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DTSTART;TZID=America/Los_Angeles:20260911T150000
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DTSTAMP:20260908T164853Z
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SUMMARY:Valderrama\, S. (CSE) - Closing Data Gaps for Cybersecurity AI Training and Evaluation
DESCRIPTION:Artificial intelligence and machine learning models depend on large and diverse datasets to learn patterns across different situations and generalize to cases they have not seen before. In cybersecurity\, suitable data is difficult to find for two main reasons: first\, sensitive information limits what organizations can share; second\, public datasets often lack the coverage or context in specific use cases. My research addresses this problem by developing methods that synthetically create and curate data\, where we propose data-generation algorithms that provide mechanisms to generate data to close these gaps. \nSo far\, we have started this research by developing algorithms to curate data for specific cybersecurity needs. We collect and label a collection of 64\,132 command blocks from different data sources trying to describe the security behavior they represent. Measuring this collection reveals important differences in coverage and provides a basis for directing command generation toward these rare behaviors. We then develop a generation and validation process that fills these gaps\, creating new examples that are correct\, relevant\, and diverse. Finally\, we automate the process of creating applications that could serve different cybersecurity purposes\, which together with a set of instructions\, a way of grading them\, and reference solutions became a tasks\, a valuable training unit for machine learning models. \nThese contributions show that we can reliably guide synthetic data generation that produces useful results. As future work\, we will extend this approach from individual applications to complex environments involving multiple systems. Executions in these environments will produce records of activity linked to known actions and their outcomes\, whether benign or malicious\, enabling analysis and modeling of adversarial behavior. The broader goal is to make cybersecurity data generation more targeted\, automatic\, and useful for artificial intelligence research. \nEvent Host: Sergio Valderrama\, Ph.D. Student\, Computer Science & Engineering  \nAdvisor: Alvaro Cardenas  \nZoom: https://ucsc.zoom.us/j/92072651510?pwd=UB55FkjUfMi0vssfcqIbYsPWjDr9xw.1 \nPasscode: 026956 \n 
URL:https://events.ucsc.edu/event/valderrama-s-cse-closing-data-gaps-for-cybersecurity-ai-training-and-evaluation/
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:20260914T100000
DTEND;TZID=America/Los_Angeles:20260914T120000
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SUMMARY:Mirzanezhad\, H. (EE) - Van der Waals materials-based memory devices for neuromorphic computing
DESCRIPTION:The separation of memory and processing in conventional computer architectures leads to significant energy consumption associated with data transfer\, known as the von Neumann bottleneck. Two-dimensional (2D) materials provide a promising platform for developing nanoscale memory devices for in-memory and neuromorphic computing because of their atomic-scale thickness\, lack of dangling bonds\, and ability to form van der Waals structures. In this work\, bilayer MoS₂ is investigated as an active material for memory devices based on two different switching mechanisms. The first is a resistive memory based on a vertical junction that exhibits a reproducible hysteresis loop and can operate as a memristor. Possible mechanisms contributing to the observed hysteresis include ion migration\, charge trapping\, polarization switching\, or a combination of these mechanisms. The second is a ferroelectric memory based on the established ferroelectricity of parallel-stacked bilayer MoS₂\, where broken inversion symmetry gives rise to switchable out-of-plane polarization. This work therefore follows two parallel directions: a memory device based on the resistive effect and a memory device based on the ferroelectric effect\, with the longer-term goal of developing 2D material-based memory devices for neuromorphic computing. \nEvent Host: Hamid Mirzanezhad\, Ph.D. Student\, Electrical Engineering  \nAdvisor: Aiming Yan
URL:https://events.ucsc.edu/event/mirzanezhad-h-ee-van-der-waals-materials-based-memory-devices-for-neuromorphic-computing/
LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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DTSTART;TZID=America/Los_Angeles:20260915T100000
DTEND;TZID=America/Los_Angeles:20260915T113000
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SUMMARY:Tornoe\, S. (ECE) - Improving the UV optical response and suppressing surface plasmon resonance in aluminum and silver base telescope mirrors
DESCRIPTION:Aluminum and silver are excellent metal-bases for astronomical telescopes designed for broadband spectral measurements. In particular silver excels in the violet to infrared spectral range range with over 95% reflectance. In turn aluminum excels deep ultra-violet to infrared with over 90% reflectance across the bulk of its spectral range. In astronomical telescope optics\, each percent reflectance is extremely valuable\, meaning each mirror type has place. However\, both metal-bases suffer from unwanted oxidation necessitating protective coatings. Unfortunately\, even a few nanometers of traditional coatings like AlOx\, can completely eliminate the far and deep UV response. These dielectric protective coatings can also induce surface plasmon resonance (SPR) in the mirrors. SPR occurs when light propagates in a dielectric protective coating along a metal-dielectric interface exciting electrons in the metal as it propagates. The plasmonic response in the metal-base alters how light reflects from the mirror creating an oscillation in the UV spectral range. This research will focus on improving the UV response and diminishing the detrimental effects of SPR for two cases: aluminum mirrors and silver mirrors. \nEvent Host: Soren Tornoe\, Ph.D. Student\, Electrical & Computer Engineering  \nAdvisor: Nobuhiko Kobayashi \nZoom: https://ucsc.zoom.us/j/93650451146?pwd=IFEbCOKZQAyDdoYMO5b19JqGZbX07m.1 \nPasscode: 657292
URL:https://events.ucsc.edu/event/tornoe-s-ece-improving-the-uv-optical-response-and-suppressing-surface-plasmon-resonance-in-aluminum-and-silver-base-telescope-mirrors/
LOCATION:2300 Delaware Avenue\, 2300 Delaware Ave\, Santa Cruz\, CA\, 95060
CATEGORIES:Ph.D. Presentations
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DTSTART;TZID=America/Los_Angeles:20260925T130000
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SUMMARY:Mastoras\, M. (BMEB) - Polishing genome assemblies and leveraging their improved quality to study centromere variation
DESCRIPTION:A complete and accurate genome reconstruction serves as the foundation for studying an organism’s biology and the mechanisms underlying disease. It is particularly critical for reference genomes\, which provide a universal coordinate system for downstream genomic analysis. Errors or missing sequences in a reference genome create bias in all of the studies built on top of them. The Human Pangenome Reference Consortium (HPRC) seeks to address this bias by transitioning the field to a pangenome reference\, a graph based collection of many genome assemblies\, providing a better representation of variation in the human population. Removing errors in the HPRC assemblies is critical to ensure the pangenome serves as a robust standard for genomic variant discovery. In the first part of my thesis\, I improve the base level accuracy of the HPRC release 2 assemblies (HPRC2) with a machine learning model for assembly polishing called DeepPolisher. Next\, I make additional contributions to reference-based genomic analysis by polishing reference genomes of other model organisms\, and helping to develop a new method for de-novo assembly and variant calling from a single-flow cell nanopore sequencing protocol. Finally\, I take advantage of the highly accurate\, near complete assemblies from HPRC2 that I improved with DeepPolisher to study a region only recently made accessible to genomics analysis: the human centromere. I apply the tool Centrolign\, the first ever multiple-sequence-aligner for centromeres to the HPRC2 assemblies\, establishing precise estimates of mutation rates and spatial variation patterns across centromeric arrays. \nEvent Host: Mira Mastoras\, Ph.D. Candidate\, Biomolecular Engineering & Bioinformatics \nAdvisor: Benedict Paten \nZoom: https://ucsc.zoom.us/j/98960011053?pwd=mFKCwtSIhvT5FvEURbE85lRbbMvzuo.1 \nPasscode: 118488
URL:https://events.ucsc.edu/event/mastoras-m-bmeb-polishing-genome-assemblies-and-leveraging-their-improved-quality-to-study-centromere-variation/
LOCATION:Physical Sciences Building\, Physical Sciences Building\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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