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DTSTART;TZID=America/Los_Angeles:20261012T104000
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CREATED:20261005T192533Z
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SUMMARY:ECE 290 Seminar: "Photon Counting Detectors for X-Ray Imaging"
DESCRIPTION:Presented by: Jin Zhang\, Chief Engineer @ iRay Group \nDescription: “Photon-counting detectors measure individual X-ray interactions\, enabling fine spatial sampling and energy-resolved imaging. This seminar connects semiconductor signal formation and readout electronics to imaging benefits and practical limitations in medical and industrial applications. It examines charge sharing\, pulse pileup\, calibration drift\, and material decomposition\, then surveys clinical PCCT systems and detector technologies from Siemens\, GE\, Canon\, Varex\, CERN\, DECTRIS\, and iRay. Examples span diagnostic CT\, industrial inspection\, and laboratory and synchrotron measurements. The discussion concludes with research priorities in sensor manufacturing\, high-rate electronics\, module integration\, and quantitative validation. The central question is how to preserve useful spatial and spectral information at realistic flux\, dose\, and throughput\, and demonstrate improvements through fair\, task-specific comparisons.” \nBio: Jin Zhang\, Ph.D.\, is an engineering and R&D leader with more than 20 years of experience in medical imaging\, semiconductor detectors\, and AI-enabled sensing. Currently Chief Engineer at iRay Group\, he previously held leadership roles at Varex Imaging\, Quanergy\, and PerkinElmer\, leading global teams across North America\, Europe\, and Asia. His expertise spans photon-counting detectors\, image processing\, and bringing advanced imaging technologies into commercial products. He earned his Ph.D. from the University of Rochester and completed postdoctoral research at Stanford University. He has authored more than 40 publications and holds a granted U.S. patent. \n  \nHosted by: Professor Zhicong Yu\, Electrical & Computer Engineering Department \nWhen: Monday\, October 12th from 10:40 to 11:45 AM PST \nLocation: Classroom E2-194
URL:https://events.ucsc.edu/event/ece-seminar-photon-counting-detectors-for-x-ray-imaging/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Seminars
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DTSTART;TZID=America/Los_Angeles:20261015T114000
DTEND;TZID=America/Los_Angeles:20261015T131500
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SUMMARY:BME 280B Seminar: Dr. Kiley Graim "Beyond the Majority: Correcting for Genetic Ancestry Bias in Disease Genomics"
DESCRIPTION:Welcome to the 3rd week of our BME 280B seminar series\, a weekly seminar covering topics of current research in computational biology and bioinformatics. \nOpen to all\, food is provided. Class enrollment is not required. \nPlease join us this week to learn more about the works of Dr. Kiley Graim: \n  \nPresenter: Dr. Kiley Graim\, Assistant Professor of Computer & Information Science & Engineering\, University of Florida \nDescription: Advances in high-throughput sequencing have transformed disease genomics\, yet persistent sampling biases continue to confound downstream analyses. Omics datasets remain overwhelmingly enriched for individuals of European ancestry\, introducing population stratification that is frequently conflated with disease-relevant signals. This is compounded by the fact that local adaptation has shaped allele frequency distributions differentially across ancestral populations\, producing lineage-specific variants that modulate both disease susceptibility and drug response. AI models often fail to generalize across populations\, exacerbating disparities in precision medicine. I will present approaches for mitigating these confounds by integrating population-scale genomic resources (e.g.\, gnomAD\, 1000 Genomes) with comparative evolutionary genomics across species\, enabling ancestry-aware and cross-species-informed modeling strategies for more generalizable disease genomics. \nBio: Dr. Kiley Graim is an Assistant Professor in the Department of Computer & Information Science & Engineering at the University of Florida. She earned a B.S. and M.S. in Computer Science from Colorado State University and a Ph.D. in Biomolecular Engineering from the University of California\, Santa Cruz\, followed by postdoctoral training at the Simons Foundation and Princeton University. Her research combines biology\, genomics\, and computer science to develop multi-omic artificial intelligence methods for identifying the molecular drivers of human disease. Her lab’s research spans cancer evolution across mammalian species\, precision medicine\, equitable AI\, and sepsis. \nHosted by: Professor of Biomolecular Engineering\, Dr. Benedict Paten
URL:https://events.ucsc.edu/event/bme-280b-seminar-101526/
LOCATION:Physical Sciences Building\, Physical Sciences Building\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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DTSTART;TZID=America/Los_Angeles:20261015T160000
DTEND;TZID=America/Los_Angeles:20261015T170000
DTSTAMP:20261012T195741Z
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SUMMARY:Statistics Seminar: A Latent Factor Multivariate Point Process Model for Inferring the Neural Code
DESCRIPTION:Presenter: Reza Ramezan\, Associate Professor\, Teaching Stream (Research Active)\, University of Waterloo \nDescription: Neural spike trains are electrochemical signals that are localized in time and serve as the primary means of communication among nerve cells. The analysis of simultaneously recorded spike trains from multiple neurons presents substantial statistical and computational challenges. In this talk\, I will introduce a biologically inspired multivariate point process model for analyzing such data\, formulated as a continuous-time latent factor model (LFM). To the best of our knowledge\, this is the first continuous-time multivariate LFM developed to investigate neuronal interactions and functional connectivity using neural spike trains. By leveraging the computational advantages of approximate Bayesian inference\, I will demonstrate that the proposed model can accommodate larger neuronal ensembles than existing methods\, thereby providing an efficient framework for studying population coding. I will also discuss an application to experimental data from a classical conditioning study of the rat prefrontal cortex\, illustrating how the proposed approach can enhance our understanding of the neural encoding of cue and outcome values. \nAbout the speaker: Reza Ramezan is an Associate Professor\, Teaching Stream (Research Active)\, in the Department of Statistics and Actuarial Science at the University of Waterloo\, where he is also a core member of the institution’s Centre for Theoretical Neuroscience. His methodological research focuses on developing computationally efficient stochastic\, multivariate\, and high-dimensional models\, with applications in neuroscience. Reza earned his PhD in Statistics from the University of Waterloo in 2014. He subsequently joined the Department of Mathematics at California State University\, Fullerton\, before returning to Waterloo in 2017. He has received numerous awards and recognitions for his teaching and research. His professional leadership includes serving as the former President of the Business and Industrial Statistics Section of the Statistical Society of Canada. \n\n\n \n\n\n\nThis seminar is hosted by Professor Yunyi Shen
URL:https://events.ucsc.edu/event/statistics-seminar-a-latent-factor-multivariate-point-process-model-for-inferring-the-neural-code/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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