \nMy research studies how precise subgroup semantics can support portable and correct high-performance GPU kernels. SIMT-Step\, my main completed work\, develops a formal and flexible operational semantics for GPU subgroup execution. It introduces dynamic blocks to specify converged subgroup execution and subgroup-operation participation\, classifies instructions as independent\, synchronous\, or collective to express a spectrum of candidate subgroup semantics\, and validates these models through a TLA+ implementation and an empirical fuzzing study across real GPUs. My systems work studies how subgroup-dependent kernels behave in practice\, including WebGPU FlashAttention kernels for LLM inference\, tunable WebGPU kernels for performance portability\, and Vulkan-based execution for heterogeneous SoCs. Building on these foundations\, my proposed verification work develops data-race-free checking techniques for ML kernels that rely on subgroup operations and matrix operations. Together\, these projects aim to clarify the execution guarantees that optimized GPU kernels can rely on and to support portable GPU programming systems whose performance and correctness can be reasoned about across diverse hardware. \nEvent Host: Zheyuan Chen\, Ph.D. Student\, Computer Science & Engineering \nAdvisor: Tyler Sorensen \nZoom: https://ucsc.zoom.us/j/92175288480?pwd=jGajtqerVbKuW1FPNr3awqOYoxATsp.1&jst=3 \nPasscode: 693354
URL:https://events.ucsc.edu/event/chen-z-cse-gpu-subgroup-semantics-for-portable-high-performance-kernels/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260604T100000
DTEND;TZID=America/Los_Angeles:20260604T120000
DTSTAMP:20260627T000852
CREATED:20260528T203838Z
LAST-MODIFIED:20260528T203838Z
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SUMMARY:Okamoto\, F. (BMEB) - Improving read-to-pangenome alignment in complicated genomic regions
DESCRIPTION: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 “reference bias” by including known variation within the reference itself. Yet read alignment is still difficult in graph regions featuring certain complex variation. I will address specific known limitations of pangenome read alignment by developing better methods to align reads to pangenomes (1) in centromeres\, (2) in regions with cycles\, (3) when a “split”/supplementary alignment is required\, and (4) for RNA-seq reads. \nEvent Host: Faith Okamoto\, Ph.D. Student\, Biomolecular Engineering & Bioinformatics \nAdvisor: Benedict Paten \nZoom: https://ucsc.zoom.us/j/3543092299?pwd=5xbPfPhxvoJlx24tusiOwPuLSjzwzb.1 \nPasscode: 767376
URL:https://events.ucsc.edu/event/okamoto-f-bmeb-improving-read-to-pangenome-alignment-in-complicated-genomic-regions/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260604T100000
DTEND;TZID=America/Los_Angeles:20260604T120000
DTSTAMP:20260627T000852
CREATED:20260512T161057Z
LAST-MODIFIED:20260512T171434Z
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SUMMARY:Kordonowy\, S. (CS) - The Role of Circuits in Near-Term Quantum Computation
DESCRIPTION: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 high error rates that quickly degrade computation into noise. This thesis addresses two interconnected questions: what non-trivial computational tasks can near-term devices execute and how should algorithms be implemented to exploit available hardware? We examine circuit design as the bridge between these concerns\, analyzing how gate choices determine algorithmic efficiency and computational hardness. By deriving explicit circuit constructions\, we obtain tangible cost estimates for practical quantum computation\, enabling precise comparisons to classical approaches and identification of break-even points in system size and error rates. Understanding these trade-offs is essential for near-term quantum computing\, where experiments are expensive and error-prone. \nWe apply these ideas to three domains:\n1. Streaming: we provide circuit implementations for the Boolean Hidden Matching problem\, a combinatorial problem which exhibits exponential space separation compared to classical algorithms. We give explicit resource estimates and experimentally validate on Quantinuum’s trapped-ion hardware. We demonstrate that quantum advantage persists even when accounting for error correction overhead. \n2. Variational eigensolving: We examine how gate set choices influence trainability of variational quantum eigensolvers and provide Lie algebraic decompositions for differing gate sets. These decompositions are in turn used as a warm-starting heuristic to overcome barren plateaus\, a common problem in quantum machine learning tasks\, and improve convergence. We apply this technique to three combinatorial problems with primary focus on portfolio optimization. \n3. Cryptography: We develop a digital signature scheme based on circuit learning hardness and classical shadows. Error detection plays a direct role in the circuits considered\, with a focus on practical implementation for near-term devices. \nThese case studies demonstrate how careful circuit design can either mitigate near-term\nconstraints or expose where error correction becomes necessary to achieve quantum\nadvantage. \n  \nEvent Host: Steven Kordonowy\, Ph.D. Candidate\, Computer Science  \nAdvisor: Alexandra Kolla  \nZoom: https://ucsc.zoom.us/j/9524731001?pwd=MzdrNmhidVBsTXNFbktBcjEvNmZIQT09&omn=96338496668  \nPasscode: J29XGi \n  \n 
URL:https://events.ucsc.edu/event/kordonowy-s-cs-the-role-of-circuits-in-near-term-quantum-computation/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260603T150000
DTEND;TZID=America/Los_Angeles:20260603T180000
DTSTAMP:20260627T000852
CREATED:20260602T193539Z
LAST-MODIFIED:20260602T193539Z
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SUMMARY:Xu\, D. (BMEB) - Interplay Between CENP-A\, DNA Methylation\, and H3K9me3 in Defining Centromere Identity
DESCRIPTION: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 genome benchmark (T2T-HG002) to provide a detailed study of centromeric sequence and chromatin architecture on individual haplotypes. Using adaptive-sampling-enriched\, ultra-long-read DiMeLo-seq\, we achieve single-molecule chromatin profiling across all centromeres\, revealing that along single chromatin fibers\, CENP-A\, the histone variant specifying centromere identity\, forms multiple discrete subdomains within hypomethylated centromere dip regions (CDRs) that are flanked by H3K9me3-enriched heterochromatin. Despite underlying sequence variation\, CDRs localize to sequence-homogeneous domains and maintain relatively balanced CENP-A dosage and aggregate length across all chromosomes and between haplotypes. Further\, we show that bidirectional changes to centromeric and pericentromeric DNA methylation are accompanied by changes to centromeric chromatin architecture. In passaged cells with centromeric hypomethylation\, subdomain boundaries are eroded\, and adjacent CENP-A domains tend to merge and expand. Conversely\, in pluripotent stem cells with centromeric hypermethylation\, CDRs are fundamentally reorganized\, such that discrete hypomethylated domains are frequently consolidated into broader contiguous tracts. These methylation-associated CDR restructuring events suggest that DNA methylation acts as a principal regulator of human centromere organization\, with implications for understanding centromere plasticity\, epigenetic inheritance\, and chromosomal instability in development and disease. \nEvent Host: Daniel Xu\, PhD Candidate\, Biomolecular Engineering & Bioinformatics  \nAdvisor: Karen Miga \nZoom: https://ucsc.zoom.us/j/99197563825?pwd=meEWoi4ffdZ0K4Syo09Jr0ZbpPThMk.1
