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DTSTAMP:20260417T053426
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SUMMARY:Weatherwax\, K. (CM) - LoFi to X and Y: Background Media Use as Colloquial Assistive Technology for Neurodivergent People
DESCRIPTION:Research in media psychology has often framed background media as a distraction that undermines performance. Such perspectives rely on narrow\, output-oriented definitions of success and overlook the emotional\, mental\, social\, and environmental needs that shape how people actually work. They also fail to account for neurodivergent experiences\, ignoring the diverse ways people engage with media in daily life.\n   \nThis dissertation uses a critical disability and neurodiversity lens to examine background media\, with a focus on LoFi as a commonly used exemplar\, as a form of colloquial assistive technology. Drawing on interviews and large-scale online discourse\, I show how LoFi is not primarily used to increase productivity\, but to manage affect\, sustain attention\, and reduce cognitive or sensory overload. Users describe it as a supportive presence—helping them transition into work\, recover from fatigue\, and feel accompanied in otherwise isolating contexts.\n   \nThese findings challenge dominant narratives about distraction and media use. Rather than being passively consumed\, background media is deliberately shaped and adopted as a source of support. This work rethinks what counts as assistive technology\, foregrounds the self-directed practices of neurodivergent people\, and offers design directions for systems that legitimize and extend such strategies. \n  \nEvent Host: Kevin Weatherwax\, Ph.D Candidate\, Computational Media  \nAdvisor: Kate Ringland
URL:https://events.ucsc.edu/event/weatherwax-k-cm-lofi-to-x-and-y-background-media-use-as-colloquial-assistive-technology-for-neurodivergent-people/
LOCATION:CA
CATEGORIES:Ph.D. Presentations
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DTSTART;TZID=America/Los_Angeles:20251022T120000
DTEND;TZID=America/Los_Angeles:20251022T130000
DTSTAMP:20260417T053426
CREATED:20251008T195221Z
LAST-MODIFIED:20251016T181905Z
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SUMMARY:Penumbra de la memoria: Brown Bag with Maya Scherr-Willson
DESCRIPTION:During this presentation\, Maya Scherr-Willson (PhD Student in the Film and Media Department) will show material and reflect on insights from a research trip that laid the groundwork for Penumbra de la memoria\, a feature documentary to be shot this summer. The project reunites eight women fifty years after they were held as political prisoners together during Argentina’s last military dictatorship to film an adaptation from memory of their prison-era performance of The House of Bernarda Alba by Federico García Lorca. The group\, engaged in collective work\, will be the protagonist of the film that chronicles the political memory that erupts through their creative process.
URL:https://events.ucsc.edu/event/penumbra-de-la-memoria-brown-bag-with-maya-scherr-willson/
LOCATION:Huerta Center Conference Room (Casa Latina)\, 641 Merrill Rd\, Santa Cruz\,\, CA\, 95064
CATEGORIES:Lectures & Presentations,Ph.D. Presentations
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DTSTART;TZID=America/Los_Angeles:20251028T100000
DTEND;TZID=America/Los_Angeles:20251028T120000
DTSTAMP:20260417T053426
CREATED:20251024T173428Z
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SUMMARY:Alatawi\, A. (ECE) - Learning-Based Channel Estimation for Next-Generation Wireless Communications
DESCRIPTION:Accurate Channel State Information (CSI) is critical for coherent detection\, equalization\, and adaptive resource allocation in modern wireless systems. Traditional estimators rely on stationary statistical models\, and many learning-based methods assume training and deployment conditions are matched. In practice\, these assumptions break down under user mobility and environmental dynamics\, leading to degraded performance. This proposal explores machine-learning approaches for channel estimation that address two complementary challenges. \nFirst\, we develop an adaptive deep neural network (ADNN) for single-input single-output links over slowly time-varying channels. The method converts readily available physical-layer feedback—cyclic redundancy check (CRC) and automatic repeat request (ARQ)—into reliable self-supervision. Specifically\, packets decoded without errors are re-estimated using least squares (LS) across all symbols to generate high-quality labels\, and the DNN weights are periodically updated online. This design eliminates the need for ground-truth labels at deployment and enables continual learning. Simulations show that the ADNN tracks distributional shifts and recovers near–linear minimum mean-square error (LMMSE) performance in both mean-square error (MSE) and symbol error rate (SER)\, whereas a fixed offline-trained DNN degrades as channel statistics change. \nSecond\, we propose a sequence-to-sequence LSTM estimator for orthogonal frequency-division multiplexing (OFDM). The model exploits both temporal and frequency correlation by taking LS pilot estimates from several previous OFDM blocks as input and reconstructing the full channel frequency response of the current block. Trained on realistic time-selective channels such as WINNER II\, the LSTM outperforms LS interpolation and recent super-resolution–based methods across a wide range of SNRs\, pilot densities\, and temporal window sizes. \nFinally\, the proposal outlines future research on semantic-aware channel estimation using CSI timeliness\, and enhanced sequence models with DNN-refined pilots\, whole-block inputs\, and efficient GRU architectures. \nEvent Host: Abdulaziz Alatawi\, Ph.D. Student\, Electrical & Computer Engineering \nAdvisor: Hamid Sadjadpour & Zouheir Rezki \nZoom- https://ucsc.zoom.us/j/94895993579?pwd=Bs1ppmjqFvNknefRAHoVGXPSXxdZ6i.1 \nPasscode- 884927
URL:https://events.ucsc.edu/event/alatawi-a-ece-learning-based-channel-estimation-for-next-generation-wireless-communications/
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CATEGORIES:Ph.D. Presentations
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