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SUMMARY:ECE Seminar: From Plumes to Produce: Leveraging Atmospheric Modeling and Smart Sensing for Food Safety
DESCRIPTION:Presenter: Derek Hollenbeck\, postdoctoral research scholar\, University of California\, Merced  \nDescription: Advances in drone-based environmental sensing\, atmospheric modeling\, and intelligent monitoring systems are creating new opportunities for addressing emerging challenges in food safety and agricultural resilience. This talk explores how methodologies originally developed for methane emission detection and quantification could be translated toward agricultural and food safety applications. The presentation begins with an overview of research experiences in autonomous sensing and environmental monitoring\, including work associated with the inaugural CITRIS Aviation Prize before outlining some key potential areas for food safety with drones. Then\, the talk overviews previous research on the topics related to drone-based environmental monitoring\, Digital Twins\, and Smart Sensing – with a focus on methane emission source quantification\, atmospheric transport modeling of a point source\, and inverse problem methodologies for real-time parameter estimation. Finally\, the talk examines how these concepts may be adapted to food safety research questions\, as well as highlight opportunities for interdisciplinary collaboration alongside emerging priorities from organizations and certification frameworks. \nBio: Derek Hollenbeck is a postdoctoral research scholar at the University of California\, Merced (UCM)\, where he serves as the manager of the Center for Methane Emissions Research and Innovation (CMERI) under the supervision of Dr. YangQuan Chen. He earned his B.Sc. (2016) and Ph.D. (2023) in Mechanical Engineering from UCM\, where he conducted research in the Mechatronics Embedded Systems and Automation (MESA) Lab.\n \nHis work sits at the intersection of fluid mechanics\, controls\, dynamics\, and inverse problems\, with a focus on developing intelligent environmental monitoring systems using small unmanned aerial systems (sUAS). His research integrates machine learning and physics-based modeling to detect\, localize\, and quantify methane emissions in complex environments.\n \nDr. Hollenbeck is the author of Smart Sensing with Digital Twins: Methane Emission Source Determination with sUAS\, which presents a framework for combining digital twins\, inverse modeling\, and autonomous sensing to improve environmental observability. His work emphasizes how data-driven and physics-informed approaches can be fused to optimize sensor placement\, enhance estimation accuracy\, and enable real-time decision-making in single/distributed mobile sensing systems. \nHosted by: Professor Marco Rolandi\, ECE Department \nZoom Link: https://ucsc.zoom.us/j/96727838511?pwd=1Qzl9HTV3G2BxaSEG8GeKOPZVu2NWj.1
URL:https://live-events-ucsc.pantheonsite.io/event/ece-seminar-from-plumes-to-produce-leveraging-atmospheric-modeling-and-smart-sensing-for-food-safety/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Seminars
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DTSTART;TZID=America/Los_Angeles:20260518T104000
DTEND;TZID=America/Los_Angeles:20260518T114500
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SUMMARY:ECE 290 Seminar: AI for Enhancing Power Grid Resilience Against Extreme Weather Events
DESCRIPTION:Presenter: Masood Parvania\, Roger P. Webb Endowed Professor\, University of Utah \n  \nDescription: Many communities across the world are experiencing more frequent and severe extreme weather disturbances such as wildfires\, heatwaves\, drought\, storms\, rising sea levels\, and flooding\, which not only pose threats to human health\, and the environment but also affect the ability of the power grid to continue powering the communities. This requires upgrading the operation of power grid from passive and manual applications to making complex decisions in real-time to facilitate the automated recovery of the system after major disturbances. This talk will review the application of various AI and ML techniques for detection\, response and mitigation of cyber anomalies and extreme weather events in power distribution systems.\n \n  \nBio: Masood Parvania is the Roger P. Webb Endowed Professor of Electrical and Computer Engineering and the Director of Utah Smart Energy Laboratory (U-Smart) at the University of Utah. Dr. Parvania is the Principal Investigator and Director of the U.S.-Canada Center on Climate-Resilient Western Interconnected Grid (NSF WIRED Global Center)\, co-funded by U.S. National Science Foundation (NSF) and Natural Sciences and Engineering Research Council of Canada (NSERC). He is also the Founder and President of the Energy-AI company\, Grid Elevated\, which specializes on developing and commercializing AI technology for resilient and efficient power grid operation. \n  \nHosted by: Professor Soumya Bose\, ECE Department \nZoom Link: https://ucsc.zoom.us/j/97975378707?pwd=ljcgaCfhMmhZ88Vt5dqQUBVQRjehOx.1
URL:https://live-events-ucsc.pantheonsite.io/event/ece-290-seminar-ai-for-enhancing-power-grid-resilience-against-extreme-weather-events/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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DTSTART;TZID=America/Los_Angeles:20260518T132500
DTEND;TZID=America/Los_Angeles:20260518T143000
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SUMMARY:Seminar Series | Is the Farm a Digital Factory?: Labor\, Leafy Greens\, and the Limits of Automation with Summer Sullivan
DESCRIPTION:Host:Madeleine Fairbairn \nSilicon Valley investors\, entrepreneurs\, and engineers are increasingly interested in agriculture as a site to disrupt and improve upon with their technologies. The nearby Salinas Valley – known as the Salad Bowl of the World – might be considered a “ground zero” for these operations of technological introduction\, with some calling it the Silicon Valley of Agriculture. This exit talk showcases my research on the evolving context in which new technologies are transforming social and environmental relations\, especially for already exploited\, racialized workers in the Salinas Valley. I trace the uneven ways in which agricultural automation is unfolding\, but also its profound limits within the region’s delicate\, leafy farming systems. Through interviews\, focus groups\, participant observation\, and historical analysis\, I will show how the materiality of crops such as lettuce continues to organize labor and limit full automation. Contributing to critical analysis of the uneven racial\, class\, and gender dynamics of the “future of work\,” this project centers emergent\, uncertain relationships among farmworkers\, the plants they care for\, and the fragile futures of capitalism. \nIn person and on Zoom \nMeeting ID:  949 5253 7079 \nPasscode: 552886