URL:https://events.ucsc.edu/event/xu-d-bmeb-interplay-between-cenp-a-dna-methylation-and-h3k9me3-in-defining-centromere-identity/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260603T110000
DTEND;TZID=America/Los_Angeles:20260603T121500
DTSTAMP:20260627T000852
CREATED:20260529T172740Z
LAST-MODIFIED:20260529T172740Z
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SUMMARY:
DESCRIPTION:Presenter: Sai Teja Peddinti\, Google \nAbstract: 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 to improve digital privacy\, security\, and safety from the network layer down to the individual user. We will examine how LLMs are opening new frontiers in cybersecurity and solving complex challenges\, such as: inferring device identities through semantic analysis of network traffic\, mapping global privacy trends by distilling over a decade of app reviews\, and analyzing user help-seeking behaviors across millions of social media interactions. Ultimately\, this talk will demonstrate how AI is evolving from a technological novelty into an essential foundation for scalable\, proactive\, and human-centric digital defense. \nBio: Sai Teja Peddinti (https://www.saitejapeddinti.com) is a Staff Research Scientist at Google\, where his research focuses on the intersection of Privacy\, Security\, Artificial Intelligence\, and Data Mining. His research employs a multidisciplinary approach\, blending qualitative and quantitative methods to investigate user and developer privacy preferences and translate those insights into scalable privacy/security features using LLMs and large-scale data analysis. Sai Teja holds a Ph.D. in Computer Science from the NYU Tandon School of Engineering (2014). His research has garnered industry recognition\, including the IAPP SOUPS Privacy Award and finalist placements in major applied research competitions. Throughout his education\, he has been honored with numerous accolades. \nHosted by: Professor Ram Sundara Raman \nDate and Time: Wednesday\, June 3\, from 11:00 am – 12:15 pm \nLocation: Engineering 2\, Room E2-180 (Refreshments such as fruit\, pastries\, coffee\, and tea will be provided.) \nZoom Option: https://ucsc.zoom.us/j/93445911992?pwd=YkJ2TQtF79h0PcNXbEcpZLbpK0coiY.1&jst=3
URL:https://events.ucsc.edu/event/12348/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260602T140000
DTEND;TZID=America/Los_Angeles:20260602T160000
DTSTAMP:20260627T000852
CREATED:20260527T204156Z
LAST-MODIFIED:20260527T204156Z
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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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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260602T130000
DTEND;TZID=America/Los_Angeles:20260602T150000
DTSTAMP:20260627T000852
CREATED:20260526T162137Z
LAST-MODIFIED: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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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260601T123000
DTEND;TZID=America/Los_Angeles:20260601T133000
DTSTAMP:20260627T000852
CREATED:20260526T191332Z
LAST-MODIFIED:20260526T191332Z
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SUMMARY:CM Seminar - Alex Olwal\, "Human-Centered Augmentation: Interacting with Matter\, Humans\, and Machines"
DESCRIPTION:Presented by: Alex Olwal \nDescription: “In this talk\, I will share my perspectives on the evolution and future of human-centered augmentation\, through the lens of two decades of research and development. Drawing from experiences across academia and industry\, I will discuss insights from having led projects in augmented reality\, accessibility\, electronic textiles\, novel sensing and displays\, and their implications for emerging AI-augmented interfaces.” \nBio: Alex Olwal is a research scientist and engineering leader focused on interaction technology and human augmentation. During his tenure at Google\, he founded the Interaction Lab and Biointerfaces team \, and tech transferred accessibility-focused language glasses to the Augmented Reality product organization\, where he established the Augmented Language Team. As an engineering manager in the product organization\, he evolved his team’s scope to deliver Human-AI language capabilities\, including speech perception\, natural language understanding\, real-time translation and captions\, and generative AI. The team’s conversational AI experiences for AR glasses were a key feature in the Google I/O 2022 keynote. Alex’s research has spanned augmented reality\, ubiquitous computing\, wearables\, and accessibility\, often leveraging novel opportunities in display technology\, sensing\, soft electronics\, and machine intelligence. He is passionate about impactful problems that can be addressed through Human-AI interfaces\, real-time interaction techniques and transformative applications. \nPreviously\, Alex conducted research at MIT Media Lab as a postdoctoral fellow after receiving his Ph.D. from KTH Royal Institute of Technology\, with research conducted at Columbia University\, UC Santa Barbara\, and Microsoft Research (research internship). He has held faculty positions at Stanford University\, Rhode Island School of Design\, and KTH. \nWebsite: www.olwal.com \nHosted by: Professor Katherine Isbister \nWhen: Monday\, June 1\, 2026 from 12:30PM to 1:30PM \nLocation:  \nIN-PERSON @ UCSC Main Campus\, E2-280. \nViewing room @ SVC 3212. \nLUNCH WILL BE PROVIDED AT BOTH LOCATIONS! Faculty and students are highly encouraged to attend. \nZoom info: \nhttps://ucsc.zoom.us/j/97081260699?pwd=eyt5f4CAEHHLQWBhdaLA693T3gecaj.1\nMeeting ID: 970 8126 0699\nPasscode: 047011
URL:https://events.ucsc.edu/event/cm-seminar-alex-olwal-human-centered-augmentation-interacting-with-matter-humans-and-machines/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260601T104000
DTEND;TZID=America/Los_Angeles:20260601T114500
DTSTAMP:20260627T000852
CREATED:20260528T185942Z
LAST-MODIFIED:20260528T185942Z
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SUMMARY:ECE 290 Seminar: Memristors for a brain-scale neuromorphic chip
DESCRIPTION:Presenter: Sung-Mo “Steve” Kang\, Distinguished Professor Emeritus and Research Professor\, UC Santa Cruz \n  \nDescription: Recently\, applications of artificial intelligence (AI) have far outpaced Moore’s law in chip development\, thus creating an increasingly large gap between user demand and the supply that the semiconductor industry can deliver. In this talk\, we will discuss the unique roles of memristor technologies that can be leveraged to develop scaled-up AI neural networks\, particularly spiking neural networks (SNNs) for brain-like neuromorphic computing and unsupervised learning with high energy efficiency. Open-source memristor circuit designs\, along with open-source software\, may facilitate the development of micro- and nano-electronic systems that emulate brain functions. In this venue\, we will discuss how to harness memristor- based circuits and systems to build memristor neurons\, synapses\, and their interconnects for ultra-high packing density\, low power consumption\, and the fabrication services needed to enable innovation. \n  \nBio: Sung-Mo “Steve” Kang is a Distinguished Professor Emeritus and Research Professor at the Baskin School of Engineering\, UC Santa Cruz; Chancellor Emeritus of UC