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DTSTART;TZID=America/Los_Angeles:20250818T103000
DTEND;TZID=America/Los_Angeles:20250818T103000
DTSTAMP:20260420T011400
CREATED:20250814T070000Z
LAST-MODIFIED:20250925T231431Z
UID:10000103-1755513000-1755513000@events.ucsc.edu
SUMMARY:Vats\, V. (CSE) - Learning to Remember: Multi-Agent Self-Refinement toward Persistent Machine Perception
DESCRIPTION:Modern machine perception is powerful yet brittle: failing in response to subtle data adversaries and lacking mechanisms to learn from their errors. We address this challenge by progressing from diagnosing such failures to developing a framework for persistent learning. We first investigate the sources of this fragility\, demonstrating how both naturally occurring adversarial artifacts\, like specular highlights\, and conditions of data scarcity fundamentally limit model robustness. We then replace a passive reliance on quality data curation with active\, multi-agent refinement: a Worker-Supervisor loop that iteratively critiques and corrects outputs to meet complex\, rule-rich guidelines at inference time. While this system achieves dynamic error correction\, it rarely remembers what was learned. We thus plan to tackle this problem of non-remembrance by proposing an experience memory that records validated fixes as reusable insights\, retrieves them when similar contexts recur\, and\, where available\, grounds them across viewpoints and time. Together\, these components turn momentary fixes into long-term skills\, paving the way for more capable and reliable perception in fields like augmented reality and robotics. \nEvent Host: Vanshika Vats\, PhD Student\, Computer Science & Engineering \nAdvisor: James Davis
URL:https://events.ucsc.edu/event/vats-v-cse-learning-to-remember-multi-agent-self-refinement-toward-persistent-machine-perception/
LOCATION:CA
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250818T180000
DTEND;TZID=America/Los_Angeles:20250818T180000
DTSTAMP:20260420T011400
CREATED:20250807T070000Z
LAST-MODIFIED:20250925T231429Z
UID:10000096-1755540000-1755540000@events.ucsc.edu
SUMMARY:Model Context Protocol: Why It Matters
DESCRIPTION:The Impact of Model Context Protocol \nJoin us for an engaging exploration of the Model Context Protocol—a groundbreaking framework designed to improve communication and context-sharing across AI agents. As AI systems become more modular and collaborative\, MCP offers a powerful solution for maintaining continuity across tasks\, tools\, and models. This free\, online event is sponsored by the AI Program at UCSC Silicon Valley Extension. \n\nTopics \n\nThe origins and motivations behind MCP\nHow MCP structures and preserves context\nThe growing importance of MCP in the development of scalable\, interoperable AI agent systems\nReal-world use cases\nCurrent limitations of MCP\nHow MCP is shaping the future of AI infrastructure\nHow to advance your knowledge and skills in this frontier technology.\n\n\nSpeaker \nPraveen Krishna\, AI Program chair and platform architect for Audio AI/ML Solutions\, Performance & Power\, Intel\, teaches AI Essentials\, Deep Learning and Artificial Intelligence\, Open Computer AI Agent by Hugging Face\, Practical uses of DeepSeek/Llama\, Computer Vision and Image Processing\, and Capstone Building Integrated AI Applications. \n\nInterested in a deeper dive into this topic? \nAI Technology Workshop Series: Model Context Protocol (Aug. 29)\nA one-day workshop held in person at the Silicon Valley Campus or online.