URL:https://live-events-ucsc.pantheonsite.io/event/seminar-series-is-the-farm-a-digital-factory-labor-leafy-greens-and-the-limits-of-automation-with-summer-sullivan/
LOCATION:Interdisciplinary Sciences Building\, 7487 Red Hill Road\, Santa Cruz\, CA\, 95064
CATEGORIES:Seminars
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260518T160000
DTEND;TZID=America/Los_Angeles:20260518T170000
DTSTAMP:20260408T220408Z
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SUMMARY:Statistics Seminar: Unifying Regression-Based and Design-Based Causal Inference in Time-Series Experiments and Crossover Experiments
DESCRIPTION:Presenter: Peng Ding\, Associate Professor\, UC Berkeley \nDescription: I will present some recent results on unifying regression-based and design-based causal inference in time-series experiments and crossover experiments. Part I: Time-series experiments\, also called switchback experiments or N-of-1 trials\, play increasingly important roles in modern applications in medical and industrial areas. Under the potential outcomes framework\, recent research has studied time-series experiments from the design-based perspective\, relying solely on the randomness in the design to drive the statistical inference. Focusing on simpler statistical methods\, we examine the design-based properties of regression- based methods for estimating treatment effects in time-series experiments. We demonstrate that the treatment effects of interest can be consistently estimated using ordinary least squares with an appropriately specified working model and transformed regressors. Additionally\, we show that asymptotically\, the heteroskedasticity and autocorrelation consistent variance estimators provide conservative estimates of the true\, design-based variances. This part is based on https://arxiv.org/pdf/2510.22864  \nPart II: Crossover designs randomly assign each unit to receive a sequence of treatments. By comparing outcomes within the same unit\, these designs can effectively eliminate between-unit variation and facilitate the identification of both instantaneous effects of current treatments and carryover effects from past treatments. They are widely used in traditional biomedical studies and are increasingly adopted in modern digital platforms. However\, standard analyses of crossover designs often rely on strong parametric models\, making inference vulnerable to model misspecification. We unify the analysis of crossover designs using least squares\, with restrictions on the coefficients and weights on the units. Based on the theory\, we recommend specifying the regression function\, weighting scheme\, and coefficient restrictions to assess identifiability\, construct efficient estimators\, and estimate variances in a unified manner. This part is based on https://arxiv.org/pdf/2511.09215 \nAbout the speaker: Peng Ding is an Associate Professor in the Department of Statistics at UC Berkeley. He obtained his Ph.D. from the Department of Statistics\, Harvard University in May 2015 and worked as a postdoctoral researcher in the Department of Epidemiology\, Harvard T. H. Chan School of Public Health until December 2015. Previously\, he received his B.S. in Mathematics\, B.A. in Economics\, and M.S. in Statistics from Peking University. \nThis seminar is hosted by Professor Allen Kei.
URL:https://live-events-ucsc.pantheonsite.io/event/statistics-seminar-unifying-regression-based-and-design-based-causal-inference/
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:20260518T160000
DTEND;TZID=America/Los_Angeles:20260518T170000
DTSTAMP:20260429T152454Z
CREATED:20260429T152454Z
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SUMMARY:AM Seminar: Dissecting Complex Disease Mechanisms with Causal Inference and Deep Learning
DESCRIPTION:Presenter: Dr. David A. Knowles\, New York Genome Center & Columbia University \nDescription: Many human diseases have a substantial genetic component\, which association studies are increasingly capable of characterizing\, empowered by ever-growing sample sizes. These associations have the potential to elucidate complex disease biology and prioritize therapeutic interventions. However\, it is challenging to determine the impacted genes\, pathways and cellular states since most risk variants are noncoding. I will describe strategies we have explored to address this challenge\, particularly in the context of Alzheimer’s disease. We have mapped genetic effects on expression\, splicing and RNA editing in over 10k postmortem brain samples\, enabling interpretation of common variant associations. We developed a Mendelian randomization-based causal network inference method to estimate how genetic effects propagate through the gene network to converge on disease risk. We show that deep learning models of pre- and post- transcriptional regulation can refine functional fine-mapping\, improve the portability of polygenic risk scores across ancestries\, and increase power in novel annotation-aware noncoding rare variant association studies. Finally\, we designed a CRISPR/Cas13-based strategy to perform isoform-specific knockdown\, opening the door for isoform-resolved functional characterization of putative disease-causal transcriptomic changes. \nAbout the speaker: Dr. Knowles studied Natural Sciences and Information Engineering at Cambridge before obtaining an MSc in Bioinformatics and Systems Biology at Imperial College London. During his PhD in the Cambridge University Machine Learning Group under Zoubin Ghahramani he worked on variational inference and Bayesian nonparametric models. He was a postdoc at Stanford developing methods for functional genomics with Daphne Koller (CS)\, Sylvia Plevritis (Computational Systems Biology/Radiology) and Jonathan Pritchard (Genetics/Biology). At Columbia\, he is an Associate Professor of Computer Science\, an Interdisciplinary Appointee in Systems Biology and an Affiliate Member of the Data Science Institute. He is also a Core Faculty Member at the New York Genome Center. His group develops methods to better understand the genetic basis of human disease. \n\n\n\nThis seminar is hosted by Professor Nilah Ioannidis.
URL:https://live-events-ucsc.pantheonsite.io/event/am-seminar-dissecting-complex-disease-mechanisms-with-causal-inference-and-deep-learning/
LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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