Merced; and President Emeritus of KAIST. He has published more than 500 journal and conference papers\, authored 10 books\, and holds 17 patents. Before returning to academia in 1985\, he led the development of the world’s premier fully CMOS 32-bit VLSI microprocessor chipsets for telecommunications and computing applications as a technical supervisor at AT&T Bell Laboratories in Murray Hill\, New Jersey. This work was recognized as an IEEE Milestone in February 2025. He has received honors\, including best paper awards\, induction into the Silicon Valley Engineering Hall of Fame\, the Alexander von Humboldt Senior US Scientists Award\, the IEEE Millennium Medal\, the IEEE Mac Van Valkenburg Circuits and Systems (CAS) Society Award\, the IEEE CAS Society Technical Excellence Award\, the US Semiconductor Research Corporation (SRC) Technical Excellence Award\, the IEEE Leon K. Kirchmayer Graduate Teaching Technical Field Award\, and the IEEE CAS Society John Choma Education Award\, as well as the Chang-Lin Tien Education Leadership Award. Dr. Kang is a Life Fellow of the IEEE and a Fellow of the Association for Computing Machinery (ACM)\, the American Association for the Advancement of Science (AAAS)\, and the Asia-Pacific AI Association. He is a life member of the European Academy of Sciences and Arts and the Korean Academy of Science and Technology\, and a foreign member of the National Academy of Engineering\, Korea. He received his B.S. from Fairleigh Dickinson University\, Teaneck\, New Jersey\, in 1970; an honorary B.S. from Yonsei University; an M.S. from the State University of New York at Buffalo in 1972; and a Ph.D. from the University of California at Berkeley in 1975\, all in electrical engineering. \n  \nHosted by: Professor Soumya Bose\, ECE Department \nZoom Link: https://ucsc.zoom.us/j/97975378707?pwd=ljcgaCfhMmhZ88Vt5dqQUBVQRjehOx.1
URL:https://events.ucsc.edu/event/ece-290-seminar-memristors-for-a-brain-scale-neuromorphic-chip/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260529T140000
DTEND;TZID=America/Los_Angeles:20260529T160000
DTSTAMP:20260627T000852
CREATED:20260512T162505Z
LAST-MODIFIED:20260512T163221Z
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SUMMARY:Zhu\, R. (ECE) - From Neuromorphic Principles to Efficient Neural Language Architectures
DESCRIPTION:This dissertation investigates how neuromorphic and brain-inspired principles can guide the design of efficient neural language architectures. It addresses two central limitations of modern Transformer-based language models: memory growth with context length and high computational cost from dense matrix multiplication. Through studies of spiking neural networks\, linear-recurrent language models\, hybrid attention architectures\, MatMul-free models\, and looped language models\, the dissertation develops practical approaches for bounded-memory and bounded-compute language modeling. The central conclusion is that recurrent state\, temporal decay\, sparse computation\, and parameter reuse can provide useful design principles for scalable language models\, even when they are abstracted beyond literal biological spiking. \nEvent Host: Ridger Zhu\, Ph.D. Candidate\, Electrical & Computer Engineering  \nAdvisor: Jason Eshraghian \nZoom: https://ucsc.zoom.us/j/96672322005?pwd=3MSitgbm5WboIENbf1hKpxwXnt9VXh.1
URL:https://events.ucsc.edu/event/zhu-r-ece-from-neuromorphic-principles-to-efficient-neural-language-architectures/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260528T120000
DTEND;TZID=America/Los_Angeles:20260528T140000
DTSTAMP:20260627T000852
CREATED:20260526T163353Z
LAST-MODIFIED:20260526T163353Z
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SUMMARY:Ortiz Barbosa\, D. (CSE) - HARDENING AUTONOMOUS CYBER-PHYSICAL SYSTEMS AGAINST ADVERSARIAL CONDITIONS
DESCRIPTION:Autonomous systems\, such as Autonomous Vehicles (AVs) and drones\, are increasingly\ndeployed across a wider array of contexts for both civilian and military use. As these\nsystems become more common\, they may be targeted by malicious actors seeking to\nexploit and abuse them\, compromising safety-critical operations. Among the ways to\nprotect these systems simulation based testing frameworks have been developed. How-\never\, existing testing frameworks primarily focus on identifying logical flaws or system\nvulnerabilities\, often emphasizing static scenarios and paying less attention to an adap-\ntive intelligent adversary.\nTo help reduce this gap\, this dissertation develops and applies adaptive\, adversary-\naware methodologies to discover\, formalize\, and mitigate security vulnerabilities in au-\ntonomous systems spanning vehicle platooning\, drone swarms\, and vision-based drone\nrecovery. We first apply NLP techniques to discover and formalize driving rules across\nNorth American and Australian jurisdictions\, identifying possible restriction that an\nadversary can exploit. Likewise\, these rules can be used to test the adaptability of AVs\nto new contexts and to establish a formal basis for context-aware AV testing. Next\,\nwe apply optimization-based adversarial search to both ACC-controlled vehicle pla-\ntoons and obstacle-avoiding drone swarms. We uncover maneuvers that an adversary\ncan use against the system that range from crash-inducing patterns against platooning\ncontrollers to herding strategies that divert swarms from their objectives. Finally\, to\naddress the gap regarding the possible solutions to an adversarial attack we explore how\na drone can recover from it by using LVLMs to understand its context and select a safe\nlanding surface. \nEvent Host: Diego Ortiz Barbosa\, Ph.D. Candidate\, Computer Science & Engineering  \nAdvisor: Alvaro A Cardenas
URL:https://events.ucsc.edu/event/ortiz-barbosa-d-cse-hardening-autonomous-cyber-physical-systems-against-adversarial-conditions/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260528T110000
DTEND;TZID=America/Los_Angeles:20260528T120000
DTSTAMP:20260627T000852
CREATED:20260522T165248Z
LAST-MODIFIED:20260522T165248Z
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SUMMARY:Oh\, S. (CSE) - Efficient Instruction Supply for Datacenter Processors
DESCRIPTION: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 create severe frontend pathologies; and agentic AI systems\, in which large-language-model agents plan\, dispatch tools\, and maintain growing conversational contexts\, causing CPUs to account for up to 88% of end-to-end agent latency. Reflecting this shift\, major CPU vendors have publicly repositioned the CPU as the orchestration layer of the AI stack and have begun shipping processors optimized for agent-centric workloads. \nThis dissertation argues that instruction delivery is the dominant CPU bottleneck across both workload classes and that the recent trend towards agentic AI further exacerbates this challenge. In hyperscale server binaries\, the primary pathologies are wrong-path prefetch pollution and post-recovery instruction-delivery gaps across large\, irregular call graphs. In agentic AI systems\, the bottleneck shifts to an orchestration substrate composed of protocol stacks\, dynamic-runtime dispatch\, and agent-specific extensions that is even more frontend-bound than traditional warehouse-scale workloads. \nTo address these bottlenecks\, this dissertation presents three technical contributions\, together with a companion infrastructure contribution. First\, Utility-Driven Prefetching (UDP) extends fetch-directed instruction prefetching (FDIP) with a learned per-prefetch utility model that admits candidates based on their historical contribution to demand-fetch hits\, including those reached along wrong-path execution. Second\, Junction-based Unified Miss-point Prefetching (JUMP) addresses the post-recovery instruction-delivery gap that UDP and prior FDIP optimizations cannot reach by launching a lightweight secondary FDIP thread at a learned miss point following each branch-prediction failure. Across a suite of datacenter workloads\, UDP improves IPC by 3.6% on average (up to 16.1%) over a state-of-the-art FDIP baseline\, while JUMP improves IPC by 2.0% on average (up to 14.9%). Combined\, the two mechanisms substantially close the gap between FDIP and a perfect L1 instruction cache at a storage cost of only a few tens of kilobytes.