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DTSTART;TZID=America/Los_Angeles:20260817T010000
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DTSTAMP:20260820T192853Z
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UID:10015350-1786928400-1789088399@events.ucsc.edu
SUMMARY:Graduate Preparation Program
DESCRIPTION:The Graduate Preparation Program (GPP) is a four-week intensive non-credit course offered in person on the main campus prior to the fall quarter\, which is open to all current and newly admitted international graduate students. It focuses on English for Academic Purposes (EAP)\, academic skills\, and cultural orientation. The program also provides a foundation for transitioning into the Teaching Assistant role. \nThe course offers guided support with: \n\nSeminar-style classroom discussion\nPresentation and oral communication skills\nInformation on U.S. classroom culture; roles and responsibilities of faculty\, Teaching Assistants\, and students\nCritical thinking: using evidence to support ideas\nResearch writing\, citation\, and avoiding plagiarism\nCollaborative and project-based learning\n\nA collaboration between the Graduate Division and Global Engagement\, the Graduate Preparation Program provides a strong foundation for the U.S. classroom environment and university system\, in addition to the opportunity to practice and master the language and academic skills needed for success. \nFor more information and to register\, please visit our webpage.
URL:https://events.ucsc.edu/event/graduate-preparation-program/
CATEGORIES:Conference,Lectures & Presentations
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260817T100000
DTEND;TZID=America/Los_Angeles:20260817T120000
DTSTAMP:20260810T162940Z
CREATED:20260810T162940Z
LAST-MODIFIED:20260810T162940Z
UID:10015328-1786960800-1786968000@events.ucsc.edu
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:20260817T130000
DTEND;TZID=America/Los_Angeles:20260817T150000
DTSTAMP:20260813T194341Z
CREATED:20260813T194341Z
LAST-MODIFIED:20260813T194341Z
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SUMMARY:Condon\, C. (BMEB) - Genomic conflict across scales
DESCRIPTION:Genomes are often viewed as cooperative systems in which genes work together to support organismal function. Yet genetic elements can also act in ways that favor their own transmission or persistence\, creating conflict within the genome. In this talk\, I examine the evolutionary and functional consequences of such genomic conflict across three systems. First\, I investigate segregation distortion in Arabidopsis hybrids and its potential role in the early evolution of reproductive isolation. Second\, I characterize the population dynamics and functional effects of introners\, mobile elements that generate new introns in the green alga Micromonas pusilla. Finally\, I explore widespread splicing dysfunction in algal mating-type chromosomes and its consequences for transcript diversity. Together\, these studies highlight how departures from genome cooperation can shape inheritance\, genome evolution\, and gene regulation. \nEvent Host: Chris Condon\, Ph.D. Candidate\, Biomolecular Engineering & Bioinformatics  \nAdvisor: Russell Corbett-Detig
URL:https://events.ucsc.edu/event/condon-c-bmeb-genomic-conflict-across-scales/
LOCATION:Biomedical Sciences Building\, 575 McLaughlin Drive
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260817T180000
DTEND;TZID=America/Los_Angeles:20260817T190000
DTSTAMP:20260730T192605Z
CREATED:20260730T192605Z
LAST-MODIFIED:20260730T192605Z
UID:10015118-1786989600-1786993200@events.ucsc.edu
SUMMARY:Build better chips
DESCRIPTION:Join us for an interactive discussion on current industry trends\, emerging skills\, and career opportunities\, and learn how our Silicon Chip Design & Semiconductor Engineering courses are designed to help professionals build expertise\, stay competitive\, and achieve their educational and career goals. \nWhether you’re looking to advance in your current role\, transition into a new field\, or expand your knowledge\, this session is a great place to start. \nYour speaker\nArvind Vidyarthi –  M.S.E.E. is vice president of Silicon Design Engineering at Altera\, where he leads SoC and IP physical design implementation\, methodology\, and automation. Arvind has served since 2022 as chair of the UCSC Silicon Valley Extension Silicon Chip Design & Semiconductor Engineering program\, where he also sits on the VLSI engineering advisory group and mentors the next generation of design engineers. \nKeep learning\nThe Silicon Chip Design & Semiconductor Engineering program presents this information session. Visit our program page for complete program details and a closer look at all upcoming courses. \nClaim your seat today. 
