BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Events - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Events
X-ORIGINAL-URL:https://events.ucsc.edu
X-WR-CALDESC:Events for Events
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/Los_Angeles
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20240310T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20241103T090000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20250309T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20251102T090000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20260308T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20261101T090000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250819T100000
DTEND;TZID=America/Los_Angeles:20250819T100000
DTSTAMP:20260427T170015
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
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:20250819T120000
DTEND;TZID=America/Los_Angeles:20250819T120000
DTSTAMP:20260427T170015
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/
CATEGORIES:Lectures & Presentations,Meetings & Conferences,Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250819T130000
DTEND;TZID=America/Los_Angeles:20250819T130000
DTSTAMP:20260427T170015
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
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Jack Baskin Engineering Baskin Engineering 1156 High Street Santa Cruz CA 95064;X-APPLE-RADIUS=500;X-TITLE=Baskin Engineering 1156 High Street:geo:-122.0632371,37.000369
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250819T140000
DTEND;TZID=America/Los_Angeles:20250819T140000
DTSTAMP:20260427T170015
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:20260427T170015
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
END:VCALENDAR