The MICrONS program aims to advance a new generation of neural-inspired machine learning algorithms by reverse engineering the algorithms and computations of the brain. The Sandia team’s efforts will include applying sensitivity analysis to validate computational neural models, developing novel challenge stimuli and evaluation metrics to assess the performance of novel machine learning algorithms, and designing evaluation methodologies for assessing computational neural model designs and the neural fidelity of machine learning algorithms. This work will book through the Defense Systems and Assessments PMU and supports the Synergistic Defense Products Mission Area. The team includes (in alphabetical order):īrad Aimone (1462), Kristofor Carlson (1462), Brad Carvey (1461), Warren Davis (1461), Michael Haass (1461), Jacob Hobbs (6132), Kiran Lakkaraju (1463), Kim Pfeiffer (1720), Fred Rothganger (1462), Timothy Shead (1461), Craig Vineyard (1462), Christina Warrender (1461) The Sandia team is highly interdisciplinary and includes computational neuroscientists (a growing capability within 1460) as well as researchers from existing 1460 strengths in machine learning, data analytics, and computation. Rising Stars in Computational & Data Sciences is an intensive academic and research career workshop series for women graduate students and postdocs. Co-organized by Sandia and UT-Austin’s Oden Institute for Computational Engineering & Sciences, Rising Stars brings together top women PhD students and postdocs for technical talks, panels, and networking events. The workshop series began in 2019 with a two-day event in Austin, TX. Due to travel limitations associated with the pandemic, the 2020 Rising Stars event went virtual with a compressed half-day format. Nonetheless, it was an overwhelming success with 28 attendees selected from a highly competitive pool of over 100 applicants. The workshop featured an inspiring keynote talk by Dr. Rachel Kuske, Chair of Mathematics at Georgia Institute of Technology, as well as lightning-round talks and breakout sessions. Several Sandia managers and staff also participated. The Rising Stars organizing committee includes Sandians Tammy Kolda (Distinguished Member of Technical Staff, Extreme-scale Data Science & Analytics Dept.) and James Stewart (Sr. Single-cell omics is the fastest-growing type of genomics data in the literature and public genomics repositories.Manager, Computational Sciences & Math Group), as well as UT Austin faculty Karen Willcox (Director, Oden Institute) and Rachel Ward (Assoc. Leveraging the growing repository of labeled datasets and transferring labels from existing datasets to newly generated datasets will empower the exploration of single-cell omics data. However, the current label transfer methods have limited performance, largely due to the intrinsic heterogeneity among cell populations and extrinsic differences between datasets. Here, we present a robust graph artificial intelligence model, single-cell Graph Convolutional Network (scGCN), to achieve effective knowledge transfer across disparate datasets. Through benchmarking with other label transfer methods on a total of 30 single cell omics datasets, scGCN consistently demonstrates superior accuracy on leveraging cells from different tissues, platforms, and species, as well as cells profiled at different molecular layers. scGCN is implemented as an integrated workflow as a python software, which is available at. Single-cell omics technologies are increasingly used in biomedical research to provide high resolution insights into the complex cellular ecosystem and underlying molecular interconnectedness 1, 2, 3. Leading this wave of omics is single-cell RNA sequencing (scRNA-seq), which allows measurement of the transcriptome in thousands of single cells from multiple biological samples under various conditions 4, 5, 6, 7, 8. Other single-cell-based assays, include Single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq), profile cellular heterogeneity at the epigenetic level 9, 10, 11, which further elucidates transcriptional regulators 8, 12. These technological developments allow profiling of multiple molecular layers at single-cell resolution and assaying cells from multiple samples under different conditions. The rapid advances in single-cell technologies have led to remarkable growth of single cell omics data. As more and more single-cell datasets become available, there is an urgent need to leverage existing and newly generated data in a reliable and reproducible way, learning from the established single-cell data with well-defined labels as reference, and transferring labels to newly generated datasets to assign cell-level annotations 10, 11.
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