URL:https://events.ucsc.edu/event/build-better-chips-2/
LOCATION:Silicon Valley Campus\, 3175 Bowers Avenue\, Santa Clara\, CA\, 95054\, United States
CATEGORIES:Lectures & Presentations,Meetings & Conferences,Training
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260818T100000
DTEND;TZID=America/Los_Angeles:20260818T110000
DTSTAMP:20260810T162245Z
CREATED:20260810T162245Z
LAST-MODIFIED:20260810T162245Z
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SUMMARY:Gutie\, J. (SciCAM) -  SORh: Hyperbolic Relaxation Methods For Elliptic Problems In Computational Fluid Dynamics
DESCRIPTION:This thesis explores iterative methods for solving elliptic partial differential equations (PDEs)\, which are used in computational fluid dynamics (CFD) to model a wide range of physical phenomena. The primary application of interest here is self-gravity\, modeled by Poisson’s equation. Although many numerical approaches exist\, including direct matrix inversion\, FFT-based methods\, and classical iterative methods such as Jacobi and Gauss-Seidel\, these approaches involve tradeoffs in computational cost\, scalability\, implementation complexity\, and adaptability to changing boundary conditions and problem configurations. \nTherefore\, we introduce SORh\, a simple and efficient relaxation method derived from a hyperbolic reformulation of Poisson’s equation. SORh generalizes classical successive over-relaxation (SOR) by providing independent control of residual relaxation and the directional propagation of Gauss–Seidel corrections. We present formulations of SORh in one and two spatial dimensions and investigate its stability\, accuracy\, and computational performance through analytical derivations and numerical comparisons with established relaxation methods. The results identify favorable SORh formulations\, clarify their relationships to classical relaxation methods\, and demonstrate improved convergence on selected test problems. Finally\, we demonstrate applications of SORh to astrophysical self-gravity simulations in the FLASH code and to magnetohydrodynamic (MHD) divergence cleaning. \nEvent Host: Jonathan Guite\, M.S. Candidate\, Scientific Computing & Applied Mathematics  \nAdvisor: Dongwook Lee \nZoom: https://ucsc.zoom.us/j/92153750104?pwd=ZdLiDZeLqOAlVNX9C4bCloKno9tAeB.1 \nPasscode: 769232
URL:https://events.ucsc.edu/event/gutie-j-scicam-sorh-hyperbolic-relaxation-methods-for-elliptic-problems-in-computational-fluid-dynamics/
CATEGORIES:Ph.D. Presentations
ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option-1.jpg
LOCATION:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260818T140000
DTEND;TZID=America/Los_Angeles:20260818T160000
DTSTAMP:20260817T160201Z
CREATED:20260817T155423Z
LAST-MODIFIED:20260817T160201Z
UID:10015341-1787061600-1787068800@events.ucsc.edu
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:20260820T130000
DTEND;TZID=America/Los_Angeles:20260820T150000
DTSTAMP:20260814T163909Z
CREATED:20260814T163815Z
LAST-MODIFIED:20260814T163909Z
UID:10015340-1787230800-1787238000@events.ucsc.edu
SUMMARY:Penunuri\, G. (BMEB) - Genomic\, Proteomic\, and Computational Approaches to the Study of Host-Microbe Systems
DESCRIPTION:Host-microbe systems are core to some of biology’s most consequential interactions\, from the pathogens that drive infectious disease to symbionts affecting agricultural pest control and vector-borne disease transmission. Yet unlike the model organisms that have driven most of modern molecular biology\, the microbes at the center of these interactions are rarely genetically tractable: many cannot be cultured outside a host\, resist standard tools for genetic manipulation\, and are annotated largely by homology to distantly related free-living relatives. This dissertation develops genomic\, proteomic\, and computational methods to work around this lack of infrastructure and contribute techniques and tools to the study and further understanding of host-microbe systems. Using Wolbachia cultured in Drosophila melanogaster cell lines\, I demonstrate that chemical mutagenesis can be used to perturb intracellular genomes leaving a detectable mutational signal. I employ a low error rate sequencing technique to record and model the mutational landscape left by the mutagen ethyl methanesulfonate (EMS) demonstrating its use for mutagenesis screens of intracellular bacteria. I next utilize structural proteome datasets to screen host-microbe proteomes for strong candidates of molecular mimicry\, microbe proteins that have coevolved a eukaryotic like domain or structure and suggest use for host manipulation or microbe survival in the host environment. Building off of this screen for novel effectors through structural alignments I develop and test a distributed computing system for performing large scale systematic literature reviews. Altogether these projects represent generalizable approaches to the study of host-microbe systems reaching from classically studied and thoroughly understood to novel and non-model systems. \nEvent Host: Gabriel Penunuri\, Ph.D. Candidate\, Biomolecular Engineering & Bioinformatics  \nAdvisor: Russell Corbett-Detig \nZoom: https://ucsc.zoom.us/j/98216883331?pwd=uqmUSQba2X6GVNBhOhAGRwgCZyjAyj.1 \nPasscode: 730377
URL:https://events.ucsc.edu/event/penunuri-g-bmeb-genomic-proteomic-and-computational-approaches-to-the-study-of-host-microbe-systems/
LOCATION:Biomedical Sciences Building\, 575 McLaughlin Drive
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260820T180000
DTEND;TZID=America/Los_Angeles:20260820T190000
DTSTAMP:20260722T235945Z
CREATED:20260722T235746Z
LAST-MODIFIED:20260722T235945Z
UID:10015113-1787248800-1787252400@events.ucsc.edu
SUMMARY:Become a Special Education Teacher Assistant
DESCRIPTION:Interested in becoming a Special Education Teacher Assistant? \nJoin us for an informative session to explore the courses designed to prepare you for success in inclusive and special education classrooms. We’ll provide an overview of the curriculum\, discuss current industry trends and workforce demand\, highlight career opportunities\, and answer your questions about getting started. \nYour speaker\nSharmila Roy\, Ph.D.\, began her teaching journey in India and pursued further training in the United Kingdom before earning her doctorate in Special Education from the University of Buffalo in New York. She brings over three decades of expertise in special education to her role as program chair of the Special Education Teacher Assistant certificate program at UCSC Silicon Valley Extension. \nKeep learning\nThis session is hosted by the Special Education Teacher Assistant program. Visit our program page for additional details and a closer look at all upcoming courses. \nCLAIM YOUR SEAT TODAY.
