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SUMMARY:Neuroimaging Initiative webinar: Bayesian scalar-on-image neural networks with application to neuroimaging data
DESCRIPTION:About Dr. Kang: Jian Kang is a Professor in the Department of Biostatistics and is a faculty member of the Kidney Epidemiology and Cost Center (KECC) at the University of Michigan.  He received his PhD in Biostatistics from the University of Michigan in 2011. He was an Assistant Professor in the Department of Biostatistics and Bioinformatics and the Department of Radiology and Imaging Sciences at Emory University from 2011 – 2015. He was a core faculty member in the Center for Biomedical Imaging Statistics (CBIS) at Emory University.  His primary research interests are in developing statistical methods for large-scale complex biomedical data with application in precision medicine\, imaging\, epidemiology and genetics. \n\nBAYESIAN SCALAR-ON-IMAGE NEURAL NETWORKS WITH APPLICATION TO NEUROIMAGING DATA\nDeep neural networks have been adopted in the scalar-on-image regression which predicts the outcome variable using image predictors. However\, training DNN often requires a large sample size to achieve a good prediction accuracy and the model fitting results can be difficult to interpret. In this work\, we construct a novel single-layer Bayesian neural network (BNN) with spatially-varying coefficients (SVC) for the scalar-on-image regression. Our goal is to select interpretable image features and to achieve the high prediction accuracy with limited training samples. We assign the soft-thresholded Gaussian process (STGP) prior to the SVCs and develop an efficient posterior computation algorithm based on stochastic gradient Langevin Dynamics (SGLD). The BNN-STGP provides a large prior support for sparse\, piecewise-smooth and continuous SVCs\, enabling efficient posterior inference on image feature selection and automatically determining the network structures. We establish the posterior consistency of estimating the SVCs in the model and image feature selection consistency when the number of voxels/pixels grows much faster than the sample size. We compared our methods with state-of-the-art deep learning methods via extensive simulations and analyses of multiple real datasets including the task fMRI data from the ABCD study. \n\nConnect to the webinar via this link. Meeting ID: 923 3875 2870. Meeting Passcode: 149254
URL:https://micde.umich.edu/event/bayesian-scalar-on-image-neural-networks-with-application-to-neuroimaging-data/
LOCATION:Zoom Event
CATEGORIES:Webinar
ATTACH;FMTTYPE=image/jpeg:https://micde.umich.edu/wp-content/uploads/2023/02/Jian-Kang-Headshot.jpg
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DTSTART;TZID=America/Detroit:20200929T140000
DTEND;TZID=America/Detroit:20200929T150000
DTSTAMP:20230905T171252Z
CREATED:20230905T171252Z
LAST-MODIFIED:20230905T171252Z
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SUMMARY:MICDE / Mechanical Engineering Seminar: Sophia Haussener\, Associate Professor\, Laboratory of Renewable Energy Science and Engineering\, EPFL\, Lausanne\, Switzerland
DESCRIPTION:View webinar recording. \nBio: Sophia Haussener is an Associate Professor heading the Laboratory of Renewable Energy Science and Engineering at the Ecole Polytechnique Fédérale de Lausanne (EPFL). Her current research is focused on providing design guidelines for thermal\, thermochemical\, and photoelectrochemical energy conversion reactors through multi-physics modelling. Her research interests include: thermal sciences\, fluid dynamics\, charge transfer\, electro-magnetism\, and thermo/electro/photochemistry in complex multi-phase media on multiple scales. She received her MSc (2007) and PhD (2010) in Mechanical Engineering from ETH Zurich. Between 2011 and 2012\, she was a postdoctoral researcher at the Joint Center of Artificial Photosynthesis (JCAP) and the Energy Environmental Technology Division of the Lawrence Berkeley National Laboratory (LBNL). She has published over 70 articles in peer-reviewed journals and conference proceedings\, and 2 books. She has been awarded the ETH medal (2011)\, the Dimitris N. Chorafas Foundation award (2011)\, the ABB Forschungspreis (2012)\, the Prix Zonta (2015)\, the Global Change Award (2017)\, and the Viskanta Award (2019)\, and is a recipient of a Starting Grant of the Swiss National Science Foundation (2014). She is a deputy leader in the Swiss Competence Center for Energy Research (SCCER) on energy storage and acts as a Member of the Scientific Advisory Council of the Helmholtz Zentrum. \nModelling\, experimentation and scaling of photo-electrochemical fuel processing devices\nThe development of a sustainable energy economy based on renewable\, carbon-neutral energy is a necessary and urgent task. Photo-electrochemical approaches for solar fuels and materials are interesting\, provided they can be efficiently\, stably\, scalably\, and sustainably implemented. The functionality of such devices relies on complicated and coupled multi-physics processes\, occurring at multiple temporal and spatial scales. Device modelling can actively and efficiently support the choice of the most promising – in terms of efficiency\, cost\, robustness\, scalability\, and practicability – conceptual design pathways\, material choices\, and operating approaches. \nFirst\, I focus on cost competitive photo-electrochemical (PEC) devices identified through quasi-transient techno-economic modelling [1]. I will describe the conceptual idea of thermal integration in the context of PEC [2]\, provide results of maximum theoretical efficiency calculations to quantify the benefits\, and review the modelling framework that enabled the design of a feasible device [3]. I will illustrate how we have used our models to design and implement a PEC device with a solar-to-fuel efficiency of 17%\, and discuss ongoing approaches to scale up by our lab in order to bridge the gap between research and practical applications. \nSecond\, I will discuss detailed multi-dimensional mesoscale models that allow to assess the transport in complex (photo)electrodes. Specifically\, we use direct pore-level simulations for the coupled transport characterization of mesostructured (photo)electrodes utilizing nano-tomography techniques