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DTSTART;TZID=America/Los_Angeles:20251124T093000
DTEND;TZID=America/Los_Angeles:20251124T113000
DTSTAMP:20260416T171559
CREATED:20251112T181924Z
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SUMMARY:Chen\, Q. (CSE) - New Approximation and Online Algorithms using Novel Combinatorial Structures
DESCRIPTION:Most optimization problems face the challenge of computing an optimum solution requiring superpolynomial time. In particular\, they are classified as NP-hard problems that have no polynomial-time algorithm to date. Instead\, computer scientists turn to find an approximate solution and create numerous elegant algorithms. However\, in the modern era\, computational environments have changed drastically\, and we are not able to afford to design new algorithms for each new problem via repeated trial and error. Therefore\, systematic ways to understand the possibilities and limitations of these problems are desired. This dissertation studies several central combinatorial optimization problems\, focusing on understanding the key structural obstacles and developing unified frameworks. Mainly\, we study two types of combinatorial optimization problems:\n(1) Scheduling. The problem is associated with limited resources\, and our target is to find an allocation method to complete all jobs over time that minimizes the overall budget cost.\n(2) Network Design. Different from scheduling problems. In this problem\, we aim to find a minimum-cost topological network that supports routing for demanding communications. \nOur first work is focused on a group-to-group survivable network design problem that generalizes the classic point-to-point network to support routing between any pair of subsets of nodes. Previous research stops at limited faults\, and the difficulty comes from the way to compress the graph into a tree. We propose a new framework via capacitated tree embeddings against arbitrary faults in the network\, which gives the first polylogarithmic approximation algorithm. Further\, this framework captures nearly all the recent models proposed in the area. \nIn contrast to the offline optimization problems mentioned above\, online algorithms are natural adaptations that have been found in tremendous real applications. In online algorithms\, the algorithm wants to compete against arbitrary uncertainty\, which means the instance is unknown at first and revealed over time. We study various scheduling problems and focus on some important metrics – average flow time\, which measures the average time a job stays in the system from its arrival to completion. Real-world demands give online scheduling problems enormously different settings. Computer scientists need to repeat errors and trials to find a provably good solution. We find the key required combinatorial property is supermodularity for the residual objective\, which measures the average completion time for all alive jobs assuming they have the same arrival time. Further\, we relate supermodularity with gross-substitute/linear-substitute (GS/LS)\, which is a well-studied definition in economics. Finally\, we propose a meta-algorithm that solves all captured problems in one shot. \nEvent Host: Qingyun Chen\, Ph.D. Student\, Computer Science and Engineering \nAdvisor: Sungjin Im \nZoom-  https://ucsc.zoom.us/j/94376536164?pwd=cPloEcyKuQg1C9reIbuh6rejrOaRfR.1
URL:https://events.ucsc.edu/event/chen-q-cse-new-approximation-and-online-algorithms-using-novel-combinatorial-structures/
LOCATION:
CATEGORIES:Ph.D. Presentations
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DTSTART;TZID=America/Los_Angeles:20251124T104000
DTEND;TZID=America/Los_Angeles:20251124T114500
DTSTAMP:20260416T171559
CREATED:20251117T231136Z
LAST-MODIFIED:20251117T231136Z
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SUMMARY:ECE 290 Seminar: Fundamental Nanopower Analog Circuits
DESCRIPTION:Presenter: Joey Sankman\, Analog/Power Designer\, Analog Devices \nDescription: With the rising interest in edge computing\, and the addition of AI/ML functionality\, nanopower circuits are in great demand to reduce the quiescent power consumption of remote sensors. In this tutorial\, fundamental building blocks for nanopower circuits will be covered\, including startup-less low-voltage references\, low-frequency clocks\, and LDO regulators. Attendees can expect a deep dive into fundamental and practical analog techniques to design nanopower systems. \nBio: Joey Sankman received the B.S. degree from the University of Arizona\, Tucson\, AZ\, and Ph.D. degree from the University of Texas at Dallas\, TX in electrical engineering in 2010 and 2014\, respectively. At the University of Texas at Dallas\, his research included energy harvesting circuits and systems as well as high-performance switch mode power converters. He is currently an analog/power designer at Analog Devices\, Principal Member of Technical Staff\, working on automotive PMICs. Previously\, he was an Analog R&D Engineer working on audio amplifiers\, ultra-low power circuits\, and radhard gate drivers at Kilby Labs\, TI\, Dallas\, TX. He was the recipient of the U.S. National Science Foundation Graduate Research Fellowship and the 2011 Texas Instruments/Semiconductor Research Corporation Graduate Fellowship. He has authored or co-authored 20 publications in various IEEE journals and conferences. He currently serves on the IEEE ISSCC power subcommittee. \nHosted by: Professor Soumya Bose\, ECE Department \nZoom Link: https://ucsc.zoom.us/j/97975378707?pwd=ljcgaCfhMmhZ88Vt5dqQUBVQRjehOx.1
URL:https://events.ucsc.edu/event/ece-290-seminar-fundamental-nanopower-analog-circuits/
LOCATION:CA
CATEGORIES:Lectures & Presentations,Seminars
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DTSTART;TZID=America/Los_Angeles:20251124T160000
DTEND;TZID=America/Los_Angeles:20251124T170000
DTSTAMP:20260416T171559
CREATED:20251105T172921Z
LAST-MODIFIED:20251105T172921Z
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SUMMARY:AM Seminar: Linear Stochastic Emulators of the Ocean Circulation based on Balanced Truncation: A Caution\, perhaps\, for Machine Learning?
