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DTSTART;TZID=America/Los_Angeles:20260723T100000
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DTSTAMP:20260715T162923Z
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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/
CATEGORIES:Ph.D. Presentations
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DTSTART;TZID=America/Los_Angeles:20260723T120000
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LAST-MODIFIED:20260708T162027Z
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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/
CATEGORIES:Ph.D. Presentations
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