to obtain the exact mesostructure that is utilized in direct numerical simulations [4]. I will extend these investigations to ordered structures for the assessment of the transport in mesostructured electrodes for the electorchemical reduction of CO2 and discuss the effect of the mass transport on selectivity and activity [5]. I will then present possibilities to simplify these involved multi-dimensional numerical models into rapid screening models based on semi-analytical correlations. I will discuss analysis results for a large range of semiconductor materials [6\,7]. I will end with an outlook on research challenges and gaps in the field of (photo)electrochemical water and CO2 splitting. \n\nThis seminar is co-hosted by the Michigan Institute for Computational Discovery & Engineering\, and the Mechanical Engineering department within the University of Michigan College of Engineering. Dr. Haussener will be hosted by Rohini Bala Chandran\, Assistant Professor of Mechanical Engineering. \nThe MICDE Fall 2020 and Winter 2021 Seminar Series is open to the general public. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend.  \nQuestions? Email MICDE-events@umich.edu \n\nReferences: \n[1] M. Dumortier\, S. Tembhurne\, S. Haussener\, Energy Environ. Sci. \, 8:3614–3628\, 2015\n[2] S. Tembhurne\, F. Nandjou\, S. Haussener\, Nature Energy\, 10.1038/s41560-019-0373-7\, 2019\n[3] S. Tembhurne\, S. Haussener\, Journal of The Electrochemical Society \, 163:H1008-H1018\, 2016\n[4] S. Suter\, M. Catoni\, Y. Gaudy\, S. Pokrant\, S. Haussener\, Linking Morphology and Multi-Physical Transport in\nStructured Photoelectrodes\, Sustainable Energy & Fuels \, doi: 10.1039/C8SE00215K\, 2018.\n[5] S. Suter\, S. Haussener\, Energy Environmental Science \, doi: 10.1039/C9EE00656G\, 2019.\n[6] Y. Gaudy\, S. Haussener\, Rapid Performance Optimization Method for Photoelectrodes\, Journal of Physical Chemistry\nC\, doi: 10.1021/acs.jpcc.9b04102\, 2019.\n[7] Y. Gaudy\, Z. Gacevic\, Haussener\, Theoretical maximum photogeneration efficiency and performance characterization\nof InxGa1-xN/Si tandem water-splitting photoelectrodes\, APL Materials\, accepted\, 2020.
URL:https://micde.umich.edu/event/micde-mechanical-engineering-seminar-sophia-haussener-associate-professor-laboratory-of-renewable-energy-science-and-engineering-swiss-federal-institute-of-technology-lausanne/
LOCATION:Zoom Event
CATEGORIES:Featured Events,MICDE Seminar Series
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2020/09/Sophia-Haussener.png
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DTSTART;TZID=America/Detroit:20200923T140000
DTEND;TZID=America/Detroit:20200923T170000
DTSTAMP:20260612T024254Z
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SUMMARY:Using GPUs with Python
DESCRIPTION:Python is the Lingua Franca of Data today and is being increasingly used in scientific computations. This workshop introduces Python GPU tools for porting and writing code that runs on GPUs. The primary tools\, Numba and CuPy\, are presented with examples. This workshop is presented by Kristopher Keipert of NVIDIA. \nThis event is open to students\, faculty\, and staff within the University of Michigan community. A Jupyter notebook is used along with a set of lecture slides. The workshop will use online tools\, so there is no need to install any software ahead of time. \nThis event is brought to you by the Michigan Institute for Computational Discovery and Engineering\, and Consulting for Statistics\, Computing & Analytics Research at the University of Michigan in partnership with NVIDIA. \nRegistration for this event has closed.
URL:https://micde.umich.edu/event/using-gpus-with-python-workshop-by-nvidia/
LOCATION:Zoom Event
CATEGORIES:Workshops
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DTSTART;TZID=America/Detroit:20200909T130000
DTEND;TZID=America/Detroit:20200909T140000
DTSTAMP:20260612T025653Z
CREATED:20230905T171250Z
LAST-MODIFIED:20260612T025653Z
UID:10000397-1599656400-1599660000@micde.umich.edu
SUMMARY:Testing and Code Review Practices in Research Software Development Webinar
DESCRIPTION: MICDE’s Center for Scientific Software Infrastructure encourages you to check out this webinar! \nThis webinar is part of the HPC Best Practices webinar series. This series address issues faced by developers of computational science and engineering (CSE) software on high-performance computers  (HPC). \nSoftware quality in a research context is essential because research software is used in mission-critical situations\, decision making\, and computation of evidence for research publications. This webinar will cover the use of two software quality practices in the development of research software: software testing and peer code review. These practices in software development can lead to both improved scientific results through higher quality software in the short term and more maintainable software in the long term. While these practices are essential for any type of software\, developers of research software typically do not use peer code review and software testing as frequently as they could for maximum impact. \nThe presenter\, California Polytechnic State University Assistant Professor Nasir Eisty\, will discuss the motivation\, challenges\, barriers\, and necessary improvements to make the practices effective for research software development\, based on studies of the research software community conducted via interviews\, surveys\, workshops\, and tutorials. \nParticipation is free and open to the public\, however registration is required for each event. This series is designed for HPC software developers who are seeking help in increasing their team’s productivity\, as well as facility staff who interact extensively with users. \nThese webinars have been organized by the IDEAS project in collaboration with the DOE/ASCR computing facilities (ALCF\, NERSC\, and OLCF)\, and the Exascale Computing Project (ECP).
URL:https://micde.umich.edu/event/testing-and-code-review-practices-in-research-software-development-webinar/
LOCATION:Zoom Event
CATEGORIES:Featured Events,Workshops
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2023/02/Testing-and-Code-Review-Practices-in-Research-Software-Development-Webinar.png
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