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DTSTART;TZID=America/Los_Angeles:20261102T160000
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DTSTAMP:20261001T175836Z
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SUMMARY:AM Seminar: Data Analysis Workflow from Low-Code Tools to Generative and Agentic AI
DESCRIPTION:Presenter: Reza Fazel-Rezai\, Senior Science and Education Application Engineer\, MathWorks \nDescription: Please join us to learn how to accelerate data analysis using a workflow that spans low-code tools\, Generative AI\, and Agentic AI. Through live demonstrations and practical examples\, you’ll see how to import\, explore\, visualize\, model\, and process data using interactive tools that require minimal coding. The session will demonstrate how these tools can automatically generate reproducible MATLAB code\, how Generative AI can assist with code creation\, explanation\, and troubleshooting\, and how Agentic AI workflows can help plan\, execute code\, validate results\, diagnose issues\, and iteratively refine outcomes. Highlights: – Build a data analysis workflow using low-code and interactive tools. – Use Generative AI to support code generation\, explanation\, and troubleshooting. – Explore how Agentic AI can plan analysis steps\, run code\, test results\, diagnose errors\, and iterate toward reliable outcomes. \nAbout the speaker: Dr. Reza Fazel-Rezai\, with a Ph.D. and MS in Biomedical Engineering and a BS in Electrical Engineering\, brings over two decades of experience in both industry and academia. As a senior research scientist\, research team manager\, and the founding Director and tenured full Professor of Biomedical Engineering\, he has extensive expertise and background in the field. Dr. Fazel-Rezai has authored more than 200 scientific publications\, edited and published seven books\, and pursued diverse research interests in biomedical signal and image processing\, particularly through machine learning and deep learning methods. Passionate about leveraging and sharing his skills to help others achieve their goals\, he currently serves as an ABET PEV for Biomedical Engineering\, works part-time as an instructor at UC San Diego\, and is a full-time Senior Science and Education Application Engineer at MathWorks. \nThis seminar is hosted by Professor Pascale Garaud.
URL:https://live-events-ucsc.pantheonsite.io/event/am-seminar-data-analysis-workflow-from-low-code-tools-to-generative-and-agentic-ai/
LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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DTSTART;TZID=America/Los_Angeles:20261103T100000
DTEND;TZID=America/Los_Angeles:20261103T110000
DTSTAMP:20261020T173621Z
CREATED: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:20261109T160000
DTEND;TZID=America/Los_Angeles:20261109T170000
DTSTAMP:20261007T210443Z
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SUMMARY:AM Seminar: Enhanced Resolution Imaging in Strongly Scattering Media
DESCRIPTION:Presenter: Chrysoula Tsogka\, Professor\, Applied Mathematics\, UC Merced \nDescription: In a strongly scattering medium\, waves undergo multiple scattering that is usually seen as an obstacle to imaging. Time-reversal experiments show the opposite: waves re-emitted into the medium refocus on their source more sharply than in a homogeneous medium\, because scattering makes the array appear larger than it is. This effect\, known as super-resolution\, is not available to conventional imaging. Since the medium through which the waves travelled is unknown\, conventional methods back-propagate the data in an approximation of the true background. In this talk I will show how super-resolution can be achieved in imaging when abundant array data are available. The key idea is to learn the Green’s functions of the unknown random medium directly from the data using sparse dictionary learning. The Green’s functions are recovered as unordered columns of the sensing matrix. To associate each column with its location in the imaging window\, we build a graph from cross-correlations of the recovered columns and apply multidimensional scaling to the resulting proxy distances. The learned Green’s functions are then used to form images by back-propagation or by $\ell_2$ and $\ell_1$ methods\, with resolution beyond the homogeneous medium limit. I will also discuss recent extensions: a new dictionary learning algorithm that starts from a random initialization\, imaging on unstructured grids\, and imaging through changing random media. Joint work with M. Moscoso\, A. Novikov\, G. Papanicolaou\, A. Christie\, M. Leibovich and J. Zheng. \nAbout the speaker: Chrysoula Tsogka is a Professor of Applied Mathematics at the University of California\, Merced\, where she also serves as Chair of the Applied Mathematics Graduate Group. She received her Ph.D. in Applied Mathematics from the University Paris IX Dauphine and was a Postdoctoral Fellow at Stanford University. Before joining UC Merced in 2019\, she was a tenured CNRS researcher at LMA in Marseille\, an Assistant Professor at the University of Chicago\, and a Professor at the University of Crete. Her research focuses on numerical methods for direct and inverse wave propagation problems and on imaging in complex media. Her recent work includes quantitative synthetic aperture radar (SAR) imaging for the detection and classification of buried landmines\, and data-driven methods\, such as dictionary learning\, for super-resolution imaging in strongly scattering media. She serves on the editorial boards of Inverse Problems\, the Journal of Computational Physics and the Journal of Mathematical Imaging and Vision.
