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DTSTART;TZID=America/Los_Angeles:20260821T110000
DTEND;TZID=America/Los_Angeles:20260821T120000
DTSTAMP:20260820T171344Z
CREATED:20260820T171314Z
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SUMMARY:Nava\, A. (AM) - Machine-Learning Methods for Prediction of Biological Systems
DESCRIPTION:Advances in microscopy have enabled the collection of high-quality single-cell datasets\, providing new opportunities to identify the mechanisms underlying complex biological processes. In this work\, we develop machine-learning frameworks using single-cell temporal data with the goal of predicting and providing insights into these mechanisms. We produce frameworks for two biological systems\, bacterial spore germination\, the process in which bacteria begin metabolic activity\, and embryonic stem cell organization. Bacterial spore germination is a critical transition in which the spore becomes susceptible to control techniques\, however the mechanisms governing this transition are unknown. We develop a machine-learning framework that predicts germination timing at the single-spore level\, enabling identification of predictive features associated with germination that may reflect underlying biological mechanisms. Embryonic stem cell organization has been shown to closely recapitulate formations seen in embryonic development\, but the mechanisms driving their spatial organization remain unclear. Here\, we develop a simple agent-based model in which spatial organization is driven by cell-cell interaction parameters. We then train a machine-learning framework to infer these underlying interaction parameters from simulated data and propose that this approach can be extended to other agent-based models calibrated to experimental stem cell data. These studies demonstrate that machine-learning models can be used for prediction using single-cell temporal data\, as well as tools to develop mechanistic hypotheses. \n  \nEvent Host: Alexandra Nava\, Ph.D. Student\, Applied Mathematics  \nAdvisor: Marcella Gomez \nZoom: https://ucsc.zoom.us/j/98821445104?pwd=OFAKwGrObh02bPLgXieXsDcTxxS1Cj.1 \nPasscode: 392769
URL:https://events.ucsc.edu/event/nava-a-am-machine-learning-methods-for-prediction-of-biological-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:20260818T140000
DTEND;TZID=America/Los_Angeles:20260818T160000
DTSTAMP:20260817T160201Z
CREATED:20260817T155423Z
LAST-MODIFIED:20260817T160201Z
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SUMMARY:Lupin-Jimenez\, L. (AM) - Data-Driven Deep Learning for Turbulent Phenomena: Regional Ocean Prediction and Assimilation\, Spectral Bias in Diffusion Models\, and Equation Discovery
DESCRIPTION:Deep learning models trained on simulation and reanalysis data can now emulate turbulent geophysical flows at a small fraction of the computational cost of numerical solvers.\nTheir scientific utility depends on physical consistency\, which for the systems studied here\nrests in large part on spectral fidelity\, the accurate reconstruction of variance across spatial\nscales. This document presents two published studies and two studies in progress that develop\, analyze\, and apply data-driven methods for turbulent phenomena along that thread.\nThe first study develops FCDS\, a framework that autoregressively emulates surface ocean\ndynamics over the Gulf of Mexico at 8 km resolution and simultaneously downscales and\nbias-corrects the emulated fields to 4 km\, with a spectral loss that keeps decadal integrations stable and statistically consistent with a high-resolution reanalysis. The second study\ndevelops a neural-operator-conditioned denoising diffusion model that reconstructs regional\nsurface ocean states from Lagrangian-like observations at 99% and 99.9% sparsity without a\nbackground dynamical model\, and shows that the recovered small-scale dynamics are visible\nin spectral diagnostics but not in pointwise metrics. The third study derives a signal-tonoise theory of spectral bias in diffusion models for 2D turbulence\, organized around the\ncrossover wavenumber kc(τ) at which signal and noise contribute equal power\, and validates\nits predictions on a sweep of 28 models spanning seven forcing wavenumbers and four noise\nschedulers. The fourth study develops a window-pair spectral method for discovering governing equations from single-point sensor measurements of soliton dynamics in a superfluid\nwave flume\, replacing noise-amplifying instantaneous derivatives with finite-time spectral\nshifts and verifying the discovered equations against a measured-scalar null model. A concluding chapter summarizes the results and outlines future work on novel architectures and\nmethods for data-driven emulation of physical simulations. \nEvent Host: Leonard Lupin-Jimenez\, Ph.D. Student\, Applied Mathematics  \nAdvisor: Ashesh Chattopadhyay \nZoom: https://ucsc.zoom.us/j/97866640488?pwd=UJdTs3sxKfFbz5mabKLIyx5ZYF90J9.1 \nPasscode: 815911
URL:https://events.ucsc.edu/event/lupin-jimenez-l-am-data-driven-deep-learning-for-turbulent-phenomena-regional-ocean-prediction-and-assimilation-spectral-bias-in-diffusion-models-and-equation-discovery/