\nThird\, this dissertation introduces the Agentic Tax\, the first CPU characterization study of agentic AI workloads across three runtime families. The study is packaged as a deterministic-replay benchmark infrastructure that enables repeatable\, cycle-level evaluation under controlled conditions. The characterization shows that the orchestration substrate of agentic AI workloads is significantly more frontend-bound than the hyperscale datacenter workloads examined in prior work\, and that it introduces new dominant function families with no analog in traditional warehouse-scale systems. These findings motivate two architectural directions proposed as future work: extending UDP and JUMP to optimize the orchestration substrate itself\, and designing heterogeneous CPU cores that allocate frontend resources according to the execution phase. \nEvent Host: Surim Oh\, Ph.D. Candidate\, Computer Science & Engineering  \nAdvisor: Heiner Litz \nZoom: https://ucsc.zoom.us/j/94753352649?pwd=7vQxlnSJkUb0KfG3t6STo639LhRv7j.1 \nPasscode: 205162
URL:https://events.ucsc.edu/event/oh-s-cse-efficient-instruction-supply-for-datacenter-processors/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260527T110000
DTEND;TZID=America/Los_Angeles:20260527T123000
DTSTAMP:20260627T000852
CREATED:20260330T203942Z
LAST-MODIFIED:20260330T203942Z
UID:10011815-1779879600-1779885000@events.ucsc.edu
SUMMARY:CSE Colloquium - Learning to Image: Computational Microscopy for Dynamic Systems
DESCRIPTION:Presenter: Laura Waller\, UC Berkeley \nAbstract: \nComputational imaging jointly designs hardware and algorithms to push beyond the classical limits of imaging\, enabling measurement of new quantities (e.g. 3D\, phase\, and super-resolution) with simple\, inexpensive hardware. These approaches have already transformed consumer photography; our goal is to achieve a similar transformation in scientific microscopy. \nIn this talk\, I will show how end-to-end learning is reshaping the design of imaging systems\, from programmable illumination with LED arrays to compact\, lensless cameras built from Scotch tape. By combining physical models with neural networks\, we can jointly learn how to capture data\, reconstruct images\, and self-calibrate systems that would otherwise be too complex to model. However\, many computational methods rely on multiple measurements\, limiting their use for live\, dynamic samples. I will introduce new space-time algorithms based on implicit neural representations (INRs) that jointly recover structure and motion\, correct artifacts\, and enable high-resolution imaging in regimes where traditional approaches fail. \nBio: \nLaura Waller is the Charles A. Desoer Professor of Electrical Engineering and Computer Sciences at UC Berkeley. She received B.S.\, M.Eng. and Ph.D. degrees from the Massachusetts Institute of Technology in 2004\, 2005 and 2010. After that\, she was a Postdoctoral Researcher and Lecturer of Physics at Princeton University from 2010-2012. She is a Packard Fellow for Science & Engineering\, Moore Foundation Data-driven Investigator\, OSA Fellow\, and Chan-Zuckerberg Biohub Investigator. She has received the Carol D. Soc Distinguished Graduate Mentoring Award\, OSA Adolph Lomb Medal\, the SPIE Early Career Award and the Max Planck-Humboldt Medal. \nHosted by: Professor Alvaro Cardenas \nLocation: Engineering 2\, Room E2-180 (Refreshments such as fruit\, pastries\, coffee\, and tea will be provided.) \nZoom Option: https://ucsc.zoom.us/j/93445911992?pwd=YkJ2TQtF79h0PcNXbEcpZLbpK0coiY.1&jst=3
URL:https://events.ucsc.edu/event/cse-colloquium-learning-to-image-computational-microscopy-for-dynamic-systems/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260526T103000
DTEND;TZID=America/Los_Angeles:20260526T123000
DTSTAMP:20260627T000852
CREATED:20260512T164007Z
LAST-MODIFIED:20260512T164007Z
UID:10014630-1779791400-1779798600@events.ucsc.edu
SUMMARY:Castro\, S. (CSE) - Agentic AI for Security: Adversarial Foundations for Autonomous Cyber Operations
DESCRIPTION:Autonomous Cyber Operations (ACO) agents promise effective security automation with minimal human intervention\, yet their deployment raises three interconnected challenges: agents must be realistic (reproducing diverse attacker sophistication)\, secure (preventing autonomy from becoming an attack surface)\, and feasible (safely replicating human behavior at full autonomy). \nWe argue that these three properties are requirements for ACO agents. Existing approaches do not address them together and lack diverse adversarial coverage\, formal threat models for attacks against the agents themselves\, and systematic evaluation of multi-agent topologies. \nWe advance all three ACO properties: (1) For realism\, we establish adversarial foundations by discovering Windows OS vulnerabilities and releasing two exploits reliable across XP through 11. (2) For security\, we formalize ACO meta-attacks and meta-defenses\, propose the first invariant-based Meta-IDS detecting both sensor and actuator meta-attacks\, and introduce the first hybrid LLM–RL ACO integration for defense with a novel inter-agent communication protocol. (3) For feasibility\, we present MaLO\, the first dynamic-topology multi-agent ACO system\, achieving a 78.6\% success rate across a new 42-task security benchmark and solving operations up to 40× faster than human experts. We further propose the Security Operation Complexity Index (SOCX) classification and the T×V×O taxonomy as the first objective-driven evaluation methodology for coding-agent attacks. \nTogether\, these contributions demonstrate that ACO agents can match real-world adversarial sophistication\, resist meta-attacks\, and outperform human operators on complex security tasks. Open challenges remain in adaptive adversaries\, LLM–RL co-training\, dynamic topology selection\, and deployment beyond simulated environments. \n  \nEvent Host:  Sebastián R. Castro\, PhD Candidate\, Computer Science & Engineering \nAdvisor: Alvaro A. Cárdenas \nZoom: https://ucsc.zoom.us/j/2267557290?pwd=S0dNTTV3emZGUzlqV3dLbTg3a0NFUT09&omn=92791061627 \nPasscode: G20c06
URL:https://events.ucsc.edu/event/castro-s-cse-agentic-ai-for-security-adversarial-foundations-for-autonomous-cyber-operations/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260526T100000
DTEND;TZID=America/Los_Angeles:20260526T110000
DTSTAMP:20260627T000852
CREATED:20260518T185313Z
LAST-MODIFIED:20260518T190031Z
UID:10014653-1779789600-1779793200@events.ucsc.edu
SUMMARY:Harsh\, B. (CSE) - SUPERSCALAR\, MULTIPLE TAKEN BRANCH PREDICTOR