DESCRIPTION:Presenter: Professor Andy Moore\, UCSC Ocean Sciences \nDescription: Linear inverse models have enjoyed considerable popularity in the geosciences\, particularly in the arena of climate research and climate prediction\, for several decades as a straightforward approach to dimension reduction and streamlining computational efficiency. The most common approach is to truncate the system by retaining the leading Empirical Orthogonal Functions (EOFs) which represent the left singular vectors of the transition matrix. While singular value decomposition is the best low rank approximation of the transition matrix\, ignoring information contained in the right singular vectors\, as is commonly done in linear inverse models\, has consequences for the dynamics that approximate the system. Dimension reduction based on balanced truncation simultaneously preserves information from the right and left singular vectors. This talk will review some of these ideas and present examples from the ocean. Since EOF decomposition is quite commonly used for dimension reduction in some machine learning approaches\, there may be some lessons here for the machine learning community to consider. \nBio: Professor at UCSC since 2016. \nHosted by: Professor Julie Simons
URL:https://events.ucsc.edu/event/am-seminar-linear-stochastic-emulators-of-the-ocean-circulation-based-on-balanced-truncation-a-caution-perhaps-for-machine-learning/
LOCATION:CA
CATEGORIES:Lectures & Presentations,Seminars
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DTSTART;TZID=America/Los_Angeles:20251125T134000
DTEND;TZID=America/Los_Angeles:20251125T150000
DTSTAMP:20260416T171559
CREATED:20251108T002503Z
LAST-MODIFIED:20251120T174503Z
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SUMMARY:Macroeconomics & International Finance Seminar Series Presents: Helen Popper
DESCRIPTION:Macroeconomics and International Finance Seminar\nDate: Tuesday\, November 25\, 2025\nTime: 1:40-3:00 p.m.\nLocation: E2-499\n\n \n\nSpeaker: Helen Popper\nTitle: Professor of Economics\nAffiliation: Santa Clara University \nHost: Galina Hale\n \nSeminar title:  Artificial Intelligence and Macroeconomic Dynamics: Growth\, Pricing\, and Distribution\n \nABSTRACT:  This paper builds a simple general equilibrium model in which an AI producer is a monopolist who both learns by doing and uses AI recursively as an input. These mechanisms link today’s scale to tomorrow’s costs\, so pricing is dynamic: the firm sets a price below the static monopoly benchmark to expand capacity and speed learning. Final goods are produced by monopolistic competitors with constant returns to scale each period. We first use Cobb–Douglas technologies to solve for a generalized balanced growth path that pins down the condition for stable\, nonexplosive growth. On this path\, AI output grows faster than final output\, the relative price of AI falls persistently\, real wages rise with overall output\, and the specialized–to–nonspecialized wage ratio is flat. We then analyze CES versions of both sectors and derive a closed form effective demand elasticity for AI that combines input substitution in production with final-goods market substitution across varieties. Finally\, simulations link adoption and distribution to elasticities\, and they allow us to explore the dynamics. When final-goods inputs are complements\, adoption is learning-first and capital-light before scaling; when they are substitutes\, adoption is scale-first and the two-phase pattern attenuates. On the distribution side\, the specialized–to–nonspecialized wage premium is lowest with complements and rises with substitutes. Greater substitutability in AI production amplifies these patterns without changing their sign.
URL:https://events.ucsc.edu/event/macroeconomics-international-finance-seminar-series-presents-helen-popper/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Seminars
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DTSTART;TZID=America/Los_Angeles:20251130T100000
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SUMMARY:Dried Wreath Workshop
DESCRIPTION:Learn how to create beautiful\, long-lasting dried flower holiday wreaths with instructor Beth Benjamin. She will demonstrate the mechanics of putting everything together and will have a couple examples to guide your inspiration. Personal artistic style is highly encouraged! You’ll be able to choose from a wide selection of dried materials from the UCSC Farm and other sources. With care\, your wreath will last for years. Light refreshments will be served and the atmosphere promises to be jovial\, creative and social. \n\n\n\n\nRegister
URL:https://events.ucsc.edu/event/dried-wreath-workshop/
LOCATION:Hay Barn\, 94 Ranch View Road\, Santa Cruz\, CA\, 95064\, United States
CATEGORIES:Lectures & Presentations
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