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:20260817T100000
DTEND;TZID=America/Los_Angeles:20260817T120000
DTSTAMP:20260810T162940Z
CREATED:20260810T162940Z
LAST-MODIFIED:20260810T162940Z
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SUMMARY:Nikolakakis\, M. (ECE) - Learned Gridless Representations of Cone Beam Computed Tomography Scans
DESCRIPTION:Medical image representation has long been dominated by voxel-grid matrices. While\ntheir inherent structure and order work efficiently for various linear transformations and\nprovide a seamless visualization method on monitors\, they fail to preserve the topology\nof the scan and to encode sparse information in a memory-efficient way.   The recent emergence of machine learning-based continuous coordinate-based\nscene representations such as neural radiance fields and Gaussian splatting has provided alternative representation techniques. These approaches overfit the weights of\na model by iterative differentiable rendering and have been shown to be more compact than grid representations. They are then able to perform novel view\nsynthesis from any given camera pose.\nOff-grid representations translate directly to Cone Beam Computed Tomography\nsparse-view acquisitions\, where streaking and quantum noise artifacts are dominant.\nUsing differentiable rendering\, a continuous representation is achieved\, with interpolation providing a path to recover some of the lost signal.\nIn this dissertation\, we apply a variety of methodologies\, including Gaussian splatting\, implicit occupancy fields\, and Neural Attenuation Fields regularized with an\nanatomic prior\, to Cone Beam Computed Tomography reconstruction\, and evaluate\ntheir performance across a range of anatomic datasets. Our models show that learned\ngridless representations achieve substantial memory reduction\, recover signal under\nextreme view sparsity\, and preserve scene topology. \nEvent Host: Manolis Nikolakakis\, Ph.D. Candidate\, Electrical and Computer Engineering  \nAdvisor: Razvan Marinescu \nZoom: https://ucsc.zoom.us/j/5964517596?pwd=c1AwRlJLNk5pVzFBUENibEw3by85Zz09
URL:https://events.ucsc.edu/event/nikolakakis-m-ece-learned-gridless-representations-of-cone-beam-computed-tomography-scans/
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:20260814T083000
DTEND;TZID=America/Los_Angeles:20260814T103000
DTSTAMP:20260811T162729Z
CREATED:20260811T162729Z
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SUMMARY:Krishnaswamy\, L. (CSE) - Network Load Balancing for Geographically Distributed Datacenters
DESCRIPTION:As datacenters scale up and become more geographically distributed\, wide-area network inter-datacenter traffic\, which typically consists of data-heavy tasks\, has become increasingly prevalent. Some of the noteworthy challenges raised by the coexistence and interaction between inter- and intra-datacenter traffic are the differences in their QoS requirements\, link utilization\, and round-trip times. To the best of our knowledge\, these challenges have not yet been addressed by current datacenter load balancers. To highlight this gap\, we conducted a comparative performance study of state-of-the-art datacenter load balancers. Through extensive simulations\, we study how they perform under different network topologies and workloads\, including intra-datacenter\, inter-datacenter\, and mixed intra- and inter-datacenter workloads that reflect how datacenters have evolved to keep up with their continuously changing driving application landscape. Our study shows that current load balancers are not able to adequately distribute load under inter-DC workloads as well as mixed intra- and inter-datacenter traffic coexistence.\nMotivated by our observations\, we introduce Balancia\, a transport agnostic\, lightweight network load balancer that dynamically switches between per-flow and per-packet control in order to provide adequate performance for both intra- and inter-DC traffic given their different characteristics and quality-of-service (QoS) requirements. We show that\, when compared against state-of-the-art load balancers\, Balancia achieves close to 80% reduction in the 99% tail flow completion times for inter-datacenter traffic in the presence of intra- and inter-datacenter workload coexistence. Further in this work\, we explore proactively monitoring for congestion with the help of phantom queues and rerouting flows in a timely manner. Through Balancia2.0 we decouple congestion control and load balancing signaling\, and examine its effects on DC and WAN traffic. \nEvent Host: Lakshmi Krishnaswamy\, Ph.D. Candidate\, Computer Science & Engineering \nAdvisor: Katia Obraczka \nZoom: https://ucsc.zoom.us/j/94414934371?pwd=7P3Umt0QQ930ESV02jMvCVHVbkIp9r.1 \nPasscode: 905786
URL:https://events.ucsc.edu/event/krishnaswamy-l-cse-network-load-balancing-for-geographically-distributed-datacenters/
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:20260724T140000
DTEND;TZID=America/Los_Angeles:20260724T170000
