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DTSTART;TZID=America/Los_Angeles:20261021T130000
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DTSTAMP:20261020T165240Z
CREATED:20261020T165240Z
LAST-MODIFIED:20261020T165240Z
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SUMMARY:Zhang\, R. (CSE) - Towards next-generation infrastructure for agentic AI
DESCRIPTION:LLM workloads are shifting from chat to agents: one task becomes tens of model calls\, dispatched along a graph the harness holds and repeating the same instructions. This execution is structured and predictable\, yet the serving stack discards that structure at every boundary it crosses. I argue that agent infrastructure should treat execution semantics and state as first-class abstractions. My completed work recovers them between harness and engine: a policy-driven runtime layer and CacheSage\, which predicts when each agent returns\, cutting time to first token by up to 45%. Proposed work extends this across machines\, shared retrieval\, and reinforcement learning. \nEvent Host: Rui Zhang\, PhD Student\, Computer Science & Engineering  \nAdvisor: Liting Hu  \nZoom: https://ucsc.zoom.us/j/4646537931?pwd=aJHaERqBaST4DTj2SMJg22HgFeEjuN.1#success \n 
URL:https://live-events-ucsc.pantheonsite.io/event/zhang-r-cse-towards-next-generation-infrastructure-for-agentic-ai/
LOCATION:
CATEGORIES:Ph.D. Presentations
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DTSTART;TZID=America/Los_Angeles:20261103T100000
DTEND;TZID=America/Los_Angeles:20261103T110000
DTSTAMP:20261020T173621Z
CREATED:20261020T173621Z
LAST-MODIFIED:20261020T173621Z
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SUMMARY:Rondoni\, N. (AM) - Inverse Problems and Control of Nonlinear Systems with Applications in Biology
DESCRIPTION:Steering living systems toward a desired behavior is a central goal in synthetic and quantitative biology. Levers of control range from electric fields and optogenetic stimulation to engineered gene circuits\, chemical dosing\, and tuning the adhesive attraction and repulsion between cells. Biology makes it hard to know how much each lever should be pulled\, or even to discern the current state of the system. Dynamics are nonlinear\, and observations are noisy and indirect. Models are often incomplete\, as exact replication of dynamics requires an immense number of parameters. This dissertation solves inverse problems and develops control methods within this setting across three biological testbeds. Here we take steps toward more reliably engineering tissues to form patterns on demand\, cells to manufacture therapeutics to desired specifications\, and neural experiments to respond to activity in real time. \nIn the first chapter\, we invert the forward mapping between the firing of a neuron and the noisy calcium indicator traces actually recordable in high throughput imaging. This work utilizes model predictive control on a novel mechanistic set of ODEs\, competes with the state of the art\, and is the first of its kind to our knowledge able to provide real time inference of this type with no variable backtracking. While other methods may edit their answers based on future information\, ours operates with 60ms of lag\, then leaves predictions fixed. \nIn the second chapter\, we suppose direct control of the forward mapping\, a nonlinear model representing production of thyroxine by engineered cellular factories\, and ensure the measured output tracks a desired value even when the plant and observations are confounded by stochasticity. We use a radial basis function neural network for online function approximation\, and introduce numerous modifications to this classic architecture to empirically track setpoints stably even in the presence of immense delays in the forward model’s output. We show the median steady state error in terms of normalized root mean square error to be 12.5% or less across setpoints in a Monte Carlo study. \nIn the final chapter\, we again solve an inverse problem\, this time in spatial cellular patterning. Treating a black box agent based model as the forward map\, we recover attraction and repulsion rates that reproduce a target pattern up to rotation\, using an objective defined only on summary entropies of the pattern\, rather than on target cell positions or the true parameters. We show convergence from 100 initial layouts\, for two different target patterns. For a layout where convergence is marginal\, we show that converging and nonconverging initial rates have statistically distinct centroids\, then build a classifier that predicts which initializations are likely to succeed under this iterative intervention strategy. \nApplications such as engineered spatial patterns\, bioelectronic interventions\, and closed loop neural experiments each depend on three tasks. The first is deriving a forward model\, potentially black box or explicit in equations. The second task is computing the input that drives the biological system to a target\, while the third is recovering hidden inputs from what observations are actually available. More broadly\, this dissertation advances control of living systems that are only partially observed and partially understood. \nEvent Host: Nicholas Rondoni\, PhD Candidate\, Applied Mathematics \nAdvisor: Marcella Gomez  \nZoom: https://ucsc.zoom.us/j/97002422471?pwd=BarPJkyaLcJ6OUA1a2Za74GyrAb274.1 \nPasscode: 970 0242 2471
URL:https://live-events-ucsc.pantheonsite.io/event/rondoni-n-am-inverse-problems-and-control-of-nonlinear-systems-with-applications-in-biology/
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:20261123T080000
DTEND;TZID=America/Los_Angeles:20261123T090000
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SUMMARY:Bondar\, G. (AM) - Geometric and Graph Structured Optimal Transport for Learning and Control