URL:https://events.ucsc.edu/event/model-context-protocol-what-you-need-to-know-why-it-matters/
LOCATION:CA
CATEGORIES:Lectures & Presentations,Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250819T100000
DTEND;TZID=America/Los_Angeles:20250819T100000
DTSTAMP:20260420T011400
CREATED:20250815T070000Z
LAST-MODIFIED:20250925T231431Z
UID:10000105-1755597600-1755597600@events.ucsc.edu
SUMMARY:Osorio\, S. (AM) - Image-Based Wound Infection Classification
DESCRIPTION:This thesis investigates the use of deep learning for classifying wound infections from photographic images\, using colony-forming unit (CFU) counts as a quantitative labeling standard. Leveraging the visual information in wound photographs and the clinical relevance of bacterial burden\, the study implements a multi-task U-Net architecture for both image reconstruction and binary classification in a shared-encoder framework. Three experimental conditions were explored: one using original images with positive class weighting\, one incorporating data augmentation to enhance visual diversity\, and one employing 5-fold cross-validation with augmentation to improve validation reliability. The non-augmented model achieved 91.7% accuracy at a threshold of 0.8\, correctly identifying 4 of 5 infected cases\, while Experiment 2 achieved 87.5% accuracy at a moderate threshold of 0.5 but became more conservative at higher thresholds. The third experiment reached 79.6% accuracy at a threshold of 0.3\, detecting all 11 infected cases despite signs of overfitting. These results highlight the model's strong performance in minimizing false negatives\, particularly in the non-augmented setting\, but also reflect limitations from the small dataset\, class imbalance\, and reliance on a small validation set. These factors suggest results should be interpreted cautiously and motivate further study with larger datasets\, improved regularization\, and more varied clinical scenarios. \nEvent Host: Sebastian Osorio\, M.S. Candidate\, Scientific Computing & Applied Mathematics \nAdvsior: Marcella Gomez
URL:https://events.ucsc.edu/event/osorio-s-am-image-based-wound-infection-classification/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
GEO:37.0009723;-122.0632371
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250819T120000
DTEND;TZID=America/Los_Angeles:20250819T120000
DTSTAMP:20260420T011400
CREATED:20250626T070000Z
LAST-MODIFIED:20250925T231315Z
UID:10000049-1755604800-1755604800@events.ucsc.edu
SUMMARY:Programming with Rust
DESCRIPTION:Join us in learning more about Rust\, one of the fastest-growing programming languages\, which continues to be ranked the most-loved language by its users. Its user base\, aka “Rustaceans\,” has tripled in just two years as more and more software products are being developed in Rust. \nIn this fast-paced virtual overview with Danesh Forouhari\, we’ll talk about: \n\nThe history of Rust\nProblems Rust solves\nComparing Rust to other programming languages\nBenchmarking data (vs. C & Go)\nThe good\, the bad\, and the ugly of programming with Rust\nRunning some sample Rust code\, if time permits\n\nSpeaker\nDanesh Forouhari\, M.S.\, has more than 20 years of experience in the software development industry. He teaches Python for Programmers. \nThis fall info session is sponsored by the UCSC Silicon Valley Professional Education Computer Programming certificate program.
URL:https://events.ucsc.edu/event/programming-with-rust-6423/
LOCATION:CA
CATEGORIES:Lectures & Presentations,Meetings & Conferences,Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250819T130000
DTEND;TZID=America/Los_Angeles:20250819T130000
DTSTAMP:20260420T011400
CREATED:20250813T070000Z
LAST-MODIFIED:20250925T231430Z
UID:10000102-1755608400-1755608400@events.ucsc.edu
SUMMARY:Moreland\, Z. (AM) - Transcriptomic and Computational Analysis of Burn and Excisional Wound Healing
