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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
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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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SUMMARY:AM Seminar: Column Subset Selection: Theory\, Structure\, and Algorithms
DESCRIPTION:Presenter: Anil Damle\, Associate Professor\, Cornell University \nDescription: The column subset selection problem is a classical topic in numerical linear algebra\, with renewed interest driven by applications in computational quantum chemistry\, integral equations\, model reduction\, and model compression in machine learning. This talk surveys recent advances that clarify how structural properties of a matrix influence the performance of column selection algorithms. We focus on structure-aware and randomized methods\, highlighting both theoretical guarantees and practical algorithmic consequences. \nAbout the speaker: Anil Damle is an associate professor of computer science at Cornell University. His research focuses on the development and analysis of robust and efficient algorithms in applied and computational mathematics that exploit structure coming from underlying physical or statisical models. He interfaces with a broad range of application areas\, and his work is inherently interdisciplinary—with the ultimate goal of developing algorithms that are usable for practitioners. He received his PhD from Stanford University in computational and mathematical engineering (ICME)\, and his MS in applied mathematics and BS in applied mathematics and computer engineering from the University of Colorado\, Boulder. \nThis seminar is hosted by Applied Mathematics.
URL:https://events.ucsc.edu/event/am-seminar-column-subset-selection-theory-structure-and-algorithms/
LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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SUMMARY:Statistics Seminar: Learning under Constraints and Extremes: Methods and Applications in Energy Systems
DESCRIPTION:Presenter: Yu Zhang\, Associate Professor\, ECE Department of UC\, Santa Cruz \nDescription: Modern cyber-physical systems present statistical learning problems that deviate significantly from standard i.i.d. supervised settings. In particular\, two challenges frequently arise: (i) learning under hard structural constraints\, and (ii) learning under severe distributional imbalance and rare events. In this talk\, I present two case studies from energy systems that illustrate these challenges and motivate new learning paradigms. First\, I consider the problem of approximating the solution map of the AC optimal power flow (AC-OPF)\, a nonlinear and nonconvex optimization problem governing power grid operations. Rather than relying solely on labeled optimal solutions\, we develop both unsupervised and semi-supervised physics-informed learning frameworks that incorporate equality constraints directly into the training objective via augmented Lagrangian formulations and implicit gradient estimation. These approaches enable data-efficient learning while maintaining physical feasibility\, and can be interpreted as constrained function estimation where physical laws provide structural supervision. Second\, I discuss short-term power outage forecasting under extreme weather conditions\, where the data exhibit zero inflation\, heavy tails\, and strong temporal dependence. We propose a two-stage modeling framework that separates event occurrence and magnitude\, combining calibrated classification with Tweedie-based regression to better capture rare but high-impact events. Together\, these examples highlight a unifying theme: modern applications often require learning methods that effectively integrate domain structure while remaining robust to challenging data characteristics such as sparsity and extreme events. I conclude with a discussion of broader implications for scalable learning\, uncertainty handling\, and decision-making in complex systems. \nAbout the speaker: Dr. Yu Zhang is an Associate Professor in the Department of Electrical and Computer Engineering at the University of California\, Santa Cruz. He received his Ph.D. in Electrical and Computer Engineering from the University of Minnesota\, followed by postdoctoral appointments at the University of California\, Berkeley and the Lawrence Berkeley National Laboratory. Dr. Zhang’s research advances the resilience\, efficiency\, and sustainability of modern electric power systems through innovations in AI-driven optimization\, machine learning\, and dynamic decision-making. His work develops physics-aware learning methods\, stochastic and robust optimization techniques\, and cyber-physical coordination frameworks to support reliable grid operations under uncertainty. Recent projects include learning-augmented outage forecasting\, planning for weather-driven grid hardening\, and integrating large flexible loads such as data centers into market and operational strategies. Dr. Zhang has been recognized with multiple awards\, including the 2025 Outstanding Young Investigator Award from the Energy Systems Division of the Institute of Industrial and Systems Engineers (IISE)\, the 2021 Early Career Best Paper Award from the INFORMS Energy\, Natural Resources\, and the Environment (ENRE) Section\, and the 2019 Hellman Fellowship. \nThis seminar is hosted by Professor Allen Kei. \n 
URL:https://events.ucsc.edu/event/statistics-seminar-learning-under-constraints-and-extremes/
LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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