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DTSTART;TZID=America/Los_Angeles:20260713T100000
DTEND;TZID=America/Los_Angeles:20260713T120000
DTSTAMP:20260707T160215Z
CREATED:20260707T160215Z
LAST-MODIFIED:20260707T160215Z
UID:10015010-1783936800-1783944000@live-events-ucsc.pantheonsite.io
SUMMARY:Scott\, J. (CSE) - Mechanistic Specialization Does Not Guarantee Performance: Evidence from Dual AttentionTransformers
DESCRIPTION:Dual Attention Transformers (DATs) extend decoder-only Transformers with a dedicated relational-attention stream\, making them a natural architecture for abstract identity rules such asABA and ABB. Surprisingly\, we find that comparably sized GPT-2 models outperform DATs on these tasks. We investigate this gap with two complementary mechanistic analyses. First\, causal mediation analysis shows that DATs exhibit stronger evidence of hypothesized symbolic mechanisms: symbol abstraction\, symbol induction\, and retrieval\, than GPT-2. Second\, a routing analysis shows why this specialization does not translate into better behavior: DATs make more wrong-copy errors\, can attend to the correct source token while still predicting the wrong token\, and show weak direct contribution from relational attention to the correct-versus-wrong outputmargin. Ablating positive-routing heads hurts performance\, while amplifying those headsimproves DAT more than matched controls. These results show that explicit relational attentioncan shape internal organization without guaranteeing task success. For identity-rule tasks\, performance depends not only on whether relational information is represented\, but whether it is routed to the final output position in a form that affects the next-token prediction. Because pretrained DAT and GPT-2 models differ in training data\, tokenizer\, and other implementation details\, these findings should be interpreted as evidence about the mechanisms used by existing models rather than as a definitive architectural comparison. Follow-up experiments will address these confounders through controlled training comparisons that match data\, scale\, and evaluation conditions across architectures. \nEvent Host: Jonathan Scott\, Ph.D. Student\, Computer Science & Engineering \nAdvisor: Leilani Gilpin \nZoom: https://ucsc.zoom.us/j/95404396322?pwd=0e0AegKSxhcFDDKrn08muHcqfHs6WW.1 \nPasscode: 985103
URL:https://live-events-ucsc.pantheonsite.io/event/scott-j-cse-mechanistic-specialization-does-not-guarantee-performance-evidence-from-dual-attentiontransformers/
CATEGORIES:Ph.D. Presentations
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LOCATION:
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DTSTART;TZID=America/Los_Angeles:20260713T160000
DTEND;TZID=America/Los_Angeles:20260713T170000
DTSTAMP:20260708T155209Z
CREATED:20260708T155209Z
LAST-MODIFIED:20260708T155209Z
UID:10015011-1783958400-1783962000@live-events-ucsc.pantheonsite.io
SUMMARY:Kembay\, A. (ECE) - Sparse and Continual Foundations for Adaptive General Intelligence
DESCRIPTION:While the human brain learns continually\, mastering new tasks without forgetting\nthe old and adapting to unfamiliar ones from context alone\, modern neural networks\nstill lack both. To bridge the gap between biological adaptivity and modern AI\, we\nhave established foundational work on sparsity as a computational principle at three\nlevels of neural computation\, through salient feature masking that distills only the most\ninformative knowledge from a teacher\, quantized spiking neural networks whose sparse\nactivations mitigate catastrophic forgetting by updating weights only when new learn-\ning requires it\, and complex-pole value-path dynamics that give Transformer attention\na resonant\, positionally selective memory. Addressing the remaining bottleneck\, that\nthese sparse structures are fixed in advance rather than adapted to the task at hand\,\nwe propose a research roadmap centered on in-context meta-learning with sparse atten-\ntion priors\, enabling models to ‘learn to be sparse’ by inferring task-relevant structure\nfrom context alone\, without any weight update. Taken together\, this research seeks\nto unify brain-inspired sparsity with continual and in-context learning as a foundation\nfor adaptive general intelligence. \nEvent Host: Assel Kembay\, Ph.D. Student\, Electrical & Computer Engineering \nAdvisor: Jason Eshraghian \nZoom: https://ucsc.zoom.us/j/92202931005?pwd=peVIc4e03fUPwFqlGa6yWx6ZlL33lI.1 \nPasscode: 742766
URL:https://live-events-ucsc.pantheonsite.io/event/kembay-a-ece-sparse-and-continual-foundations-for-adaptive-general-intelligence/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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DTSTART;TZID=America/Los_Angeles:20260717T113000
DTEND;TZID=America/Los_Angeles:20260717T133000
DTSTAMP:20260715T163613Z
CREATED:20260715T163517Z
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SUMMARY:Calicchio\, A. (BMEB) - Comparison of long-read sequencing and analysis methods for transcriptome analysis
DESCRIPTION:Alternative splicing\, the process generating different RNA isoforms from a single gene\, is considered one of the main factors driving increased organism complexity in eukaryotes. Variations in isoform and gene expression produce the functional differences that give rise to different cell types and\, in some cases\, result in disease. Long-read RNA sequencing has transformed our ability to characterize isoforms\, since single reads can span full-length transcripts\, but limitations still prevent our identification of all the isoforms in the human transcriptome. Our research proposes to improve both the library preparation and computational analysis steps of the isoform identification process.\nTo do so\, we are updating the isoform identification and quantification tool IG28 (previously called Mandalorion) so that it can analyse both bulk and single-cell long-read sequencing data and. By pairing our analysis with single-cell clustering in Seurat\, we can generate transcriptomes for hundreds of thousands of single cells\, for individual cell types\, and for bulk datasets containing hundreds of millions of reads\, providing a scalable approach to identify isoforms in the largest and most recent datasets.\nFurthermore\, since long reads can carry both the variants defining an allele of origin and the full isoform structure\, we plan to extend IG28 to perform allele-specific transcript usage analysis. We plan to include accurate statistical tests in this module by using beta-binomial and Dirichlet-multinomial models that account for overdispersion\, to provide a tested and integrated pipeline for isoform allelic assignment.\nFinally\, recognizing that isoform detection depends on the quality\, length\, and throughput of the input data\, we are improving library preparation and benchmarking sequencing technologies. We are refining the R2C2 protocol coupled with size selection to overcome the current circularization limit for fragments beyond 6 kb\, and we are generating matched datasets to compare R2C2 to the Kinnex library preparation method\, and ONT against PacBio HiFi sequencing\, to determine which approaches produce the most accurate and longest reads for isoform identification.\nTogether\, these advances will provide a competitive pipeline\, from cDNA preparation to isoform identification and annotation\, enabling accurate isoform annotations that can lead to a deeper understanding of cell differentiation and disease etiology. \nEvent Host: Alessandro Calicchio\, Ph.D. Student\, Biomolecular Engineering & Bioinformatics \nAdvisor: Christopher Vollmers \nZoom: https://ucsc.zoom.us/j/92704819548?pwd=PUqQpq0Soandz8E5DIPCXFdvnFaf00.1 \nPasscode: 760165
URL:https://live-events-ucsc.pantheonsite.io/event/calicchio-a-bmeb-comparison-of-long-read-sequencing-and-analysis-methods-for-transcriptome-analysis/
LOCATION:Biomedical Sciences Building\, 575 McLaughlin Drive
CATEGORIES:Ph.D. Presentations
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