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DTSTART;TZID=America/Los_Angeles:20260817T010000
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DTSTAMP:20260820T192853Z
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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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DTSTART;TZID=America/Los_Angeles:20260821T110000
DTEND;TZID=America/Los_Angeles:20260821T120000
DTSTAMP:20260820T171344Z
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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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DTSTART;TZID=America/Los_Angeles:20260821T150000
DTEND;TZID=America/Los_Angeles:20260821T160000
DTSTAMP:20260817T160248Z
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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
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