DTSTAMP:20260720T162132Z
CREATED:20260720T162049Z
LAST-MODIFIED:20260720T162132Z
UID:10015109-1784901600-1784912400@events.ucsc.edu
SUMMARY:Fontana\, J. (STAT) - When We're Always Wrong: Scalable Variable Selection in M-Open Settings
DESCRIPTION:A ubiquitous task in statistical practice is that of variable selection\, identifying which of a large set of features are the relevant ones. As data sets with a large number of observations have become increasingly common\, new theoretical and computational challenges for model selection have emerged. We consider the variable selection problem for linear models in the M-open setting\, where the data generating process is outside the model space. We focus on the novel problem of “model superinduction”\, which refers to the tendency of model selection procedures to select larger models at an exponential rate as the sample size grows\, resulting in overparametrized models which collapse model interpretability and induce severe computational difficulties. For a set of popular frequentist information criteria and the Bayesian case of mixtures of g-priors\, we prove that when comparing nested models\, the larger model will always be asymptotically selected. We seek to minimize this effect for large n while preserving variable selection consistency.We propose utilizing a mixture of g-priors\, where the hyper-prior on g has hyper-parameters chosen to result in a slowly diminishing rate of prior influence on the posterior\, which favors simpler models while preserving consistency. We also propose a model space prior which induces stronger model complexity penalization for large sample sizes. The posterior model probabilities under our prior choices further provide an alternative information criterion that is resistant to the effects of model superinduction. \nNext\, we extend our results to other classes of popular variable selection priors\, the family of spike and slab priors\, the non-local priors\, and selection procedures that correspond to posterior modes such as the LASSO. We show that these procedures are all afflicted with model superinduction\, except for the continuous spike and slab priors when a Student-t distribution is used for both the spike and the slab. \nFinally\, we address existing bottlenecks in the computation efficacy of spike and slab based variable selection. We demonstrate that Gibbs samplers scale poorly to large sample sizes\, and the proposed alternatives in the literature result in posterior surrogates that are afflicted with superinduction. Instead\, we propose a search strategy based off easy to compute approximations of the posterior model probabilities. We show this procedure\, Fast Approximate Stochastic Search (FASS)\, coupled with post-hoc inference on parameters via either a Block-Variational Bayes approach or an Expectation Propagation approach\, results in competitive performance. We demonstrate the aforementioned phenomena\, and the efficacy of our proposed solutions\, via synthetic data examples and case studies using albedo data from GOES satellites and LCA application data. \nEvent Host: Jacob Fontana\, Ph.D. Candidate\, Statistical Science \nAdvisor: Bruno Sansó \nZoom: https://ucsc.zoom.us/j/99965109575?pwd=uGkOwWM3Rl3zP66aL1RecoOfB8Yat0.1 \nPasscode: 879019
URL:https://events.ucsc.edu/event/fontana-j-stat-when-were-always-wrong-scalable-variable-selection-in-m-open-settings/
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:20260724T140000
DTEND;TZID=America/Los_Angeles:20260724T160000
DTSTAMP:20260716T222234Z
CREATED:20260716T222234Z
LAST-MODIFIED:20260716T222234Z
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SUMMARY:Gholami\, K. (ECE) - Efficient Language Model Construction and Inference via Sparsity
DESCRIPTION:While large language models can match or exceed human performance\, they do so with memory and energy costs orders of magnitude greater than biological cognition. We investigate sparsity as a brain-inspired computational principle to address both. We first establish a framework for evaluating small language model construction methods\, using the next-token logit distribution as a behavioral fingerprint. Then\, we introduce a semi-structured correlation-aware weight sparsity (CWS) method that uses the full activation covariance to identify and prune correlated weights whose combined removal cost is lower than any individual score predicts. CWS\, improves perplexity over existing criteria up to 70% sparsity. To extend this gain to extreme sparsity\, we propose a hierarchical ADMM framework that optimizes pruning directly against cross-entropy and distillation loss\, first layer-wise for efficiency and then globally for cross-layer coordination. This research establishes brain-inspired principles as a foundation for efficient language models that remain accurate even under extreme compression. \nEvent Host: Kimia Gholami\, Ph.D. Student\, Electrical & Computer Engineering \nAdvisor: Jason Eshraghian \nZoom: https://ucsc.zoom.us/j/9827512398?pwd=SGpDWGtVVG81dkgyTHhjbG81dEVUZz09&omn=98349793611 \nPasscode: 8398
