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SUMMARY:UCSC Summer Academy on Artificial Intelligence for High School Students
DESCRIPTION:UCSC’s Summer Academy on Artificial Intelligence is a four-week\, in-person program for talented and motivated high school students who are interested in exploring artificial intelligence (AI) in a university setting. Hosted at the UCSC Silicon Valley Campus\, the program offers an immersive learning experience that combines foundational AI concepts with hands-on\, research-inspired work. Students learn from UCSC professors and active PhD researchers\, gaining advanced problem-solving skills\, research-oriented thinking\, and a deeper understanding of how AI is applied in cutting-edge innovations. \nApplication Deadline: April 24\, 2026
URL:https://events.ucsc.edu/event/ucsc-summer-academy-on-artificial-intelligence-for-high-school-students/
LOCATION:3175 Bowers Avenue Santa Clara\, CA 95054\, 3175 Bowers Avenue\, Santa Clara\, CA\, 95054\, United States
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DTSTART;TZID=America/Los_Angeles:20260724T120000
DTEND;TZID=America/Los_Angeles:20260724T170000
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SUMMARY:Spring Exhibitions at the Institute of the Arts and Sciences
DESCRIPTION:Visit the IAS\, UCSC’s premier art galleries\, for our spring exhibitions. On view April 10–August 16\, 2026 are three diverse and interdisciplinary shows: Libia Posada: Everything is Going Right\, the first US solo exhibition by the Colombia-based artist and medical doctor; Gina Athena Ulysse: A Redwoods Rasanblaj\, a site-specific and immersive exploration of the Haitian kreyol conception of rasanblaj; and Ronaldo V. Wilson: There Are No Words\, But Melodies\, a mixed-media exhibition emerging at the intersections of Black poetics\, performance\, and visual art. \nThe IAS Galleries are open Wednesday-Sunday\, 12 pm – 5 pm. Admission is free to the public. \nLibia Posada: Everything is Going Right\nLibia Posada’s first solo exhibition in the United States features installations\, sculptures\, and drawings meticulously constructed from surgical instruments\, gauze bandages\, crutches\, used books\, and domestic picture frames. The new and existing works in the exhibition powerfully stitch together the personal\, social\, and political disorders and afflictions that currently trouble the world\, from the wars that resonate across the globe to the violences of aging in US prisons.  \nGina Athena Ulysse: A Redwoods Rasanblaj: Origins & Disentanglements\nThe internationally-lauded work of humanities professor Gina Athena Ulysse is on view as a premier Faculty Spotlight Exhibition. The site-specific installation\, produced in community from things collected\, found\, purchased and donated\, centers on the Haitian concept of rasanblaj\, a form of assembly and collage that transcends the formal use of materials to draw together people\, spirits\, and ideas.  \nRonaldo V. Wilson: There Are No Words\, But Melodies\nCollage is both a material practice and a structural interrogation in the Faculty Spotlight Exhibition artworks by literature professor Ronaldo V. Wilson. In video\, painting\, and installation\, layers and folds conceal and reveal\, delving into the experience\, both bodily and emotive\, of living in times of violence.  \n 
URL:https://events.ucsc.edu/event/spring-exhibitions-at-the-institute-of-the-arts-and-sciences/2026-07-24/
LOCATION:Institute of the Arts and Sciences\, 100 Panetta Ave\, Santa Cruz\, United States
CATEGORIES:Exhibits
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DTSTART;TZID=America/Los_Angeles:20260724T140000
DTEND;TZID=America/Los_Angeles:20260724T160000
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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://events.ucsc.edu/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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SUMMARY:Fontana\, J. (STAT) - When We're Always Wrong: Scalable Variable Selection in M-Open Settings
DESCRIPTION:A ubiquitous task in statistical practice is that of variable selection\, identifying which of a large set of features are the relevant ones. As data sets with a large number of observations have become increasingly common\, new theoretical and computational challenges for model selection have emerged. We consider the variable selection problem for linear models in the M-open setting\, where the data generating process is outside the model space. We focus on the novel problem of “model superinduction”\, which refers to the tendency of model selection procedures to select larger models at an exponential rate as the sample size grows\, resulting in overparametrized models which collapse model interpretability and induce severe computational difficulties. For a set of popular frequentist information criteria and the Bayesian case of mixtures of g-priors\, we prove that when comparing nested models\, the larger model will always be asymptotically selected. We seek to minimize this effect for large n while preserving variable selection consistency.We propose utilizing a mixture of g-priors\, where the hyper-prior on g has hyper-parameters chosen to result in a slowly diminishing rate of prior influence on the posterior\, which favors simpler models while preserving consistency. We also propose a model space prior which induces stronger model complexity penalization for large sample sizes. The posterior model probabilities under our prior choices further provide an alternative information criterion that is resistant to the effects of model superinduction. \nNext\, we extend our results to other classes of popular variable selection priors\, the family of spike and slab priors\, the non-local priors\, and selection procedures that correspond to posterior modes such as the LASSO. We show that these procedures are all afflicted with model superinduction\, except for the continuous spike and slab priors when a Student-t distribution is used for both the spike and the slab. \nFinally\, we address existing bottlenecks in the computation efficacy of spike and slab based variable selection. We demonstrate that Gibbs samplers scale poorly to large sample sizes\, and the proposed alternatives in the literature result in posterior surrogates that are afflicted with superinduction. Instead\, we propose a search strategy based off easy to compute approximations of the posterior model probabilities. We show this procedure\, Fast Approximate Stochastic Search (FASS)\, coupled with post-hoc inference on parameters via either a Block-Variational Bayes approach or an Expectation Propagation approach\, results in competitive performance. We demonstrate the aforementioned phenomena\, and the efficacy of our proposed solutions\, via synthetic data examples and case studies using albedo data from GOES satellites and LCA application data. \nEvent Host: Jacob Fontana\, Ph.D. Candidate\, Statistical Science \nAdvisor: Bruno Sansó \nZoom: https://ucsc.zoom.us/j/99965109575?pwd=uGkOwWM3Rl3zP66aL1RecoOfB8Yat0.1 \nPasscode: 879019
URL:https://events.ucsc.edu/event/fontana-j-stat-when-were-always-wrong-scalable-variable-selection-in-m-open-settings/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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