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DTSTART;TZID=America/Los_Angeles:20260413T080000
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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:20260423T153000
DTEND;TZID=America/Los_Angeles:20260423T173000
DTSTAMP:20260422T183246
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LAST-MODIFIED:20260401T183254Z
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SUMMARY:Pawl\, E. (STAT) - Flexible and Scalable Mixtures of Experts for Oceanographic Flow Cytometry Data
DESCRIPTION:Flow cytometry is a valuable technique in microbial research used to measure the optical properties of single-celled organisms at high throughput. Oceanographers often deploy flow cytometers on research cruises in order to study the characteristics of phytosynthetic microbes—called phytoplankton—in regions and times with diverse environmental conditions. Because cytometers cannot distinguish between subpopulations\, researchers typically cluster observations into subpopulations and subsequently analyze cluster characteristics. This two-stage workflow is often manual\, difficult to reproduce\, and fails to account for uncertainty in cluster assignments when relating subpopulation behavior to environmental conditions. To address these shortcomings\, statistical mixture models are gradually being introduced as alternatives to manual flow cytometry data analysis. However\, existing models either cannot use covariates or make restrictive assumptions about the relationships between cluster characteristics and covariates. Additionally\, they are designed to analyze individual cruises and consequently characterize local\, rather than global\, patterns in phytoplankton behavior. We propose to develop computationally efficient mixtures of experts which account for the complex dependency structures in oceanographic flow cytometry data. In this framework\, cells are probabilistically assigned to latent subpopulations\, while cluster-specific regressions relate each subpopulation’s optical properties and relative abundance to environmental conditions. Our first project develops a mixture of random weight neural network experts which can estimate arbitrary nonlinear regressions at low computational cost\, without a priori specification of functional forms. In the second project\, we develop a variational Bayesian mixture of experts which automatically selects variables without requiring cross-validation for hyperparameter selection. The final project incorporates spatial and temporal dependence\, allowing joint inference on data collected from multiple research cruises conducted at different locations and times. \nEvent Host: Ethan Pawl\, Ph.D. Student\, Statistical Science \nAdvisors: Sangwon Hyun & Paul Parker \nZoom- https://ucsc.zoom.us/j/96353239941?pwd=a4PJ94EMSD6D0SJ75S3WYzrPbYsBtn.1 \nPasscode- 244463
URL:https://events.ucsc.edu/event/pawl-e-stat-flexible-and-scalable-mixtures-of-experts-for-oceanographic-flow-cytometry-data/
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:20260424T130000
DTEND;TZID=America/Los_Angeles:20260424T150000
DTSTAMP:20260422T183246
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SUMMARY:Zheng\, Z. (STATS) - Semi-Supervised Statistical Learning for Oceanographic Data
DESCRIPTION:Oceanographic data\, generated by modern technologies that measure biological systems across time\, space\, and cell populations\, are often rich\, high-dimensional\, and highly heterogeneous. Such data provide valuable opportunities to study subcellular organization\, cellular heterogeneity\, and dynamic biological processes in marine environments. However\, because marine plankton systems remain relatively understudied and less well characterized than many model biological systems\, both data generation and labeling are particularly challenging. Limited domain knowledge and less mature laboratory protocols often produce noisy observations\, while reliable annotation requires substantial expert effort and is therefore difficult to obtain at scale.\nThis proposal develops statistical methodology for oceanographic data settings in which a small amount of expert-labeled data must be combined with a much larger collection of unlabeled or imperfectly processed data. A central goal is to incorporate limited scientific knowledge into statistical learning procedures to improve interpretability\, component identifiability\, and inferential reliability. In particular\, I develop semi-supervised statistical methods that explicitly quantify the information contributed by expert annotation.\nTo address this goal\, I study three related problems: semi-supervised functional clustering for subcellular spatial proteomics\, anchored semi-supervised mixture-of-experts models for flow cytometry\, and temporally structured latent-variable models that separate smooth trend and seasonal variation from scientific signals of interest. Together\, these projects aim to develop principled and interpretable methodology for partially labeled\, structured\, and high-dimensional oceanographic data\, with an emphasis on valid uncertainty quantification. \nEvent Host: Ziyue Zheng\, Ph.D. Student\, Statistical Science \nAdvisor: Sangwon Hyun \nZoom: https://ucsc.zoom.us/j/93229540289?pwd=8bsBOSBFmISlexmS4OWTmTZKp420u2.1
URL:https://events.ucsc.edu/event/zheng-z-stats-semi-supervised-statistical-learning-for-oceanographic-data/
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:20260506T100000
DTEND;TZID=America/Los_Angeles:20260506T120000
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SUMMARY:Wang\, Q. (STAT) - Modern Statistical Methods for Modeling Spatial and Temporal Processes
