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DTSTART;TZID=America/Los_Angeles:20260206T110000
DTEND;TZID=America/Los_Angeles:20260206T120000
DTSTAMP:20260424T105132
CREATED:20260127T193801Z
LAST-MODIFIED:20260127T193801Z
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SUMMARY:Johnstone\, J. (AM) - The Effects of Asymmetry on Overshooting and Magnetic Pumping from Compressible Convection Zones
DESCRIPTION:We present a comprehensive numerical investigation examining how vertical asymmetry in compressible convection affects overshooting and the transport of large-scale magnetic fields from convective to stably stratified regions. Using three-dimensional direct numerical simulations\, we systematically vary the superadiabaticity and stratification of a convective layer to control the vertical asymmetry of the flow and analyze its influence on overshooting depth and magnetic pumping efficiency. We extend previous work by Tobias et al. (2001) and draw guidance from the asymmetry regimes identified by John & Schumacher (2023)\, investigating whether similar asymmetric convecting regimes emerge in our overshooting model that incorporates a stably stratified region below. We find that vertical asymmetry increases significantly with stratification at a moderate\, fixed Rayleigh number\, while superadiabaticity contributes primarily through enhanced downflow velocities\, with both combined leading to increasing overshooting depths reaching approximately 0.46 − 0.7 pressure scale heights. Magnetic pumping efficiency initially increases with stratification but unexpectedly decreases at higher stratification\, despite increasing overshooting depths. We find that this behavior arises from the increasing thermal and magnetic diffusivities that result from increasing stratification at fixed Ra. When instead either holding these diffusivities constant or increasing Ra sufficiently\, we find that then both overshooting and magnetic pumping depths both decrease with increasing stratification. This behavior is explained by a change of dynamical state from one of laminar downflows to one of turbulent downflowing plumes leading to a high degree of turbulent mixing and entrainment. We thus find two distinct regimes that might be described as a microscopically diffusive regime and a turbulently diffusive one. These results suggest that\, in the highly turbulent regime expected in the Sun\, magnetic pumping efficiency may decrease with increasing stratification due to enhanced turbulent entrainment\, with important implications for solar dynamo theory and the transport of large-scale magnetic fields in the solar interior. \n  \nEvent Host: Jason Johnstone\, Ph.D. Student\, Applied Mathematics \nAdvisor: Nic Brummell \nZoom- https://ucsc.zoom.us/j/5428987373?pwd=JSmNz3ZZby5ZnVBYbSoakjjQb2qQj6.1&omn=98571815542 \nPasscode- 778899
URL:https://events.ucsc.edu/event/johnstone-j-am-the-effects-of-asymmetry-on-overshooting-and-magnetic-pumping-from-compressible-convection-zones/
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:20260206T160000
DTEND;TZID=America/Los_Angeles:20260206T180000
DTSTAMP:20260424T105132
CREATED:20260128T172826Z
LAST-MODIFIED:20260128T172826Z
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SUMMARY:Yang\, J. (CSE) - Towards Controllable and Compositional Generative Vision
DESCRIPTION:Diffusion-based text-to-image models can generate impressive images\, but they largely treat an image as a single\, flat output\, which makes precise editing of individual elements difficult. This proposal studies layered generative representations that align with professional editing workflows\, enabling users to manipulate foreground objects while preserving the rest of the scene. A central focus is visual effects such as shadows and reflections\, which are essential for realistic composition yet are often missing or inconsistent in current generative pipelines. This proposal outlines a research program toward controllable\, compositional image generation that supports practical\, edit-ready content creation. \nEvent Host: Jinrui Yang\, Ph.D. Student\, Computer Science and Engineering \nAdvisor: Yuyin Zhou \nZoom- https://ucsc.zoom.us/j/91510964517?pwd=NG5Urv2li9HxlcUKrybg6Z5ZtYj9e6.1 \nPasscode- 544143
URL:https://events.ucsc.edu/event/yang-j-cse-towards-controllable-and-compositional-generative-vision/
LOCATION:
CATEGORIES:Ph.D. Presentations
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260209T130000
DTEND;TZID=America/Los_Angeles:20260209T143000
DTSTAMP:20260424T105132
CREATED:20260127T195054Z
LAST-MODIFIED:20260127T195054Z
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SUMMARY:Li\, X. (CSE) - Compute-Efficient Scaling of Fully-Open Visual Encoders