DESCRIPTION:Accurate assessment of wound healing progress is critical for optimizing patient care and preventing complications\, yet clinicians currently lack precise tools to determine where a wound stands in the healing timeline. Wound healing progresses through overlapping stages of inflammation\, proliferation\, and maturation\, each marked by characteristic shifts in gene expression that are difficult to interpret without robust computational methods. This paper proposes to classify wound healing stages from transcriptomic data using support vector machines combined with biologically informed clustering to serve as features for the hierarchical SVM classifiers. This approach is applied to two distinct wound types: excisional wounds in pigs (21-day timeline) and burn wounds in mice (42-day timeline)\, enabling comparison of classification performance across different injury mechanisms. The models achieved high overall accuracy\, with the burn model performing better at the classification of the stages. Both models made mistakes in distinguishing inflammation from early proliferation\, highlighting the inherent biological overlap between these transitional healing stages. Overall\, we find that transcriptomic-based classification can reliably identify wound healing stages across different wound types\, providing a foundation for developing personalized diagnostic tools that could transform clinical wound management and improve patient outcomes. \nEvent Host: Zoe Moreland\, M.S. Candidate\, Applied Mathematics \nAdvisor: Marcella Gomez
URL:https://events.ucsc.edu/event/moreland-z-am-transcriptomic-and-computational-analysis-of-burn-and-excisional-wound-healing/
LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064
GEO:37.000369;-122.0632371
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250819T140000
DTEND;TZID=America/Los_Angeles:20250819T140000
DTSTAMP:20260420T011400
CREATED:20250818T070000Z
LAST-MODIFIED:20250925T231432Z
UID:10000111-1755612000-1755612000@events.ucsc.edu
SUMMARY:Bhatia\, N. (CSE) - Building Adaptive Intelligence into Wireless Sensing
DESCRIPTION:WiFi-based indoor positioning is a widely researched area focused on determining the location of devices. Accurate indoor positioning has numerous applications\, including asset tracking and indoor navigation. Despite advances\, their adoption in practice remains limited due to several challenges such as environmental changes that cause signal fading\, multipath effects\, and interference\, all of which reduce positioning accuracy. Moreover\, telemetry data vary across WiFi device vendors\, presenting distinct features and formats\, while use-case requirements can also differ significantly. At present\, there is no unified model capable of handling these variations effectively. \nWe present WiFiGPT\, a decoder-only transformer-based system designed to address these variations while achieving high localization accuracy. Our experiments with WiFiGPT show that it can effectively capture subtle spatial patterns in noisy wireless telemetry\, making them reliable regressors. Compared to state-of-the-art methods\, our approach matches and often surpasses conventional techniques across multiple types of telemetry. Achieving sub-meter accuracy for RSSI and FTM and centimeter-level precision for CSI highlights the potential of LLM-based localization to outperform specialized methods\, without the need for handcrafted signal processing or calibration. Other work includes EchoSense\, which utilizes CSI to monitor vital signs such as heart rate and respiration with high accuracy. \nEvent Host: Nayan Bhatia\, PhD Student\, Computer Science & Engineering \nAdvisor: Katia Obraczka
URL:https://events.ucsc.edu/event/bhatia-n-cse-building-adaptive-intelligence-into-wireless-sensing/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
GEO:37.0009723;-122.0632371
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Engineering 2 Engineering 2 1156 High Street Santa Cruz CA 95064;X-APPLE-RADIUS=500;X-TITLE=Engineering 2 1156 High Street:geo:-122.0632371,37.0009723
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250819T140000
DTEND;TZID=America/Los_Angeles:20250819T140000
DTSTAMP:20260420T011400
CREATED:20250818T070000Z
LAST-MODIFIED:20250925T231627Z
UID:10000112-1755612000-1755612000@events.ucsc.edu
SUMMARY:Swaby\, A. (ECE) -  Improving X-ray Medical Imaging using Amorphous Selenium as a Photoconductive Layer
