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DTSTART;TZID=America/Los_Angeles:20260722T110000
DTEND;TZID=America/Los_Angeles:20260722T130000
DTSTAMP:20260708T160702Z
CREATED:20260708T160702Z
LAST-MODIFIED:20260708T160702Z
UID:10015012-1784718000-1784725200@live-events-ucsc.pantheonsite.io
SUMMARY:Holmes\, J. (CM) - Towards a Multi-dimensional Model of User Load
DESCRIPTION:Games user researchers (GURs) use various methods to understand when a game is overloading its players. In games research where data-driven multimodal approaches are necessary to drive insights\, the currently available tools to measure user load are coarse\, one-dimensional\, and often aggregated. The more dominant instruments\, such as the Cognitive Load Scale (CLS) and the NASA-TLX\, rely on player reflections of mental effort\, primarily at the end of the playtest session\, to distinguish different cognitive load types. This makes it difficult to: (1) understand where specifically players are struggling and experiencing high load\, especially at the non-reflective subconscious level\, (2) identify where that load is primarily coming from (e.g.\, perceptual clutter or difficulty/skill imbalance)\, and (3) examine user overload at scale\, a crucial component of designing a game with large player bases. Telemetry is the behavioral record of what players are doing from moment to moment\, in varying degrees of granularity. Telemetry has served as a powerful tool to understand player behaviors at scale\, yet is rarely used to measure user load\, especially through a validated multidimensional framework. This dissertation proposes that behavioral signatures of specific load constructs are observable in game telemetry\, and a model built and validated on such telemetry can measure the distinct components of each load at the moment-to-moment granularity of individual play\, as opposed to aggregated magnitude. This dissertation consists of three parts: (1) Validation through construct manipulation and reference measurement. Specifically\, manipulating theoretically grounded load constructs and confirming that the proposed telemetry features respond as predicted\, relative to the established measurements collected alongside them (e.g.\, NASA-TLX\, pupillometry\, secondary-task). (2) Individual-level validation through rigorous longitudinal examination of the same players repeatedly across many sessions such that load constructs can be tracked at the within-person granularity. This is necessary to establish that the measure works for an individual player and not just for population averages. (3) Test the user load model by applying it to naturalistic game telemetry. Additionally\, this phase will entail the development of an insight-oriented measurement tool for GURs based on our validated user load model. The overarching contribution is a behavioral\, telemetry-based method for measuring multidimensional user load in games\, validated to measure load within each person (individual-level). This gives GURs a scalable tool and replicable process for detecting user load in commercial game telemetry. \nEvent Host: Jonattan Holmes\, Ph.D. Student\, Computational Media \nAdvisor: Magy Seif El-Nasr \nZoom: https://ucsc.zoom.us/j/98245962806?pwd=HnkwPMFSamQJFrE5aihbZbKDBbt4s9.1 \nPasscode: 347521
URL:https://live-events-ucsc.pantheonsite.io/event/holmes-j-cm-towards-a-multi-dimensional-model-of-user-load/
LOCATION:
CATEGORIES:Ph.D. Presentations
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260723T100000
DTEND;TZID=America/Los_Angeles:20260723T120000
DTSTAMP:20260715T162923Z
CREATED:20260715T162923Z
LAST-MODIFIED:20260715T162923Z
UID:10015089-1784800800-1784808000@live-events-ucsc.pantheonsite.io
SUMMARY:Chen\, X. (STAT) - Changepoint Detection and Clustering Methods for Multivariate Time Series and Attributed Networks
DESCRIPTION:Time series data with dependence arise across a wide range of scientific and engineering disciplines\, often presenting challenging inferential problems related to structural change and clustering. This Ph.D. proposal addresses several related problems in statistical inference for multivariate and network-indexed time series. First\, we develop a weighted multivariate $U$-statistic procedure for detecting a single changepoint in the mean of a multivariate stationary time series. The proposed framework accommodates short-range dependence\, encompasses classical CUSUM and Wilcoxon tests as special cases\, and admits a tractable limiting distribution after a pre-whitening transformation. Second\, we study nodal clustering in graphs with dynamic attributes through a decoder-only latent space framework that integrates temporal dynamics and structural information via a graph-fused LASSO regularization. An extension of this framework\, in which the neural network decoder is replaced by an autoregressive structure at each node\, is also introduced. Lastly\, a future research project is proposed on modeling and changepoint inference for Arctic sea ice coverage data\, whose marginal distribution is doubly inflated with point masses at zero and one. A latent Gaussian process transformation approach is outlined that accommodates this exotic marginal structure while permitting temporal and spatial autocorrelation\, trends\, and seasonal dynamics. In tandem\, these efforts aim to provide flexible and theoretically grounded tools for analyzing complex dependent data. \nEvent Host: Xi Chen\, Ph.D. Student\, Statistical Science \nAdvisor: Robert Lund \nZoom: https://ucsc.zoom.us/j/97760514185?pwd=ImfeI5uEdBvq9eoiFXnF5pecmwfVHd.1 \nPasscode: 333103
