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SUMMARY:Creative Interventions (CI) Series
DESCRIPTION:Creative Interventions (CI) is a community colloquium in contemporary creativity and creative practices that addresses the interconnected work of artists\, designers\, activists\, and knowledge workers—and the intrinsic and transformative capacity of that work to cultivate a just society. \nThe CI Speaker Series raises questions of import to contemporary creative workers in media and technology:\n– How do creative workers address their most challenging problems?\n– How does creative labor intersect with other forms of labor to nurture the world views and cultural practices of our democracy? \nThe Creative Interventions (CI) Series is co-sponsored by the Arts Division’s Creative Technologies program and Porter College at UC Santa Cruz.\n—\nADMISSION\n– Free and open to UCSC affiliates.\n– All events in the series are presented online with registration required.\n– Refer to the individual event listings for more information and a link to register.\n—\nFULL SCHEDULE OF EVENTS\n– Wed.\, Oct 15\, 4:00 p.m.: “Expensive-Sounding Sounds” with Catherine Provenzano\n– Wed.\, Oct 29\, 4:00 p.m.: “Asymptote: Computation\, Disillusion\, and Enchantment” with Nora Khan\n– Additional event dates to be announced on the Creative Interventions events page here\n—\nThis program is open to all UC Santa Cruz affiliates consistent with state and federal law.
URL:https://events.ucsc.edu/event/ci-series/2025-10-15/
CATEGORIES:Exhibits,Lectures & Presentations
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DTSTART;TZID=America/Los_Angeles:20251020T104000
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DTSTAMP:20260619T120755
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SUMMARY:ECE 290 Seminar: Biohybrid Electronics Using Extracellular Electron Transfer
DESCRIPTION:Presenter: Ben Keitz\, Associate Professor\, University of Texas at Austin \nDescription: Qualities exhibited by living systems\, including self-regulation\, self-healing\, morphology control\, and environmental responsiveness\, are highly attractive for sensing and computing applications. However\, it has been challenging to develop robust and programmable interfaces between living systems and electronic components. Addressing this challenge\, our lab employs techniques from microbiology\, synthetic biology\, and metabolic engineering to control extracellular electron transfer (EET)\, a form of microbial respiration in which extracellular metals and metal oxides are used as terminal electron acceptors. Using the model electroactive bacterium Shewanella oneidensis\, we coopt EET to link cellular metabolism and protein expression to microelectronic device behavior. Specifically\, we show that S. oneidensis can interface with organic electrochemical transistors (OECTs) through specific electron transfer machinery. The inclusion of living cells endows single transistors with complex logic\, short-term plasticity\, and other unique properties. We also establish that S. oneidensis can interact with both p-type and n-type conducting polymers to further control transistor performance. Ultimately\, our work demonstrates how unique forms of bacterial respiration can be leveraged to merge the advantages of living and traditional computation. \nBio: Benjamin (Keith) Keitz received his PhD in Chemistry from the California Institute of Technology and completed his postdoctoral training at the University of California\, Berkeley. He is a native of Austin\, TX and is currently an Associate Professor and the Frank A. Liddell Jr. Fellow in Chemical Engineering in the McKetta Department of Chemical Engineering at the University of Texas at Austin. Research in the Keitz lab focuses on the engineering of electroactive bacteria and the applications of extracellular electron transfer in biocatalysis\, materials synthesis\, synthetic biology\, and biosensing. His work has received several awards including an NIH Maximizing Investigators’ Research Award\, an NSF CAREER Award\, and an Air Force Office of Scientific Research Young Investigator Award. \nHosted by: Professor Marco Rolandi\, ECE Department \nZoom Link: https://ucsc.zoom.us/j/97975378707?pwd=ljcgaCfhMmhZ88Vt5dqQUBVQRjehOx.1 \nRoom: E2-192
URL:https://events.ucsc.edu/event/ece-290-seminar-biohybrid-electronics-using-extracellular-electron-transfer/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations
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DTSTART;TZID=America/Los_Angeles:20251020T160000
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SUMMARY:AM Seminar: Sampling-Based Adaptive Rank Integrators for Multi-scale Kinetic Models.