DESCRIPTION:The presence of coronary artery calcification is a strong predictor for future cardiovascular events where cardiac risk categories are quantified depending on calcification size. Dual-energy chest X-rays provide high contrast visualization to improve opportunistic screening for quantifying coronary artery calcifications\, determining bone mineral density (i.e.\, osteoporosis) and characterizing lung lesions. As a dual-energy imaging modality\, multilayer flat panel detectors acquire low- and high-energy X-ray images as a polyenergetic\, single-exposure. Combining two detectors into a dual-layer configuration\, weighted subtraction techniques in the resulting images allow for differentiation of soft tissue from the projection of the bone structures and other high attenuating materials. To improve detection of calcifications < 1 mm in size\, the performance of a dual-layer X-ray detector is investigated as a means of providing the necessary μm-resolution and spectral separation for enhanced contrast between low- and high-energy X-ray images. A cascaded linear systems model is used to simulate the modulation transfer function\, detective quantum efficiency\, and noise power spectrum of an amorphous selenium direct conversion top detector and a cesium iodide-based indirect conversion bottom detector. As the framework for system design and optimization\, a generalized task-based analysis is used to analyze how the signal projections\, noise contributions\, task function\, and weighting factors contribute to the detectability index of the dual-layer imaging system.  \nEvent Host: Akyl Swaby\, PhD Candidate\, Electrical & Computer Engineering \nAdvisor:  Dr. Shiva Abbaszadeh
URL:https://events.ucsc.edu/event/swaby-a-ece-improving-x-ray-medical-imaging-using-amorphous-selenium-as-a-photoconductive-layer/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
GEO:37.0009723;-122.0632371
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Engineering 2 Engineering 2 1156 High Street Santa Cruz CA 95064;X-APPLE-RADIUS=500;X-TITLE=Engineering 2 1156 High Street:geo:-122.0632371,37.0009723
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250820T173000
DTEND;TZID=America/Los_Angeles:20250820T173000
DTSTAMP:20260420T011400
CREATED:20250730T070000Z
LAST-MODIFIED:20250925T231319Z
UID:10000083-1755711000-1755711000@events.ucsc.edu
SUMMARY:All UC Alumni Networking Mixer in Central California
DESCRIPTION:Alumni from all 10 University of California campuses are invited to our friendly and open networking mixer at a UC alumni-owned winery in Central California.
URL:https://events.ucsc.edu/event/all-uc-alumni-networking-mixer-in-central-california/
LOCATION:CA
CATEGORIES:Lectures & Presentations
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250820T180000
DTEND;TZID=America/Los_Angeles:20250820T180000
DTSTAMP:20260420T011400
CREATED:20250717T070000Z
LAST-MODIFIED:20250925T231623Z
UID:10000073-1755712800-1755712800@events.ucsc.edu
SUMMARY:Project and Program Management Info Session
DESCRIPTION:Project Leadership with Tim Bombosch\nJoin Program Chair Tim Bombosch for UCSC Silicon Valley’s Project and Program Management certificate\, where you'll learn from PMI®-certified experts leading teams across top Silicon Valley companies. Gain the tools and strategies to define goals\, estimate costs\, manage risk\, and deliver successful outcomes \n.\nMaster Real-World Management Skills\nBuild expertise in both traditional and agile methods—Scrum\, Kanban\, SAFe\, and more. This program satisfies PMI® training requirements and prepares you for the PMP® and CAPM® exams while equipping you to lead teams and drive results in today’s fast-paced business environment. \n\nThis fall info session is sponsored by the Project and Program Management Certificate program.
URL:https://events.ucsc.edu/event/project-and-program-management-info-session-4291/
LOCATION:CA
CATEGORIES:Lectures & Presentations,Meetings & Conferences,Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250821T110000
DTEND;TZID=America/Los_Angeles:20250821T110000
DTSTAMP:20260420T011400
CREATED:20250730T070000Z
LAST-MODIFIED:20250925T231624Z
UID:10000082-1755774000-1755774000@events.ucsc.edu
SUMMARY:HireUC Alumni Career Summit
DESCRIPTION:About the career summit \nThe HireUC Alumni Career Summit\, in partnership with Hire Talent\, is a hiring event organized for University of California (UC) alumni who are looking for early- and mid-level career opportunities. It offers a special chance for alumni to connect with employers from diverse fields and industries.