URL:https://events.ucsc.edu/event/gholami-k-ece-efficient-language-model-construction-and-inference-via-sparsity/
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:20260713T160000
DTEND;TZID=America/Los_Angeles:20260713T170000
DTSTAMP:20260708T155209Z
CREATED:20260708T155209Z
LAST-MODIFIED:20260708T155209Z
UID:10015011-1783958400-1783962000@events.ucsc.edu
SUMMARY:Kembay\, A. (ECE) - Sparse and Continual Foundations for Adaptive General Intelligence
DESCRIPTION:While the human brain learns continually\, mastering new tasks without forgetting\nthe old and adapting to unfamiliar ones from context alone\, modern neural networks\nstill lack both. To bridge the gap between biological adaptivity and modern AI\, we\nhave established foundational work on sparsity as a computational principle at three\nlevels of neural computation\, through salient feature masking that distills only the most\ninformative knowledge from a teacher\, quantized spiking neural networks whose sparse\nactivations mitigate catastrophic forgetting by updating weights only when new learn-\ning requires it\, and complex-pole value-path dynamics that give Transformer attention\na resonant\, positionally selective memory. Addressing the remaining bottleneck\, that\nthese sparse structures are fixed in advance rather than adapted to the task at hand\,\nwe propose a research roadmap centered on in-context meta-learning with sparse atten-\ntion priors\, enabling models to ‘learn to be sparse’ by inferring task-relevant structure\nfrom context alone\, without any weight update. Taken together\, this research seeks\nto unify brain-inspired sparsity with continual and in-context learning as a foundation\nfor adaptive general intelligence. \nEvent Host: Assel Kembay\, Ph.D. Student\, Electrical & Computer Engineering \nAdvisor: Jason Eshraghian \nZoom: https://ucsc.zoom.us/j/92202931005?pwd=peVIc4e03fUPwFqlGa6yWx6ZlL33lI.1 \nPasscode: 742766
URL:https://events.ucsc.edu/event/kembay-a-ece-sparse-and-continual-foundations-for-adaptive-general-intelligence/
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:20260710T110000
DTEND;TZID=America/Los_Angeles:20260710T123000
DTSTAMP:20260626T170310Z
CREATED:20260626T170310Z
LAST-MODIFIED:20260626T170310Z
UID:10014993-1783681200-1783686600@events.ucsc.edu
SUMMARY:Levine\, R. (CSE) - Validating GPU Memory Consistency and Safety at Scale
DESCRIPTION:Graphics Processing Units (GPUs) have become essential platforms for parallel computing\, supporting applications far beyond graphics. Central to GPU programming models is its memory consistency specification (MCS)\, which defines the semantics of concurrent shared-memory operations and interacts with other language features to determine security guarantees such as memory safety. Understanding whether implementations conform to an MCS\, and whether the MCS provides a sound abstraction of real hardware\, is essential for reasoning about GPU programs and validating implementations. \nThis thesis develops techniques and large-scale studies for validating GPU memory consistency and memory safety. First\, it introduces MC Mutants\, a mutation testing methodology that systematically evaluates GPU MCS test environments. Applied to WebGPU\, MC Mutants generates a suite of conformance tests and uncovers two implementation bugs. Next\, it presents GPUHarbor\, a browser- and Android-based framework for large-scale testing across commodity GPUs. GPUHarbor enables a study of 106 GPUs from seven vendors\, reveals two previously unknown memory consistency bugs\, and provides new insights into GPU behavior that inform subsequent architectural and security studies. Finally\, this thesis presents SafeRace\, a collection of security assessments and specification proposals for preserving WebGPU memory safety in the presence of data races. Evaluated across dozens of GPUs and 21 WebGPU compilation stacks\, SafeRace identifies vulnerabilities in multiple GPU implementations\, including one assigned a CVE\, and proposes a validated path toward stronger memory safety guarantees in WebGPU. \nEvent Host: Reese Levine\, Ph.D. Candidate\, Computer Science & Engineering \nAdvisor: Tyler Sorensen \nZoom: https://ucsc.zoom.us/j/94641390195?pwd=RWXp9aprCMqmaAo8nq7oKwqTt02zwN.1 \nPasscode: 628349
URL:https://events.ucsc.edu/event/levine-r-cse-validating-gpu-memory-consistency-and-safety-at-scale/
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:20260630T173000
DTEND;TZID=America/Los_Angeles:20260630T200000
DTSTAMP:20260603T215647Z
CREATED:20260603T215647Z
LAST-MODIFIED:20260603T215647Z
UID:10014896-1782840600-1782849600@events.ucsc.edu
SUMMARY:Inaugural PyTorch Santa Cruz Meetup
DESCRIPTION:A community gathering of people interested in PyTorch and the projects that use it – not an official PyTorch organization. Sponsored by Red Hat and University of California Santa Cruz \nLocation: Engineering 2\, Room 180 \n​Food\, Socializing\, and Excellent talks from the PyTorch Ecosystem\n\n5:30 – 6:30 Food and Socializing\n6:30 – 7:00 Talk 1\n​7:00 – 7:30 Talk 2\n7:30 – 8:00 Talk 3\n\nFor detailed agenda and registration – visit the event website.