DESCRIPTION:Modern scientific studies increasingly rely on complex datasets exhibiting spatial and temporal dependence\, particularly in social\, environmental\, and climate applications. This dissertation develops statistical models and computational methods for analyzing such data\, with an emphasis on capturing dependence structures\, nonlinear dynamics\, and uncertainty quantification. \nA spatial deep learning framework is developed to extend classical geostatistical models by incorporating convolutional neural network architectures\, allowing for flexible modeling of complex and nonstationary spatial dependence The proposed approach preserves principled uncertainty quantification alongside improved predictive performance for large and heterogeneous spatial datasets. \nIn the temporal domain\, a Bayesian hierarchical echo state network model is introduced for count-valued time series\, providing a flexible alternative to traditional autoregressive approaches. By embedding reservoir computing within a hierarchical probabilistic framework\, the model accommodates nonlinear temporal dynamics while enabling coherent inference and uncertainty quantification\, which are typically absent in standard neural network approaches. \nAlongside these model-driven developments\, we conduct a data-driven analysis of Northern Hemisphere snow cover using weekly satellite-derived observations from 1972 to 2024. A spatio-temporal modeling framework is developed that combines a seasonal two-state Markov structure for temporal dynamics with a Besag–York–Mollié (BYM) formulation to capture spatial dependence\, allowing both trend and seasonal effects to vary across space. Covariates including temperature\, latitude\, and elevation are incorporated to explain observed patterns. The analysis reveals substantial spatial heterogeneity and pronounced seasonal structure\, including week-specific trends and a coherent wave-like pattern of snow cover changes across continents. \nTogether\, this thesis addresses key limitations of classical approaches to spatial and temporal data analysis\, which often rely on restrictive assumptions that limit their ability to capture complex dependence structures and nonlinear dynamics. By integrating modern machine learning techniques with statistical modeling and complementing these developments with data-driven scientific analysis\, this dissertation provides a flexible and principled framework for understanding complex spatio-temporal processes while maintaining uncertainty quantification. \n  \nEvent Host: Qi Wang\, Ph.D. Candidate\, Statistical Science  \nAdvisor: Paul Parker \nZoom: https://ucsc.zoom.us/j/97486222296?pwd=419R7C5I6gLbbB0eLqwMcSVQLTN7bA.1 \nPasscode: 766602
URL:https://events.ucsc.edu/event/wang-q-stat-modern-statistical-methods-for-modeling-spatial-and-temporal-processes/
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:20260511T080000
DTEND;TZID=America/Los_Angeles:20260511T100000
DTSTAMP:20260422T183246
CREATED:20260415T202034Z
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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
DTSTAMP:20260422T183246
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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:20260422T183246
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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:20260520T080000
DTEND;TZID=America/Los_Angeles:20260520T100000
DTSTAMP:20260422T183246
CREATED:20260422T160446Z
LAST-MODIFIED:20260422T160446Z
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SUMMARY:Maram\, S. (CM) - Scripture To Console: The Nexus between Religion and Digital Play
DESCRIPTION:Religion has historically been a profound force for global mobilization\, shaping geopolitics\, economies\, and geography. Similarly\, contemporary interactive media\, with video games at the forefront\, has moved beyond mere entertainment to become a powerful vehicle for communication\, narrative\, and inspiration\, reaching millions worldwide. This dissertation investigates the intersection of these two influential forces: religion and video games\, demonstrating the influence of religion on video games\, the influence of video games on religion\, and finally\, how these two powerful mobilization forces can come together to solve global challenges. \nFirst\, I examine the current landscape of religious representation in commercial video games (e.g.\, Assassin’s Creed\, SMITE). I analyze how key stakeholders i.e. players\, game designers\, and development studios\, interpret and engage with embedded religious elements\, drawing on existing critical reception and player discourse. This analysis identifies common narrative pitfalls and successful strategies for incorporating complex religious themes in digital spaces\, culminating in proposed design frameworks for sensitive and effective representation. \nBuilding on this foundational work\, the thesis culminates in defining and validating a new interaction paradigm where learning meets religion through play. This paradigm focuses on intentionally leveraging religious content i.e. specifically its rituals and narratives as mechanics in serious games to drive motivation and learning toward collective action. I validate this paradigm through a comprehensive case study focused on climate change\, arguably the most pressing issue of the modern era. This involves the design and empirical discussion of a serious game that incorporates specific religious mechanics\, ethics\, and narratives (e.g.\, stewardship\, ritual) to effectively communicate the severity of the climate crisis and motivate stakeholders toward a collective solution. \n  \nEvent Host: Sai Siddartha Maram\, Ph.D. Candidate\, Computational Media \nAdvisor: Magy Seif El-Nasr \nZoom: https://ucsc.zoom.us/j/91946426300?pwd=wxe1x3YCRsXrtcvOSy2kmfC9dZ3inW.1 \nPasscode: 558570
URL:https://events.ucsc.edu/event/maram-s-cm-scripture-to-console-the-nexus-between-religion-and-digital-play/
LOCATION:
CATEGORIES:Ph.D. Presentations
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