URL:https://events.ucsc.edu/event/hireuc-alumni-career-summit/
LOCATION:CA
CATEGORIES:Exhibits,Lectures & Presentations
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250821T120000
DTEND;TZID=America/Los_Angeles:20250821T120000
DTSTAMP:20260420T011400
CREATED:20250815T070000Z
LAST-MODIFIED:20250925T231626Z
UID:10000104-1755777600-1755777600@events.ucsc.edu
SUMMARY:HireUC Alumni Career Fair
DESCRIPTION:The HireUC Alumni Career Fair is a hiring event designed for University of California alumni who are looking for early-and mid-level career opportunities. It offers a special chance for alumni to connect with employers from diverse fields and industries. Alumni from all 10 UC campuses are invited. UC alumni can attend the fair for free\, but it’s important to register in advance to secure participation.  \nAbout Hire Talent \n\nSince 2014\, over 100\,000+ attendees to Hire Talent Events\nCreated 1\,000s of new careers\n50% bachelors / 40% masters / 10% doctorate\nAverage experience of attendees: 6 years\n500+ University Partners\n25 Cities\n\nQuestions \n\nJeffrey Nortman jeff@gohiretalent.com\nPatricia Nguyen\, Director of Systemwide UC Santa Cruz Alumni & Diversity Initiatives patricia.nguyen@ucop.edu\n\nEmployers \nIf you work for a company looking to connect with alumni from the University of California graduating from various majors\, degrees and career levels\, please use the Employer Registration tab to get participate as a recruiter.  Employer registration is open to all employers not just UC Alumni.
URL:https://events.ucsc.edu/event/hireuc-alumni-career-fair/
LOCATION:CA
CATEGORIES:Meetings & Conferences,Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250821T130000
DTEND;TZID=America/Los_Angeles:20250821T130000
DTSTAMP:20260420T011400
CREATED:20250815T070000Z
LAST-MODIFIED:20250925T231627Z
UID:10000106-1755781200-1755781200@events.ucsc.edu
SUMMARY:Briden\, M. (CSE) -  Representation Learning and Generative Forecasting for Noisy and Limited Clinical Data: Applications in Wound Healing and EEG
DESCRIPTION:The rapid integration of artificial intelligence and machine learning into clinical practice has driven advances in disease classification\, segmentation\, and clinical decision support. However\, the complexities of medical data pose a challenge to widespread adoption. The rarity of medical conditions\, ethical considerations\, and varying acquisition protocols leads to limited and noisy data. The time-intensive process of labeling data\, the high degree of accuracy required in clinical settings\, and the ill-defined nature of certain medical conditions further complicate the application and deployment of machine learning models. Likewise\, high‐stakes medical decisions demand trustworthy and interpretable predictions. However\, prioritizing trust and explainability is rarely a primary objective in most model designs. \nThis thesis addresses three key challenges in machine learning for healthcare. First\, we develop methods for learning under noisy and limited medical data\, focusing on representation learning strategies that improve generalization when datasets are small or contain mislabeled samples. Second\, we explore the prediction of generative outcomes amid label noise and data scarcity\, utilizing parameter-efficient and temporal generative models to forecast disease trajectories. Third\, we advance trustworthy and explainable medical artificial intelligence by designing deep architectures that provide interpretable outputs suitable for clinical decision-making. \nThese challenges are addressed in the context of two complementary medical modalities: wound healing images and electroencephalogram signals. Wound healing tasks focus on predicting healing trajectories while enhancing interpretability through segmentation-based explanations and training large models in light of extreme data noise and scarcity. Electroencephalogram-based tasks emphasize representation learning and explainability for non-invasive mental state classification. These experiments demonstrate the clinical relevance of the proposed approaches and their ability to operate under challenging medical conditions across both imaging and physiological signal domains. \nEvent Host: Michael Briden\, PhD Candidate\, Computer Science & Engineering \nAdvisor: Narges Norouzi