URL:https://events.ucsc.edu/event/become-a-special-education-teacher-assistant/
LOCATION:Silicon Valley Campus\, 3175 Bowers Avenue\, Santa Clara\, CA\, 95054\, United States
CATEGORIES:Lectures & Presentations,Meetings & Conferences,Training
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260820T180000
DTEND;TZID=America/Los_Angeles:20260820T190000
DTSTAMP:20260730T195214Z
CREATED:20260730T195214Z
LAST-MODIFIED:20260730T195214Z
UID:10015122-1787248800-1787252400@events.ucsc.edu
SUMMARY:How to Apply to the Premed Postbacc Program
DESCRIPTION:Join UC Santa Cruz Healthcare Pathways for a live online information session to learn about our Premed Postbacc Cohort\, Premed and Pre-Health DIY Pathways\, and other program offerings. \nWe’ll provide an overview of our programs and cover how to apply for our next admissions cycle\, which opens August 7. We’ll also discuss the application process\, admissions timeline\, coursework\, advising\, MCAT preparation\, and more. \nYou’ll have plenty of time to ask questions and connect directly with program staff. \nCLAIM YOUR SEAT TODAY. 
URL:https://events.ucsc.edu/event/how-to-apply-to-the-premed-postbacc-program-2/
LOCATION:Silicon Valley Campus\, 3175 Bowers Avenue\, Santa Clara\, CA\, 95054\, United States
CATEGORIES:Lectures & Presentations,Meetings & Conferences,Training
ATTACH;FMTTYPE=image/png:https://events.ucsc.edu/wp-content/uploads/2026/01/SM-Cal-3.png
GEO:37.3796975;-121.9765484
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Silicon Valley Campus 3175 Bowers Avenue Santa Clara CA 95054 United States;X-APPLE-RADIUS=500;X-TITLE=3175 Bowers Avenue:geo:-121.9765484,37.3796975
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260821T110000
DTEND;TZID=America/Los_Angeles:20260821T120000
DTSTAMP:20260820T171344Z
CREATED:20260820T171314Z
LAST-MODIFIED:20260820T171344Z
UID:10015349-1787310000-1787313600@events.ucsc.edu
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:20260821T150000
DTEND;TZID=America/Los_Angeles:20260821T160000
DTSTAMP:20260817T160248Z
CREATED:20260817T160020Z
LAST-MODIFIED:20260817T160248Z
UID:10015342-1787324400-1787328000@events.ucsc.edu
SUMMARY:Huang\, X. (CSE) - Scalable and Verifiable Reasoning for Medical Foundation Models
DESCRIPTION:This PhD research focuses on developing reliable medical foundation models capable of reasoning across textual\, visual\, and interactive clinical information. The work investigates three complementary directions: improving medical reasoning through test-time scaling\, training multimodal medical models with verifiable rewards\, and synthesizing high-quality visual question-answering data from biomedical literature using generator-verifier frameworks. Building on these efforts\, the proposed research will extend medical language and multimodal models toward agentic systems that can gather evidence\, use external tools\, integrate multimodal information\, and verify decisions over sequential interactions. Overall\, this research aims to improve the reliability\, efficiency\, and transparency of medical AI reasoning while supporting reproducible and human-supervised applications in healthcare. \nEvent Host: Xiaoke Huang\, Ph.D. Student\, Computer Science & Engineering \nAdvisor: Yuyin Zhou \nZoom: https://ucsc.zoom.us/j/8855787311 \nPasscode: 197379
URL:https://events.ucsc.edu/event/huang-x-cse-scalable-and-verifiable-reasoning-for-medical-foundation-models/
CATEGORIES:Ph.D. Presentations
ATTACH;FMTTYPE=image/png:https://events.ucsc.edu/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option-3.png
LOCATION:
END:VEVENT
END:VCALENDAR