DESCRIPTION:This work addresses improvements in branch prediction mechanism to support high perfor-\nmance processors. The state of the art aims to balance the prediction latency and prediction\naccuracy using multi level correcting predictors [27]. Prior published work focusses on scalar\ndesigns and prediction accuracy improvement for hard to predict branches employing tailor\nmade\, non generic and non transferrable solutions [8]. Recent work also proposes ahead pre-\ndiction [42–44] to solve the problem of low accuracy of L0 predictor. \nThis work proposes efficent\, generic and transferrable solutions to reduce mispredic-\ntions and to use the fetch bandwidth more efficiently. This includes a biased overriding multi-\nlevel hierarchy with three predictor levels (L0\, L1\, L2). L0 uses a High-Confidence-Only Taken\n(HOTP) predictor that only predicts high-confidence taken control-flow instructions. This work\nfurther uses L1-L2 biased training to decrease mispredictions by L2 while it trains on branches\non which L1 has reached high confidence. This work proposes a superscalar predictor built\nusing the state of the art scalar predictor. Superscalar predictor is implemented by sizing a su-\nperscalar TAGE variant (BATAGE) using Optuna-based search. with varying table sizes and\naspect ratios. The work further proposes a branch predictor frontend design (nTakenBP) to de-\nliver multiple taken branch predictions per cycle. Unlike prior work\, nTakenBP achieves this by\nextending the existing BTB and TAGE tag-comparison logic rather than deepening lookahead. \n  \nEvent Host: Bhawandeep Singh Harsh\, Ph.D. Candidate\, Computer Science & Engineering \nAdvisor: Jose Renau \nZoom: https://ucsc.zoom.us/j/4166778865?pwd=cS9NcnVjRjArYlRRcDcrY3d5N0ZKQT09
URL:https://events.ucsc.edu/event/harsh-b-cse-superscalar-multiple-taken-branch-predictor/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260526T100000
DTEND;TZID=America/Los_Angeles:20260526T110000
DTSTAMP:20260627T000853
CREATED:20260514T202927Z
LAST-MODIFIED:20260520T182036Z
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SUMMARY:ECE Seminar: Advanced Sensing and AI Technologies for Food Safety and Precision Agriculture
DESCRIPTION:Presenter: Hamid Jafarbiglu\, Agricultural Technology Evaluator\, Big Idea Ventures \nDescription: California agriculture is increasingly adopting organic and regenerative production systems\, creating a growing need for technologies capable of monitoring complex agricultural environments\, assessing food safety risks\, and supporting data-driven management decisions. Emerging tools such as drones\, hyperspectral scanning\, environmental sensors\, and artificial intelligence provide new opportunities for continues field-scale monitoring\, risk detection\, and precision management while supporting sustainable agricultural practices. This talk highlights several applied research projects focused on the use of drone-based sensing\, spatio-spectral responses\, soil and environmental sensors\, and machine learning approaches to address real- world challenges in specialty crop production. These projects demonstrate how sensing technologies and advanced analytics can improve field-scale monitoring\, continuous risk assessment\, early detection\, and suitability in food production. Building on these experiences\, future research directions will focus on the intersection of food safety\, organic and regenerative agriculture\, and precision agricultural technologies. \nBio: Hamid Jafarbiglu is a researcher specializing in remote sensing\, spectral analysis\, and machine learning for agricultural systems. His work focuses on enhancing food safety\, crop monitoring\, and precision decision-making in high-value specialty crops.\n \nDr. Jafarbiglu earned his Ph.D. in Biological Systems Engineering from the University of California\, Davis\, following six years of intensive field research. His expertise integrates drone-based remote sensing\, hyperspectral imaging\, and AI to identify crop stress\, pest/disease outbreaks\, and nutrient deficiencies at their earliest stages.\n \nDuring his tenure at the UC Davis Digital Agriculture Lab\, Dr. Jafarbiglu’s doctoral and postdoctoral research resolved critical limitations in aerial spectral measurements. This work led to superior accuracy in drone-based sensing under variable field conditions and the development of scalable image-processing pipelines and digital orchard models for deep learning applications.\nBeyond research\, Dr. Jafarbiglu is an experienced extension professional. He has delivered hands-on training in drone operations and geospatial analysis to growers\, researchers\, and industry stakeholders\, bridging the gap between data-driven innovation and real-world adoption.\n \nHis background also extends to the commercial sector; as an Agricultural Technology Evaluator with Big Idea Ventures\, he conducted technical and market assessments for agri-food innovations\, including early-stage bio-based products. Today\, Dr. Jafarbiglu’s work continues to advance the integration of AI and remote sensing to foster sustainable\, regenerative farming and robust food systems across California and beyond. \nHosted by: Professor Marco Rolandi\, ECE Department \nZoom Link: https://ucsc.zoom.us/j/96727838511?pwd=1Qzl9HTV3G2BxaSEG8GeKOPZVu2NWj.1
URL:https://events.ucsc.edu/event/ece-seminar-hamid-jafarbiglu/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Seminars
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260520T110000
DTEND;TZID=America/Los_Angeles:20260520T121500
DTSTAMP:20260627T000853
CREATED:20260518T155149Z
LAST-MODIFIED:20260518T155149Z
UID:10014650-1779274800-1779279300@events.ucsc.edu
SUMMARY:CSE Colloquium - Safety Alignment of LMs via Non-cooperative Games
DESCRIPTION:Presenter: Arman Zharmagambetov\, Meta \nAbstract:\nEnsuring the safety of language models (LMs) while maintaining their usefulness remains a critical challenge in AI alignment. Current approaches rely on sequential adversarial training: generating adversarial (harmful) prompts and fine-tuning LMs to defend against them. We introduce a different paradigm: framing safety alignment as a non-zero-sum game between an Attacker LM and a Defender LM trained jointly via online reinforcement learning. Each LM continuously adapts to the other’s evolving strategies\, driving iterative improvement. Our method uses a preference-based reward signal derived from pairwise comparisons instead of point-wise scores\, providing more robust supervision and potentially reducing reward hacking. Our RL recipe\, AdvGame\, shifts the Pareto frontier of safety and utility\, yielding a Defender LM that is simultaneously more helpful and more resilient to adversarial attacks. In addition\, the resulting Attacker LM converges into a strong\, general-purpose red-teaming agent that can be directly deployed to probe arbitrary target models. \nBio:\nArman Zharmagambetov is a research scientist in the Fundamental AI Research (FAIR) team at Meta. His research primarily focuses on machine learning and optimization\, recently exploring their application in enhancing the security and robustness of AI systems. He received his PhD from the University of California – Merced. Afterward\, he completed his postdoctoral research at FAIR\, focusing on Reinforcement Learning\, AI-guided design and Optimization. \nHosted by: Professor Alvaro Cardenas and Professor Sungjin Im \nDate and Time: Wednesday\, May 20\, 2026 from 11:00 am – 12:15 pm \nLocation: Engineering 2\, Room E2-180 (Refreshments such as fruit\, pastries\, coffee\, and tea will be provided.) \nZoom Option: https://ucsc.zoom.us/j/93445911992?pwd=YkJ2TQtF79h0PcNXbEcpZLbpK0coiY.1&jst=3
