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SUMMARY:2026 Right Livelihood International Conference
DESCRIPTION:The Right Livelihood International Conference is a five-week global conference exploring how education can strengthen democracy\, collective intelligence\, and just futures. Bringing together Right Livelihood Laureates\, students\, faculty\, and community partners across continents\, the conference combines asynchronous learning with participatory dialogue and collaborative action. Rather than advocating specific outcomes\, the conference positions education as a democratic practice and the Right Livelihood College as a steward of dialogue\, student voice\, and long-term institutional learning. \nRegistration is free and open to the public. Sign up to receive conference updates\, session links\, and participation opportunities.
URL:https://events.ucsc.edu/event/2026-right-livelihood-international-conference/
LOCATION:
CATEGORIES:Film Screening,Lectures & Presentations,Meetings & Conferences,Ph.D. Presentations,Seminars,Social Gathering,Training,Undergraduate,Workshop
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DTSTART;TZID=America/Los_Angeles:20260511T080000
DTEND;TZID=America/Los_Angeles:20260511T100000
DTSTAMP:20260429T212638
CREATED:20260415T202034Z
LAST-MODIFIED:20260415T202226Z
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SUMMARY:Johns\, M. (CMPM) - Playing Together in a Co-Designed Future: Building Resilience Through Community-Centered Gameful Design
DESCRIPTION:Complex societal problems (e.g. wicked problems) such as those brought on by climate change can be addressed through a combination of Research through Design (RtD)\, co-design\, and Serious Games (SG) by inviting affected communities to take part in developing iterative\, experimental solutions and exploring their potential impact. In the course of my research\, I have proposed a framework for design research that engages with wicked problems at the community level through gameful design\, which is based on existing literature in HCI drawing from RtD\, co-design\, and SG. Core elements of the framework include supporting diverse perspectives\, interdisciplinarity\, working with local knowledge\, and aligning different concepts with specific gameful elements to support meaningful interactions and discussion. \nIn a specific case study\, my proposed framework is applied to create a gameful intervention to support wildfire resilience in communities at the Wildland-Urban Interface (WUI) which face particular risks from natural hazards. Through a community co-design process\, open discussions have identified consistent pain-points and challenges faced by communities who have experienced wildfires or evacuations\, e.g. traffic congestion in areas with one road in and out\, while also pinpointing differences in their approaches based on local conditions\, such as whether or not to encourage people to evacuate on foot. Through an RtD approach\, important ideas have emerged about how serious games can be utilized in this space. For example\, a common approach to serious game design is to align the win condition of a game with specific learning outcomes or desired changes. However\, when working with wicked problems there are often complex social dilemmas and conflicting values without clear right answers. In these cases there is a need to map dilemmas and trade-offs to game mechanics rather than mapping learning outcomes to win conditions. \nThe gameful intervention developed through this dissertation integrates local knowledge from communities alongside expert knowledge from disciplines including fire science\, social science\, engineering\, and design. The resulting artifact leverages a minigame design to map different concepts to specific and approachable game mechanics. Through universal and inclusive design practices\, the games can be accessible to a broad audience including both children and older adults. The cooperative multiplayer aspects of the games encourage discussion and collaborative play between friends\, community members\, and particularly intergenerational play within families. In addition to contributing RtD reflections as a result of the project\, I also measured change in resilience at the individual and community levels after deployment of the games through qualitative and quantitative methods. This dissertation contributes to knowledge about what game design has to offer to addressing wicked problems\, with specific approaches to better serve communities facing complex risks such as those associated with a rapidly changing climate. \nEvent Host: MJ Johns\, Ph.D. Candidate\, Computational Media  \nAdvisor: Katherine Isbister \nZoom: https://ucsc.zoom.us/j/7959349044?pwd=cVYraU9yMUVwVFhYWHp6T05OZm5rZz09
URL:https://events.ucsc.edu/event/johns-m-cmpm-playing-together-in-a-co-designed-future-building-resilience-through-community-centered-gameful-design/
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:20260512T100000
DTEND;TZID=America/Los_Angeles:20260512T120000
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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. In the end\, we revisit the proportional fairness (PF) algorithm for $L_p$-norms of flow time. By reinterpreting the previous potential function and the corresponding Fisher market\, we show that PF is competitive. \n  \nEvent Host: Qingyun Chen\, Ph.D. Candidate\, Computer Science & Engineering  \nAdvisor: Sungjin Im \nZoom: https://ucsc.zoom.us/j/92628493495?pwd=iJq8YwarrYyofPLF4AmZpwzsZnLyvt.1 \n 