DESCRIPTION:Vision encoders have demonstrated significant performance gains in visual generation and multimodal reasoning. These improvements are primarily attributed to the scaling of data\, model capacity\, and compute. However\, this progress is becoming less accessible due to a lack of transparency in data curation and training recipes. In combination with the high compute requirements of foundation-scale pre-training\, these factors hinder independent reproducibility. \nIn this dissertation\, we democratize large-scale visual encoder training by developing compute-efficient\, reproducible training recipes for video encoders\, vision-language models (VLMs)\, and multimodal large language models (MLLMs). First\, we challenge the common belief that scaling necessarily requires proportionally more resources. Specifically\, we show that decoupled pre-training separates key factors such as space/time and token length\, and learns strong priors first. This design yields dramatic efficiency gains across image\, video\, and generative modeling. Next\, we address the challenge of undisclosed or inaccessible training data by releasing and systematically studying the curation of high-quality\, large-scale datasets. We demonstrate that high-quality synthetic captions at scale enable vision-language models to learn stronger visual representations\, especially when paired with training frameworks that unify contrastive and generative objectives. Lastly\, building on these findings\, we develop fully open vision encoders with complete training data\, recipes\, and checkpoints\, and show that transparency can enable rather than hinder state-of-the-art performance as an MLLMs’ visual backbone. \nTogether\, these contributions establish that openness and efficiency are mutually reinforcing\, providing a reproducible foundation for the next generation of visual intelligence. \nEvent Host: Xianhang Li\, Ph.D. Candidate\, Computer Science and Engineering \nAdvisor: Cihang Xie  \nZoom- https://ucsc.zoom.us/j/95801462664?pwd=koENnyV65jyPnkJYTbiYr1jaNsV5BE.1 \nPasscode- 782017
URL:https://events.ucsc.edu/event/li-x-cse-compute-efficient-scaling-of-fully-open-visual-encoders/
LOCATION:
CATEGORIES:Ph.D. Presentations
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260211T120000
DTEND;TZID=America/Los_Angeles:20260211T130000
DTSTAMP:20260424T105132
CREATED:20260203T232101Z
LAST-MODIFIED:20260203T232101Z
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SUMMARY:Centering the Experiences of Undocumented Transfer Students at HSIs: A Brown Bag Presentation by Valeria Alonso Blanco
DESCRIPTION:  \nThe Huerta Center is proud to present a brown bag presentation by Graduate Student Research Awardee Valeria Alonso Blanco. She will present on a qualitative study that explores how undocumented Latinx transfer students navigate institutional support\, belonging\, and barriers at a four-year Hispanic Serving Institution (HSI). Findings reveal gaps between institutional commitments and student realities\, and she offers actionable recommendations for more equitable\, transfer-receptive practices.
URL:https://events.ucsc.edu/event/centering-the-experiences-of-undocumented-transfer-students-at-hsis-a-brown-bag-presentation-by-valeria-alonso-blanco/
LOCATION:Huerta Center Conference Room (Casa Latina)\, 641 Merrill Rd\, Santa Cruz\,\, CA\, 95064
CATEGORIES:Lectures & Presentations,Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260212T163000
DTEND;TZID=America/Los_Angeles:20260212T173000
DTSTAMP:20260424T105132
CREATED:20260203T172912Z
LAST-MODIFIED:20260203T173017Z
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SUMMARY:Sambamurthy\, A. (AM) - Lazy Diffusion: Resolving Spectral Collapse in Generative Models for Turbulence
DESCRIPTION:Diffusion-based generative models offer a principled framework for probabilistic forecasting\, but we show they suffer from a fundamental spectral collapse when applied to turbulent flows. A Fourier-space analysis of the forward SDE reveals that the mode-wise signal-to-noise ratio decays monotonically in wavenumber for power-law spectra\, rendering high-wavenumber content indistinguishable from noise. We reinterpret the noise schedule as a spectral regularizer and introduce power-law schedules that preserve fine-scale structure deeper into diffusion time. We further propose Lazy Diffusion\, a one-step distillation method that leverages the learned score geometry to bypass long reverse trajectories and prevent high-wavenumber degradation. Applied to high-Reynolds-number 2D Kolmogorov turbulence and ocean reanalysis data\, these methods resolve spectral collapse and enable stable long-horizon autoregressive emulation. \nEvent Host: Anish Sambamurthy\, Ph.D. Student\, Applied Mathematics  \nAdvisor: Ashesh Chattopadhyay \nZoom- https://ucsc.zoom.us/j/5144530307?pwd=TllaWnNDc01tcVNpa1NNeVVIMnp5QT09 \nPasscode- 55555