URL:https://events.ucsc.edu/event/cse-colloquium-safety-alignment-of-lms-via-non-cooperative-games/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260520T090000
DTEND;TZID=America/Los_Angeles:20260520T110000
DTSTAMP:20260627T000853
CREATED:20260507T160500Z
LAST-MODIFIED:20260507T160500Z
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SUMMARY:Lucas\, J. (BMEB) - Enabling Population-Scale Analysis of Human Centromere Diversity
DESCRIPTION:Centromeric DNA is critical for accurate chromosome segregation and genome stability\, but due to its repetitive nature\, it was only recently fully included in a human reference. Rapid evolution and sequence diversity in these regions limit the utility of one reference sequence\, however. Integrating centromeric and pericentromeric satellite DNA – which together constitute over 5% of the human genome – into genetic research requires access to diverse sequences and the variation between them. The HPRC’s Release 2 dataset\, together with recent advancements in long-read assembly algorithms and new tools for sequence alignment and annotation\, now make characterization of centromeric variation possible. In this proposal\, I outline my work as part of the Human Pangenome Reference Consortium (HPRC) to create a diverse set of reference assemblies that accurately represent centromeric variation (aim 1)\, use novel tooling to characterize variation in centromeric regions (aim 2)\, and define the mutational processes that drive centromere evolution (aim 3). Completion of these aims will create a resource to enable the analysis and interpretation of centromeric variation data\, bringing these historically inaccessible regions into mainstream studies of human genetics\, evolution\, and disease. \nEvent Host: Julian Lucas\, Ph.D. Student\, Biomolecular Engineering & Bioinformatics \nAdvisor: Karen Miga \nZoom: https://ucsc.zoom.us/j/94129246296?pwd=QAs2hW8QZRNgpfaGJXvmaVfo52tIh7.1 \nPasscode: 669318
URL:https://events.ucsc.edu/event/lucas-j-bmeb-enabling-population-scale-analysis-of-human-centromere-diversity/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260519T100000
DTEND;TZID=America/Los_Angeles:20260519T120000
DTSTAMP:20260627T000853
CREATED:20260512T163057Z
LAST-MODIFIED:20260512T163057Z
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SUMMARY:Paul Pena\, D. (CSE) - Efficient Pattern Counting in Sparse Graphs and Hypergraphs
DESCRIPTION:Pattern counting is a fundamental problem in computer science with applications in many domains. For a fixed small pattern H\, we are given a large graph G and we are asked to count the number of subgraphs or homomorphisms (edge-preserving maps) of H in G. For practical applications where the input graph can be very large\, we are interested in finding efficient algorithms\, that is\, algorithms that run in linear or subquadratic time with respect to the size of the input. \nFinding such algorithms in general (when G can be any graph) is not possible. Instead\, we restrict our input to sparse classes of graphs. One family of graph classes that has been widely studied in the context of subgraph and homomorphism counting is bounded-degeneracy graph classes. Real-world graphs in many domains have bounded degeneracy\, so studying these classes in theory can lead to practical algorithms. \nA series of advances in the study of homomorphism counting led to a dichotomy theorem that exactly characterized which patterns were linear-time computable for bounded-degeneracy inputs. This dissertation builds on this result\, extending it to other variants of this problem\, and generalizing it to other different settings\, like counting hypergraphs and notions of sparsity beyond degeneracy. \nOur results help develop the theory of subgraph counting in sparse graphs and hypergraphs\, and showcase how sparsity can be used both in theory and practice to develop faster algorithms. \n  \nEvent Host: Daniel Paul Pena\, Ph.D. Candidate\, Computer Science & Engineering  \nAdvisor: C. Sheshadhri \nZoom: https://ucsc.zoom.us/j/97685906168?pwd=O35brsWilyn2m8AgMn0dKgALBe6wi1.1
URL:https://events.ucsc.edu/event/paul-pena-d-cse-efficient-pattern-counting-in-sparse-graphs-and-hypergraphs/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260519T100000
DTEND;TZID=America/Los_Angeles:20260519T110000
DTSTAMP:20260627T000853
CREATED:20260514T195842Z
LAST-MODIFIED:20260514T225808Z
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SUMMARY:ECE Seminar: Multiscale Sensing for Specialty Crop Systems: From Field Monitoring to Food Safety Application
DESCRIPTION:Presenter: Eve Laroche-Pinel\, Postdoctoral Researcher\, California State University\, Fresno \nDescription: Advances in remote sensing\, drone platforms\, and data analytics are enhancing the ability to monitor agricultural systems at fine spatial and temporal scales. This presentation will highlight applied research using multispectral and hyperspectral data from satellites\, drones\, aircraft\, and ground platforms to assess crop water status\, detect disease\, and estimate fruit composition. These efforts are developed in collaboration with growers and industry partners\, with an emphasis on methods that are robust under field conditions and scalable across production systems. Building on this foundation\, the talk will examine how similar sensing approaches could be extended to address food safety challenges in California agriculture\, particularly in systems transitioning toward organic and regenerative practices . By linking environmental variability\, water dynamics\, and landscape features with potential contamination pathways\, sensing technologies may support improved risk assessment and monitoring. \nBio: Eve Laroche-Pinel is a researcher specializing in the application of sensing technologies to agricultural systems\, with a focus on translating data-driven methods into tools that support decision-making in real production environments. Her work sits at the intersection of agricultural engineering\, remote sensing\, and applied machine learning.  \nShe holds a PhD from the National Polytechnic Institute of Toulouse (France)\, completed in partnership with industry\, where she developed an operational service to monitor vineyard water status using satellite imagery. This work fostered a strong emphasis on applied research\, system integration\, and technology transfer to end users.  \nShe is currently a postdoctoral researcher at California State University\, Fresno\, contributing to a research program that uses multispectral and hyperspectral data collected from satellites\, drones\, aircraft\, and ground-based platforms. Her work addresses plant water status\, disease detection\, and crop composition\, combining field measurements\, laboratory analyses\, and predictive modeling. These projects are conducted in collaboration with growers\, industry partners\, and multidisciplinary academic teams\, with the objective of producing methods that are robust under field conditions and scalable across production systems.  \nShe plans to increasingly focus on how sensing technologies could contribute to food safety challenges in specialty crops. By linking environmental variability\, crop condition\, and landscape features with potential contamination pathways\, her future work would aim to support improved risk assessment and monitoring strategies\, particularly in systems transitioning toward organic and regenerative practices.  \nExtension and stakeholder engagement are central to her approach. She works closely with growers and partners to co-develop field trials\, adapt methodologies to operational constraints\, and translate technical outputs into actionable guidance. Her work includes participation in workshops\, training activities\, and collaborative projects that connect research with practice.  \nHer long-term goal is to build integrated research and extension programs that combine drones\, spectral sensing\, and environmental monitoring to support safe\, resilient\, and technology-enabled agriculture. \nHosted by: Professor Marco Rolandi\, ECE Department \nZoom Link: https://ucsc.zoom.us/j/96727838511?pwd=1Qzl9HTV3G2BxaSEG8GeKOPZVu2NWj.1