URL:https://live-events-ucsc.pantheonsite.io/event/chen-x-stat-changepoint-detection-and-clustering-methods-for-multivariate-time-series-and-attributed-networks/
LOCATION:
CATEGORIES:Ph.D. Presentations
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260723T120000
DTEND;TZID=America/Los_Angeles:20260723T140000
DTSTAMP:20260708T162027Z
CREATED:20260708T162027Z
LAST-MODIFIED:20260708T162027Z
UID:10015013-1784808000-1784815200@live-events-ucsc.pantheonsite.io
SUMMARY:Li\, J. (CM) - Detecting Failure to Adapt: Reading Self-Regulated Learning Breakdowns from Game Telemetry through Plan Recognition
DESCRIPTION:Three learners who fail the same level of an educational game the same number of times can be failing in three different ways\, and the difference determines what each should do next. Yet the measures a game’s logs are usually reduced to (completion time\, error counts\, mastery estimates) render the three identical. This proposal takes one breakdown as its object: failure-to-adapt\, the case where the game has repeatedly surfaced evidence that a learner’s current approach is failing and the learner’s approach shows no responsive change. The construct is grounded in Winne and Hadwin’s monitor-and-control model of self-regulated learning and defined at the level of the learner’s plan. To detect it\, a plan-recognition engine maintains a continuously updated probability estimate of which strategy the learner is executing across the whole trace; an episode is flagged when that estimate shows no evidence-responsive revision. Because behavior alone cannot settle what broke down\, flagged episodes are validated against learners’ own verbal reports\, coded blind\, and decomposed into monitoring failure\, control failure\, or control the trace cannot show. Three studies carry the work: detection and diagnosis on real telemetry from an educational game\, including a comparison against the analytics the field already runs; a formative study of what a facilitator (an instructor or teaching assistant running a class play session) must see to judge correctly which learners need attention; and a documented authoring case carrying the detection to a second game. The contribution is knowledge for game-based-learning researchers: a theory-grounded construct\, a validated way to detect it from play\, and the authoring knowledge to embed that detection in new games. \nEvent Host: Jiahong Li\, Ph.D. Student\, Computational Media \nAdvisor: Magy Seif El-Nasr \nZoom: https://ucsc.zoom.us/j/93238603235?pwd=zENRsu82HRj4JYKcMEn9MZibU8kC7F.1 \nPasscode: 835328
URL:https://live-events-ucsc.pantheonsite.io/event/li-j-cm-detecting-failure-to-adapt-reading-self-regulated-learning-breakdowns-from-game-telemetry-through-plan-recognition/
LOCATION:
CATEGORIES:Ph.D. Presentations
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DTSTART;TZID=America/Los_Angeles:20260724T140000
DTEND;TZID=America/Los_Angeles:20260724T160000
DTSTAMP:20260716T222234Z
CREATED:20260716T222234Z
LAST-MODIFIED:20260716T222234Z
UID:10015099-1784901600-1784908800@live-events-ucsc.pantheonsite.io
SUMMARY:Gholami\, K. (ECE) - Efficient Language Model Construction and Inference via Sparsity
DESCRIPTION:While large language models can match or exceed human performance\, they do so with memory and energy costs orders of magnitude greater than biological cognition. We investigate sparsity as a brain-inspired computational principle to address both. We first establish a framework for evaluating small language model construction methods\, using the next-token logit distribution as a behavioral fingerprint. Then\, we introduce a semi-structured correlation-aware weight sparsity (CWS) method that uses the full activation covariance to identify and prune correlated weights whose combined removal cost is lower than any individual score predicts. CWS\, improves perplexity over existing criteria up to 70% sparsity. To extend this gain to extreme sparsity\, we propose a hierarchical ADMM framework that optimizes pruning directly against cross-entropy and distillation loss\, first layer-wise for efficiency and then globally for cross-layer coordination. This research establishes brain-inspired principles as a foundation for efficient language models that remain accurate even under extreme compression. \nEvent Host: Kimia Gholami\, Ph.D. Student\, Electrical & Computer Engineering \nAdvisor: Jason Eshraghian \nZoom: https://ucsc.zoom.us/j/9827512398?pwd=SGpDWGtVVG81dkgyTHhjbG81dEVUZz09&omn=98349793611 \nPasscode: 8398
URL:https://live-events-ucsc.pantheonsite.io/event/gholami-k-ece-efficient-language-model-construction-and-inference-via-sparsity/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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