URL:https://events.ucsc.edu/event/inaugural-pytorch-santa-cruz-meetup/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Meetings & Conferences
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260625T140000
DTEND;TZID=America/Los_Angeles:20260625T160000
DTSTAMP:20260625T183144Z
CREATED:20260625T183144Z
LAST-MODIFIED:20260625T183144Z
UID:10014992-1782396000-1782403200@events.ucsc.edu
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:20260622T225613Z
CREATED:20260622T225613Z
LAST-MODIFIED:20260622T225613Z
UID:10014924-1782392400-1782396000@events.ucsc.edu
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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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260615T130000
DTEND;TZID=America/Los_Angeles:20260615T150000
DTSTAMP:20260609T215214Z
CREATED:20260609T215214Z
LAST-MODIFIED:20260609T215214Z
UID:10014915-1781528400-1781535600@events.ucsc.edu
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:20260526T161617Z
CREATED:20260526T161617Z
LAST-MODIFIED:20260526T161617Z
UID:10014865-1781006400-1781010000@events.ucsc.edu
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:20260526T194445Z
CREATED:20260526T194326Z
LAST-MODIFIED:20260526T194445Z
UID:10014873-1781001000-1781010000@events.ucsc.edu
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:20260527T160819Z
CREATED:20260527T160819Z
LAST-MODIFIED:20260527T160819Z
UID:10014878-1780646400-1780653600@events.ucsc.edu
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:20260528T203838Z
CREATED:20260528T203838Z
LAST-MODIFIED:20260528T203838Z
UID:10014885-1780567200-1780574400@events.ucsc.edu
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:20260512T171434Z
CREATED:20260512T161057Z
LAST-MODIFIED:20260512T171434Z
UID:10014625-1780567200-1780574400@events.ucsc.edu
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:20260602T193539Z
CREATED:20260602T193539Z
LAST-MODIFIED:20260602T193539Z
UID:10014898-1780498800-1780509600@events.ucsc.edu
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:20260529T172740Z
CREATED:20260529T172740Z
LAST-MODIFIED:20260529T172740Z
UID:10014889-1780484400-1780488900@events.ucsc.edu
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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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260602T140000
DTEND;TZID=America/Los_Angeles:20260602T160000
DTSTAMP:20260527T204156Z
CREATED:20260527T204156Z
LAST-MODIFIED:20260527T204156Z
UID:10014880-1780408800-1780416000@events.ucsc.edu
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:20260526T162137Z
CREATED:20260526T162137Z
LAST-MODIFIED:20260526T162137Z
UID:10014866-1780405200-1780412400@events.ucsc.edu
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:20260526T191332Z
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:20260528T185942Z
CREATED:20260528T185942Z
LAST-MODIFIED:20260528T185942Z
UID:10014884-1780310400-1780314300@events.ucsc.edu
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:20260512T163221Z
CREATED:20260512T162505Z
LAST-MODIFIED:20260512T163221Z
UID:10014627-1780063200-1780070400@events.ucsc.edu
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:20260526T163353Z
CREATED:20260526T163353Z
LAST-MODIFIED:20260526T163353Z
UID:10014868-1779969600-1779976800@events.ucsc.edu
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:20260522T165248Z
CREATED:20260522T165248Z
LAST-MODIFIED:20260522T165248Z
UID:10014863-1779966000-1779969600@events.ucsc.edu
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:20260330T203942Z
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:20260512T164007Z
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:20260518T190031Z
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:20260520T182036Z
CREATED:20260514T202927Z
LAST-MODIFIED:20260520T182036Z
UID:10014640-1779789600-1779793200@events.ucsc.edu
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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