URL:https://events.ucsc.edu/event/ece-seminar-researcher-in-agricultural-sensing-remote-sensing-and-applied-ai/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Seminars
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260518T104000
DTEND;TZID=America/Los_Angeles:20260518T114500
DTSTAMP:20260627T000853
CREATED:20260514T225630Z
LAST-MODIFIED:20260514T225630Z
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SUMMARY:ECE 290 Seminar: AI for Enhancing Power Grid Resilience Against Extreme Weather Events
DESCRIPTION:Presenter: Masood Parvania\, Roger P. Webb Endowed Professor\, University of Utah \n  \nDescription: Many communities across the world are experiencing more frequent and severe extreme weather disturbances such as wildfires\, heatwaves\, drought\, storms\, rising sea levels\, and flooding\, which not only pose threats to human health\, and the environment but also affect the ability of the power grid to continue powering the communities. This requires upgrading the operation of power grid from passive and manual applications to making complex decisions in real-time to facilitate the automated recovery of the system after major disturbances. This talk will review the application of various AI and ML techniques for detection\, response and mitigation of cyber anomalies and extreme weather events in power distribution systems.\n \n  \nBio: Masood Parvania is the Roger P. Webb Endowed Professor of Electrical and Computer Engineering and the Director of Utah Smart Energy Laboratory (U-Smart) at the University of Utah. Dr. Parvania is the Principal Investigator and Director of the U.S.-Canada Center on Climate-Resilient Western Interconnected Grid (NSF WIRED Global Center)\, co-funded by U.S. National Science Foundation (NSF) and Natural Sciences and Engineering Research Council of Canada (NSERC). He is also the Founder and President of the Energy-AI company\, Grid Elevated\, which specializes on developing and commercializing AI technology for resilient and efficient power grid operation. \n  \nHosted by: Professor Soumya Bose\, ECE Department \nZoom Link: https://ucsc.zoom.us/j/97975378707?pwd=ljcgaCfhMmhZ88Vt5dqQUBVQRjehOx.1
URL:https://events.ucsc.edu/event/ece-290-seminar-ai-for-enhancing-power-grid-resilience-against-extreme-weather-events/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260518T100000
DTEND;TZID=America/Los_Angeles:20260518T110000
DTSTAMP:20260627T000853
CREATED:20260514T195027Z
LAST-MODIFIED:20260514T203152Z
UID:10014638-1779098400-1779102000@events.ucsc.edu
SUMMARY:ECE Seminar: From Plumes to Produce: Leveraging Atmospheric Modeling and Smart Sensing for Food Safety
DESCRIPTION:Presenter: Derek Hollenbeck\, postdoctoral research scholar\, University of California\, Merced  \nDescription: Advances in drone-based environmental sensing\, atmospheric modeling\, and intelligent monitoring systems are creating new opportunities for addressing emerging challenges in food safety and agricultural resilience. This talk explores how methodologies originally developed for methane emission detection and quantification could be translated toward agricultural and food safety applications. The presentation begins with an overview of research experiences in autonomous sensing and environmental monitoring\, including work associated with the inaugural CITRIS Aviation Prize before outlining some key potential areas for food safety with drones. Then\, the talk overviews previous research on the topics related to drone-based environmental monitoring\, Digital Twins\, and Smart Sensing – with a focus on methane emission source quantification\, atmospheric transport modeling of a point source\, and inverse problem methodologies for real-time parameter estimation. Finally\, the talk examines how these concepts may be adapted to food safety research questions\, as well as highlight opportunities for interdisciplinary collaboration alongside emerging priorities from organizations and certification frameworks. \nBio: Derek Hollenbeck is a postdoctoral research scholar at the University of California\, Merced (UCM)\, where he serves as the manager of the Center for Methane Emissions Research and Innovation (CMERI) under the supervision of Dr. YangQuan Chen. He earned his B.Sc. (2016) and Ph.D. (2023) in Mechanical Engineering from UCM\, where he conducted research in the Mechatronics Embedded Systems and Automation (MESA) Lab.\n \nHis work sits at the intersection of fluid mechanics\, controls\, dynamics\, and inverse problems\, with a focus on developing intelligent environmental monitoring systems using small unmanned aerial systems (sUAS). His research integrates machine learning and physics-based modeling to detect\, localize\, and quantify methane emissions in complex environments.\n \nDr. Hollenbeck is the author of Smart Sensing with Digital Twins: Methane Emission Source Determination with sUAS\, which presents a framework for combining digital twins\, inverse modeling\, and autonomous sensing to improve environmental observability. His work emphasizes how data-driven and physics-informed approaches can be fused to optimize sensor placement\, enhance estimation accuracy\, and enable real-time decision-making in single/distributed mobile sensing systems. \nHosted by: Professor Marco Rolandi\, ECE Department \nZoom Link: https://ucsc.zoom.us/j/96727838511?pwd=1Qzl9HTV3G2BxaSEG8GeKOPZVu2NWj.1
URL:https://events.ucsc.edu/event/ece-seminar-from-plumes-to-produce-leveraging-atmospheric-modeling-and-smart-sensing-for-food-safety/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Seminars
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260514T090000
DTEND;TZID=America/Los_Angeles:20260514T110000
DTSTAMP:20260627T000853
CREATED:20260427T162713Z
LAST-MODIFIED:20260427T162920Z
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SUMMARY:Shadmon\, R. (CS) - Proximal Byzantine Agreement