URL:https://events.ucsc.edu/event/chen-q-cse-new-approximation-and-online-algorithms-using-novel-combinatorial-structures-2/
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:20260513T120000
DTEND;TZID=America/Los_Angeles:20260513T133000
DTSTAMP:20260429T212638
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SUMMARY:Ehrlich\, D. (CM) - Designing Open Microscopy Tools for Neuroscience Research
DESCRIPTION:Advances in microscopy have transformed our understanding of biological systems\,\nyet the high cost and limited accessibility of commercial imaging platforms continue to re-\nstrict their use in many research settings. This thesis presents the design and development of\nopen hardware microscopy tools for neuroscience research\, with a focus on integrating user-\ncentered design principles into the instrument development process. Two primary methods\nare introduced: augmenting existing microscopes with new imaging capabilities\, and the cre-\nation of modular microscopes that are designed for continuous\, long-term live-cell imaging.\nBoth platforms are built around open hardware principles\, prioritizing low cost\, modularity\, and\nadaptability to the practical needs of working researchers. Alongside the hardware contribu-\ntions\, this thesis presents user experience research methods for examining how neuroscience\nresearchers interact with novel microscopy technologies\, providing a methodological frame-\nwork for human-centered scientific instrument design. These contributions demonstrate that\npairing hardware development with user-centered design methodologies produces microscopy\ntools that are both technically capable and meaningfully accessible to both laboratories and\nindividuals studying neuroscience\, education\, and other fields. \n  \nEvent Host: Drew Ehrlich\, Ph.D. Candidate\, Computational Media  \nAdvisor: Sri Kurniawan \nZoom: https://ucsc.zoom.us/j/2491739056?pwd=UCt3MmZmL1hwdXcvVGNNaGRQM0lDQT09
URL:https://events.ucsc.edu/event/ehrlich-d-cm-designing-open-microscopy-tools-for-neuroscience-research/
LOCATION:
CATEGORIES:Ph.D. Presentations
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DTSTART;TZID=America/Los_Angeles:20260514T090000
DTEND;TZID=America/Los_Angeles:20260514T110000
DTSTAMP:20260429T212638
CREATED:20260427T162713Z
LAST-MODIFIED:20260427T162920Z
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SUMMARY:Shadmon\, R. (CS) - Proximal Byzantine Agreement
DESCRIPTION:Research on fault-tolerance protocols for approximate Byzantine agreement\n(ABA) has largely focused on ensuring that distributed processes remain\nconsistent despite fewer than 1/3 faulty processes. Yet in many\nreal systems\, consistency is only useful when it enables processes to\nmake accurate decisions from replicated\, noisy\, and potentially\nadversarially corrupted data relative to an ideal fault-free baseline.\nThis limitation is increasingly important in edge applications such as\nautonomous vehicles\, drone networks\, smart cities\, manufacturing\, and\nsensor-based systems\, where agreement directly drives downstream\nactions. At the same time\, many existing ABA protocols impose\nimpractical requirements\, such as replica counts that grow with data\ndimensionality or prior knowledge of the maximum distance between values\nproposed by each process. \nWe introduce Stochastic Byzantine Agreement (SBA)\, a new problem\nformulation in which the goal is to estimate an output from n replicated\nvalues consisting of n-f nonfaulty outputs generated by an\nunderlying stochastic process and f arbitrarily chosen\nByzantine outputs. We then present Proximal Byzantine Agreement\n(PBA)\, a stochastic agreement protocol that solves SBA by enabling\nconsumers to infer the most likely ideal output conditioned on the\noutputs they receive. In addition\, PBA provides a region\nguarantee that\, as we prove\, always contains the corresponding\nfault-free stochastic estimate of the true value. \nWe describe the design of PBA\, formalize its guarantees\, and evaluate\nits accuracy against existing techniques using stochastic simulations\nacross symmetric and asymmetric distributions and multiple system\nconfigurations. We also evaluate runtime overhead and performance in a\nfollow-the-leader drone network simulator and in a Java implementation on\nRaspberry Pis using a real-world adaptive cruise control dataset. Our\nresults show that PBA performs competitively across all evaluated\nsettings and especially well under simulated Byzantine attack. Most\nnotably\, PBA maintains stable accuracy as dimensionality increases\,\noutperforming methods that require up to 10x more replicas}\nand incur up to 10x greater computation time per agreement\ndecision. \nEvent Host: Roy Shadmon\, Ph.D. Candidate\, Computer Science  \nAdvisor: Owen Arden \nZoom: https://ucsc.zoom.us/j/98390167664?pwd=DwkNuUSRaZRKXYb7pQbDYXgf7HFFPg.1 \nPasscode: pba
URL:https://events.ucsc.edu/event/shadmon-r-cs-proximal-byzantine-agreement/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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