DESCRIPTION:Presenter: Professor Jingmei Qiu\, University of Delaware \n  \nDescription: In this talk\, we introduce a sampling-based semi-Lagrangian adaptive rank (SLAR) method\, which leverages a cross approximation strategy—also known as CUR or pseudo-skeleton decomposition—to efficiently represent low-rank structures in kinetic solutions. The method dynamically adapts the rank of the solution while ensuring numerical stability through singular value truncation and mass-conservative projections. By combining the advantages of semi-Lagrangian integration with low-rank approximations\, SLAR enables significantly larger time steps compared to conventional methods and is extended to nonlinear systems such as the Vlasov-Poisson equations using a Runge-Kutta exponential integrator. Building on this framework\, we further develop the SLAR method for the multi-scale BGK equation\, introducing an asymptotically accurate approach that eliminates the need for low-rank decompositions of the local Maxwellian in the collision operator. To enforce conservation of mass\, momentum\, and energy\, we propose a novel locally macroscopic conservative (LoMaC) technique\, which discretizes the macroscopic system using high-order DIRK methods. Additionally\, a dynamic closure strategy is employed to self-consistently adjust macroscopic moments\, enabling robust simulations across both kinetic and hydrodynamic regimes\, even in the presence of shocks and discontinuities. We validate our method through extensive benchmark tests on linear advection\, unto 3D3V nonlinear Vlasov-Poisson\, and multi-scale kinetic problems\, demonstrating its accuracy\, stability\, and computational efficiency. The Sampling-Based Adaptive Rank framework offers a promising pathway for overcoming the curse of dimensionality in high-dimensional multi-scale kinetic problems. \n  \nBio: Dr. Jingmei Qiu is a Unidel Professor in the Department of Mathematical Sciences at the University of Delaware. Her research focuses on the design\, analysis\, and application of high-order structure-preserving computational algorithms for complex systems characterized by multi-scale\, multi-physics\, and high-dimensional features. Dr. Qiu’s work includes developing low-rank tensor approximations for high-dimensional\, time-dependent problems with structure preservation\, as well as Eulerian-Lagrangian high-order numerical methods for fluid and kinetic applications. \n  \nHosted by: Professor Julie Simons \n 
URL:https://events.ucsc.edu/event/am-seminar-sampling-based-adaptive-rank-integrators-for-multi-scale-kinetic-models/
LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations
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DTSTART;TZID=America/Los_Angeles:20251020T160000
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SUMMARY:Statistics Seminar: Distance-to-set regularization for inference under constraints
DESCRIPTION:Presenter: Jason Xu\, Associate Professor\, Department of Biostatistics\, UCLA \nDescription: We consider a penalty framework based on regularizing the squared distance to set-based constraints for several core statistical tasks. These distance-to-set penalties provide a simple and flexible way to cast constrained optimization problems in more tractable unconstrained forms. We will see that they often avoid drawbacks that arise from popular alternatives such as shrinkage methods. We discuss a general strategy for eliciting effective algorithms in this framework using majorization-minimization (MM)\, the general principle behind EM that transfers difficult problems onto a sequence of more manageable subproblems. We showcase new progress on classical problems including sparse covariance estimation using this approach\, and discuss connections to Bayesian inference. In particular\, analogous ideas lead to constraint relaxation and generalized profile likelihood to include optimization subproblems\, leading to methods that are amenable to gradient-based posterior computation. \nBio: Jason Xu is an Associate Professor in the Department of Biostatistics at the University of California Los Angeles. Before joining the faculty at UCLA\, he was a faculty member in the Department of Statistical Science at Duke University. Xu’s research program focuses on stochastic modeling and computational challenges in dynamic\, dependent\, and missing data settings\, and he contributes tools at the interface of optimization and Bayesian approaches \nHosted by: Professor Paul Parker
URL:https://events.ucsc.edu/event/statistics-seminar-distance-to-set-regularization-for-inference-under-constraints/
CATEGORIES:Lectures & Presentations
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