DESCRIPTION:Research on fault-tolerance protocols for approximate Byzantine agreement\n(ABA) has largely focused on ensuring that distributed processes remain\nconsistent despite fewer than 1/3 faulty processes. Yet in many\nreal systems\, consistency is only useful when it enables processes to\nmake accurate decisions from replicated\, noisy\, and potentially\nadversarially corrupted data relative to an ideal fault-free baseline.\nThis limitation is increasingly important in edge applications such as\nautonomous vehicles\, drone networks\, smart cities\, manufacturing\, and\nsensor-based systems\, where agreement directly drives downstream\nactions. At the same time\, many existing ABA protocols impose\nimpractical requirements\, such as replica counts that grow with data\ndimensionality or prior knowledge of the maximum distance between values\nproposed by each process. \nWe introduce Stochastic Byzantine Agreement (SBA)\, a new problem\nformulation in which the goal is to estimate an output from n replicated\nvalues consisting of n-f nonfaulty outputs generated by an\nunderlying stochastic process and f arbitrarily chosen\nByzantine outputs. We then present Proximal Byzantine Agreement\n(PBA)\, a stochastic agreement protocol that solves SBA by enabling\nconsumers to infer the most likely ideal output conditioned on the\noutputs they receive. In addition\, PBA provides a region\nguarantee that\, as we prove\, always contains the corresponding\nfault-free stochastic estimate of the true value. \nWe describe the design of PBA\, formalize its guarantees\, and evaluate\nits accuracy against existing techniques using stochastic simulations\nacross symmetric and asymmetric distributions and multiple system\nconfigurations. We also evaluate runtime overhead and performance in a\nfollow-the-leader drone network simulator and in a Java implementation on\nRaspberry Pis using a real-world adaptive cruise control dataset. Our\nresults show that PBA performs competitively across all evaluated\nsettings and especially well under simulated Byzantine attack. Most\nnotably\, PBA maintains stable accuracy as dimensionality increases\,\noutperforming methods that require up to 10x more replicas}\nand incur up to 10x greater computation time per agreement\ndecision. \nEvent Host: Roy Shadmon\, Ph.D. Candidate\, Computer Science  \nAdvisor: Owen Arden \nZoom: https://ucsc.zoom.us/j/98390167664?pwd=DwkNuUSRaZRKXYb7pQbDYXgf7HFFPg.1 \nPasscode: pba
URL:https://events.ucsc.edu/event/shadmon-r-cs-proximal-byzantine-agreement/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260513T110000
DTEND;TZID=America/Los_Angeles:20260513T121500
DTSTAMP:20260627T000853
CREATED:20260330T203158Z
LAST-MODIFIED:20260330T203417Z
UID:10011814-1778670000-1778674500@events.ucsc.edu
SUMMARY:CSE Colloquium - The EU’s Cybersecurity Framework: what it is\, what it means
DESCRIPTION:Presenter: Chris Jay Hoofnagle\, Frederik Zuiderveen Borgesius\, Lothar Determann\, Pieter T.J. Wolters \nAbstract: \nThe European Union has enacted a comprehensive cybersecurity framework (the “Framework”) that imposes far-reaching obligations on developers of standalone software and connected products. This Article describes the European legislative approach before turning to a description of the Framework. Anchored by the Cyber Resilience Act and the Cybersecurity Act\, and reinforced by a constellation of sector-specific measures\, the Framework effectively creates a California-like-products-liability regime for software. It mandates extensive security-by-design obligations\, imposes stringent conformity assessment and incident-reporting duties\, and shifts substantial compliance burdens onto manufacturers\, importers\, and distributors. It even treats emotional wrongs caused by software as injurious. The Framework will take full effect in December 2027\, meaning that companies must integrate its requirements into their current product cycles. \nBio: Chris Hoofnagle is professor of law in residence at the University of California\, Berkeley\, where he teaches tort law and cybersecurity. \nHosted by: Professor Alvaro Cardenas \nLocation: Engineering 2\, Room E2-180 (Refreshments such as fruit\, pastries\, coffee\, and tea will be provided.) \nZoom Option: https://ucsc.zoom.us/j/93445911992?pwd=YkJ2TQtF79h0PcNXbEcpZLbpK0coiY.1&jst=3
URL:https://events.ucsc.edu/event/cse-colloquium-the-eus-cybersecurity-framework-what-it-is-what-it-means/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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DTSTART;TZID=America/Los_Angeles:20260512T100000
DTEND;TZID=America/Los_Angeles:20260512T120000
DTSTAMP:20260627T000853
CREATED:20260421T160759Z
LAST-MODIFIED:20260421T160759Z
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SUMMARY:Chen\, Q. (CSE) - New Approximation and Online Algorithms using Novel Combinatorial Structures
DESCRIPTION: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\, computer scientists turn to find an approximate solution and create numerous elegant algorithms. However\, in the modern era\, computational environments have changed drastically\, and we are not able to afford to design new algorithms for each new problem via repeated trial and error. Therefore\, systematic ways to understand the possibilities and limitations of these problems are desired. This dissertation studies several central combinatorial optimization problems\, focusing on understanding the key structural obstacles and developing unified frameworks. Mainly\, we study two types of combinatorial optimization problems:\n(1) Scheduling. The problem is associated with limited resources\, and our target is to find an allocation method to complete all jobs over time that minimizes the overall budget cost.\n(2) Network Design. Different from scheduling problems. In this problem\, we aim to find a minimum-cost topological network that supports routing for demanding communications. \nOur first work is focused on a group-to-group survivable network design problem that generalizes the classic point-to-point network to support routing between any pair of subsets of nodes. Previous research stops at limited faults\, and the difficulty comes from the way to compress the graph into a tree. We propose a new framework via capacitated tree embeddings against arbitrary faults in the network\, which gives the first polylogarithmic approximation algorithm. Further\, this framework captures nearly all the recent models proposed in the area. \nIn contrast to the offline optimization problems mentioned above\, online algorithms are natural adaptations that have been found in tremendous real applications. In online algorithms\, the algorithm wants to compete against arbitrary uncertainty\, which means the instance is unknown at first and revealed over time. We study various scheduling problems and focus on some important metrics – average flow time\, which measures the average time a job stays in the system from its arrival to completion. Real-world demands give online scheduling problems enormously different settings. Computer scientists need to repeat errors and trials to find a provably good solution. We find the key required combinatorial property is supermodularity for the residual objective\, which measures the average completion time for all alive jobs assuming they have the same arrival time. Further\, we relate supermodularity with gross-substitute/linear-substitute (GS/LS)\, which is a well-studied definition in economics. Finally\, we propose a meta-algorithm that solves all captured problems in one shot. In the end\, we revisit the proportional fairness (PF) algorithm for $L_p$-norms of flow time. By reinterpreting the previous potential function and the corresponding Fisher market\, we show that PF is competitive. \n  \nEvent Host: Qingyun Chen\, Ph.D. Candidate\, Computer Science & Engineering  \nAdvisor: Sungjin Im \nZoom: https://ucsc.zoom.us/j/92628493495?pwd=iJq8YwarrYyofPLF4AmZpwzsZnLyvt.1 \n 
URL:https://events.ucsc.edu/event/chen-q-cse-new-approximation-and-online-algorithms-using-novel-combinatorial-structures-2/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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