DESCRIPTION:We motivate\, formulate\, and solve several structured optimal transportation (OT) problems for learning and control. The field of OT had its inception in 1781\, when Gaspard Monge sought the optimal map to transport mass from a given initial distribution to a desired terminal distribution. Both OT and its entropy-regularized variant\, the Schrödinger bridge problem (SBP)\, have catalyzed significant developments across mathematics. More recently\, theoretical and algorithmic developments in OT and SBP have found myriad applications in stochastic optimal control and generative AI. \nThe connecting theme across the problems studied in this dissertation is the presence of structure which can be exploited in their solution — for our work\, this structure arises from geometry underlying a solution space or from a graph capturing a correlation structure between distributional data. Our original contributions begin with automated verification and discovery of OT regularity\, followed by stochastic learning and control for resource allocation and scheduling in multi-core real-time systems\, where the problem has a natural graph structure. We also formulate and solve the problem of finding an optimal graph structure in a distribution-valued dataset where none is present. \nWhile graph-structured OT is a natural fit for data-driven stochastic learning problems\, the knowledge of system dynamics can impose geometric structure to the problem. In this dissertation\, we establish the equivalence between the stochastic optimal control and path space formulations of SBP over a compact connected Lie group. Additionally\, we develop\, analyze\, and validate a novel numerical solution scheme for the SBP with control-affine dynamics. Finally\, we investigate the application of Gromov-Wasserstein (GW) OT — a variant of the problem allowing for transport between incomparable spaces — for spatial proteomics. We show throughout our work how careful considerations of the underlying geometry and graph-structures can enable bespoke solutions for practical learning and control problems through optimal transport. \nEvent Host: Georgiy Bondar\, Ph.D. Candidate\, Applied Mathematics  \nAdvisor: Abhishek Halder \nZoom: https://ucsc.zoom.us/j/96361886043?pwd=U73TbafhzI7kgS7MYAFko3YEPVIxTb.1 \nPasscode: 564490
URL:https://live-events-ucsc.pantheonsite.io/event/bondar-g-am-geometric-and-graph-structured-optimal-transport-for-learning-and-control/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20261202T100000
DTEND;TZID=America/Los_Angeles:20261202T110000
DTSTAMP:20261020T164621Z
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SUMMARY:Kobenova\, A. (CM) - Inclusive Joyful Learning: Designing and Evaluating Playful Immersive Systems for Neurodivergent Youth
DESCRIPTION:Neurodivergent children and young adults are frequently subjected to therapeutic and educational interventions anchored in “behavior deficits” inherited from the Medical Model of Disability. These paradigms prioritize normative performance\, forcing disabled individuals into cognitive and emotional “masking” at the expense of their autonomy and well-being. Resisting these ableist connotations\, this dissertation advances a philosophy of inclusive joyful learning\, grounded in the Affirmative Model of Disability and Flourishing Human-Computer Interaction (HCI)\, which position play\, self-directed exploration\, and bodily agency as acts of resistance against coercive instructional design. \nThis work introduces\, implements\, and evaluates the Joyful Extended Reality (XR) framework\, a set of inclusive design principles centered on neurodiverse populations. Operating through Research through Design (RtD) and adapted Participatory Design (PD) approaches\, the dissertation evaluates three immersive systems combining child-centered and expert evaluations. First\, a room-scale motion-capture implementation of the “Joyful XR” framework investigates how spatial mixed reality supports cognitive engagement and motor imitation through physical-virtual object mapping\, evaluated through a study with 21 autistic children from special education classrooms. Second\, “The Road to Acceptance”\, a narrative VR environment for emotional literacy and grief processing\, was evaluated through expert workshops with 12 mental health professionals\, including behavior analysts and bereavement support workers. Third\, “Coastal Resilience”\, an immersive game for environmental problem-solving and community safety\, was evaluated with 12 caregivers\, special education teachers\, and psychosocial professionals. \nAcross the three systems\, the findings demonstrate that instructional structures\, such as Discrete Trial Teaching and bereavement therapy\, can be successfully decoupled from adult coercion and embedded into self-directed\, collaborative game mechanics. The empirical evaluations reveal that these systems shift the facilitator’s role in educational and therapy settings. The participating children engaged more freely when a familiar adult provided relational support\, and practitioners emphasized the need for human-guided debriefing for emotionally intense concepts and for longitudinal transfer. This dissertation contributes: (1) the theoretical and structural Joyful XR design framework; (2) three documented\, playable immersive systems; and (3) transferable design requirements for multisensory facilitation\, relational mediation\, and non-coercive play in accessible extended reality environments. \nEvent Host: Amina Kobenova\, Ph.D. Candidate\, Computational Media  \nAdvisor: Sri Kurniawan \nZoom: https://ucsc.zoom.us/j/94504075063?pwd=PifJ9OXtqvFlZTYbdJECwJfGhPQi8s.1 \nPasscode: 831245
URL:https://live-events-ucsc.pantheonsite.io/event/kobenova-a-cm-inclusive-joyful-learning-designing-and-evaluating-playful-immersive-systems-for-neurodivergent-youth/
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CATEGORIES:Ph.D. Presentations
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