URL:https://events.ucsc.edu/event/briden-m-cse-representation-learning-and-generative-forecasting-for-noisy-and-limited-clinical-data-applications-in-wound-healing-and-eeg/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
GEO:37.0009723;-122.0632371
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250821T180000
DTEND;TZID=America/Los_Angeles:20250821T180000
DTSTAMP:20260420T011400
CREATED:20250626T070000Z
LAST-MODIFIED:20250925T231619Z
UID:10000048-1755799200-1755799200@events.ucsc.edu
SUMMARY:Regulatory Affairs and Medical Device Quality and Design – Fall Info Session
DESCRIPTION:Explore medical innovation with Kiran Gulati\nJoin medical device and biotech expert Kiran Gulati for a closer look at UCSC Silicon Valley’s Regulatory Affairs and Medical Device Quality and Design certificates. These programs equip you to navigate global regulations and bring safe\, effective products to market. \nBuild in-demand skills in a regulated field\nLearn FDA negotiation\, risk management\, and quality system standards like ISO 13485 and 14971. Whether you're aiming for RAC certification or advancing your career in biotech\, this session will highlight how these programs prepare you for success in the fast-moving healthcare industry. \nThis fall info session is sponsored by the Regulatory Affairs and Medical Device Programs.
URL:https://events.ucsc.edu/event/regulatory-affairs-and-medical-device-quality-and-design-fall-info-session/
LOCATION:CA
CATEGORIES:Lectures & Presentations,Meetings & Conferences,Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250822T130000
DTEND;TZID=America/Los_Angeles:20250822T130000
DTSTAMP:20260420T011400
CREATED:20250818T070000Z
LAST-MODIFIED:20250925T231432Z
UID:10000113-1755867600-1755867600@events.ucsc.edu
SUMMARY:Lei\, K. (CMPM) - Designing for Meaningful Large-Scale Online Communication\, Connection\, and Collective Insight
DESCRIPTION:Digital technologies have made large-scale online interaction a central part of how people communicate\, connect\, and work together. Yet scaling often comes at the cost of depth\, and interactions can become superficial and chaotic\, drifting away from the richer interactional contexts of small-scale or in-person settings that support trust and meaningful exchange\, and that make it possible for participants to respond to and build constructively on one another’s ideas. Although recent advances such as large language models have opened new possibilities for shaping online interaction\, there has been relatively little exploration of how to design interaction mechanisms that take advantage of large-scale engagement while fostering interactions that are engaged\, authentic\, connected\, and generative.\n    \nIn this dissertation\, I explore how large-scale online systems can be designed to support engaged and meaningful interaction at scale from three distinct angles: creating few-to-many conversation structures that enable broad participation while maintaining coherence and a high level of engagement; fostering authentic self-expression in ways that build connection; and designing mechanisms that allow participants to interpret and constructively build on one another’s contributions to generate collective insight. I begin by designing a chat-based interface that organizes conversations through multi-person conversational units\, enabling one or a few mentors to effectively mentor a large-group of students. I then examine how to design a gratitude-focused online community that supports authentic and positive expressions of gratitude\, cultivating positive cycles of reflection and connection. Finally\, I introduce a large language model–powered survey platform that blends qualitative depth\, quantitative structure\, and collaborative interaction\, enabling respondents to engage with and build on each other’s ideas while providing survey creators with richer and more interpretable results. My work demonstrates how technological affordances and large-scale participation can be combined to create interaction mechanisms that support the move from isolated contributions toward shared understanding\, offering unique forms of engagement that small-scale or in-person settings cannot provide. \nEvent Host: Kehua Lei\, PhD Candidate\, Computational Media \nAdvisor: David Lee
URL:https://events.ucsc.edu/event/lei-k-cmpm-designing-for-meaningful-large-scale-online-communication-connection-and-collective-insight/
LOCATION:CA
END:VEVENT
END:VCALENDAR