URL:https://live-events-ucsc.pantheonsite.io/event/am-seminar-enhanced-resolution-imaging-in-strongly-scattering-media/
LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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DTSTART;TZID=America/Los_Angeles:20261109T160000
DTEND;TZID=America/Los_Angeles:20261109T170000
DTSTAMP:20261021T170847Z
CREATED:20261021T170847Z
LAST-MODIFIED:20261021T170847Z
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SUMMARY:SciCAM M.S. Virtual Information Session
DESCRIPTION:Join the UC Santa Cruz Baskin School of Engineering for a virtual information session on our Scientific Computing and Applied Mathematics (SciCAM) M.S. program. Discover our curriculum\, research opportunities\, application requirements\, and career paths for graduates. A live Q&A session will follow the presentation. \nRegistration is required.
URL:https://live-events-ucsc.pantheonsite.io/event/scicam-m-s-virtual-information-session/
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LOCATION:https://live-events-ucsc.pantheonsite.io/event/scicam-m-s-virtual-information-session/
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20261116T160000
DTEND;TZID=America/Los_Angeles:20261116T170000
DTSTAMP:20261015T165727Z
CREATED:20261015T165630Z
LAST-MODIFIED:20261015T165727Z
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SUMMARY:AM Seminar: Heat Transport and Scalar Dissipation in Turbulent Flows: A Constructive Approach
DESCRIPTION:Presenter: Anuj Kumar\, Assistant Professor\, UC Davis \nDescription: Turbulent flows can transport heat and momentum much more efficiently than pure molecular diffusion alone. What flow structures enable this efficient transport and how can they sustain dissipation when diffusivity or viscosity is small? These questions are fundamental to understanding turbulence and have important applications in engineering. In this talk\, I will discuss a constructive approach to these questions through explicitly designed incompressible flows. We will first consider heat transport between walls held at different temperatures. Under a constraint on the power supply\, I will present a three dimensional branching construction that achieves the optimal heat transport scaling. By examining these designs carefully\, we will identify the physical mechanisms responsible for efficient heat transfer and discuss potential applications\, including the pressing problems of cooling in AI data centers. We will then turn to scalar turbulence. I will introduce a universal incompressible flow built from a carefully organized hierarchy of shear flows and explain how these constructions approach the sharp Obukhov–Corrsin regularity threshold for scalar dissipation. These constructions also exhibit eddy-diffusivity effect: advection mimics diffusion of order one even in the transport equation\, where molecular diffusion is entirely absent. Together\, these examples illustrate how the process of constructing flows that achieve sharp scaling laws can also reveal the mechanisms underlying enhanced transport and dissipation\, which are two key features of turbulence. \nAbout the speaker: Anuj Kumar is an Assistant Professor in the Department of Mathematics at the University of California Davis. His research lies at the intersection of fluid dynamics\, partial differential equations (PDEs) and applied mathematics\, combining computational\, mathematical analysis and theoretical approaches. Prior to joining UC Davis\, he was a Visiting Assistant Professor in the Department of Mathematics at the University of California Berkeley. He earned my Ph.D. in Applied Mathematics from the University of California Santa Cruz in 2023. \nThis seminar is hosted by Professor Pascale Garaud.
URL:https://live-events-ucsc.pantheonsite.io/event/am-seminar-heat-transport-and-scalar-dissipation-in-turbulent-flows-a-constructive-approach/
LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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END:VEVENT
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DTSTART;TZID=America/Los_Angeles:20261123T080000
DTEND;TZID=America/Los_Angeles:20261123T090000
DTSTAMP:20261013T161927Z
CREATED:20261013T161821Z
LAST-MODIFIED:20261013T161927Z
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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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