URL:https://events.ucsc.edu/event/sambamurthy-a-am-lazy-diffusion-resolving-spectral-collapse-in-generative-models-for-turbulence/
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:20260220T140000
DTEND;TZID=America/Los_Angeles:20260220T160000
DTSTAMP:20260424T105132
CREATED:20260210T193542Z
LAST-MODIFIED:20260210T193542Z
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SUMMARY:Fredrickson\, K. (CSE) - Practical Anonymity with Formal Resistance to Traffic Analysis
DESCRIPTION:Anonymous communication systems hide who is talking to whom\, not just what is said. However\, existing systems are either vulnerable to traffic analysis attacks–attacks where adversaries observe and correlate the network traffic of users–or are forced to rely on unrealistic and unenforceable assumptions about how users behave. Worse\, existing theory lacks tools to rigorously model traffic analysis attacks\, much less inform whether if a system is secure against traffic analysis or how to design systems that are. \nWe make several contributions toward our goal of practical anonymity systems that resist traffic analysis. First\, we develop the first formal framework for describing the security of systems against traffic analysis attacks\, allowing us to quantitatively describe and compare the security of all existing works. Second\, leveraging this framework\, we develop a security definition that distinguishes between systems that are and are not susceptible to traffic analysis. We call this property input/output independence. We use this definition to prove that the dominant model of systems–synchronous systems–cannot practically provide input/output independence. We then design a new asynchronous anonymity functionality\, deferred retrieval\, that achieves input/output independence with far more flexible user assumptions and up to 3400 times less traffic overhead for the same latency compared to prior methods. Finally\, we design and implement Sparta\, a family of high-throughput\, scalable instantiations of deferred retrieval using trusted execution environments and oblivious algorithms\, yielding the first practical anonymity systems that are formally resistant to long-term traffic analysis. \nEvent Host: Kyle Fredrickson\, Ph.D. Candidate\, Computer Science and Engineering \nAdvisor: Darrell Long \nZoom – https://ucsc.zoom.us/j/98133127429?pwd=QNICsMrQa6bQUKNPo40PthZyQEQCFl.1 \nPasscode – 242206
URL:https://events.ucsc.edu/event/fredrickson-k-cse-practical-anonymity-with-formal-resistance-to-traffic-analysis/
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:20260225T090000
DTEND;TZID=America/Los_Angeles:20260225T120000
DTSTAMP:20260424T105132
CREATED:20260210T221905Z
LAST-MODIFIED:20260210T221905Z
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SUMMARY:Liu\, C. (CSE) - Enabling LLM Unlearning at Inference Time by Decomposing Detection and Intervention
DESCRIPTION:Machine unlearning addresses the “right to be forgotten” under GDPR and enables privacy\, copyright\, and safety compliance in large language models. Training-based unlearning can remove targeted behavior on benchmarks\, but it scales poorly\, can degrade utility\, and can fail under adversarial prompting that recovers supposedly forgotten content. This prospectus proposes inference-time behavioral unlearning: rather than modifying weights to “erase” knowledge\, we detect when a query targets forgotten content and intervene in generation so the system behaves like a model never trained on that content. We formalize this approach as Detect-Intervene Decomposition and instantiate it with three complementary methods operating at the embedding\, token\, and reasoning levels under different access capabilities. Comprehensive experiments across entity unlearning\, hazardous knowledge removal\, and copyright protection demonstrate that our methods match or exceed training-based approaches while being orders of magnitude faster and preserving model utility. As LLMs increasingly operate as services with restricted weight access\, inference-time unlearning provides the only practical path for responsible AI deployment that respects privacy\, safety\, and legal requirements. \nEvent Host: Chris Liu\, Ph.D. Student\, Computer Science and Engineering \nAdvisor: Yang Liu \nZoom – https://ucsc.zoom.us/j/94799852992?pwd=EBFQe4U2lRNro1oJ8F36bgORhT2xSv.1 \nPasscode –  242384
URL:https://events.ucsc.edu/event/liu-c-cse-enabling-llm-unlearning-at-inference-time-by-decomposing-detection-and-intervention/
LOCATION:
CATEGORIES:Ph.D. Presentations
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