add four new posts

Author: hauten

_posts/2025-01-17-ecpdoe.md

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+title: "ECP Leadership Team Recognized with Secretary of Energy’s Honor Award"
+categories: story
+---
+
+LLNL employees participating in three project teams were recently recognized with Department of Energy (DOE) Secretary’s Honor Awards. Among them was the Exascale Computing Project (ECP) leadership team, which successfully delivered the ECP, a 7-year, $1.8 billion collaboration among six DOE national laboratories to create the world’s first sustainable exascale ecosystem, resulting in the development and enhancement of 25 scientific application codes to provide breakthrough simulation results on exascale computers. Additionally, over 70 software technology products were delivered in an integrated package widely used by the high-performance computing community. This first-of-a-kind software research, development and deployment (RD&D) project was jointly managed by the DOE Office of Science and the National Nuclear Security Administration, with leadership from Argonne, Lawrence Berkeley, Lawrence Livermore, Los Alamos, Oak Ridge and Sandia National Laboratories. ECP also established public-private partnerships by funding computer vendors to prepare the U.S. industry for exascale system procurements, enhancing U.S. competitiveness in the global computing market. The legacy of ECP will be felt for decades through its delivered products, trained staff and best practices in leading large, collaborative RD&D software projects. Visit LLNL News for the [full article](https://www.llnl.gov/article/52386/lab-teams-recognized-secretary-energys-honor-awards).

_posts/2025-02-11-bayesmtl-new.md

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+title: "New Repo: BayesMTL"
+categories: new-repo
+---
+
+[BayesMTL](https://github.com/LLNL/BayesMTL) is an implementation of Bayesian multitask learning from the paper [Hierarchical Sparse Bayesian Multitask Model with Scalable Inference for Microbiome Analysis](https://arxiv.org/abs/2502.02552). Abstract:
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+> This paper proposes a hierarchical Bayesian multitask learning model that is applicable to the general multi-task binary classification learning problem where the model assumes a shared sparsity structure across different tasks. We derive a computationally efficient inference algorithm based on variational inference to approximate the posterior distribution. We demonstrate the potential of the new approach on various synthetic datasets and for predicting human health status based on microbiome profile. Our analysis incorporates data pooled from multiple microbiome studies, along with a comprehensive comparison with other benchmark methods. Results in synthetic datasets show that the proposed approach has superior support recovery property when the underlying regression coefficients share a common sparsity structure across different tasks. Our experiments on microbiome classification demonstrate the utility of the method in extracting informative taxa while providing well-calibrated predictions with uncertainty quantification and achieving competitive performance in terms of prediction metrics. Notably, despite the heterogeneity of the pooled datasets (e.g., different experimental objectives, laboratory setups, sequencing equipment, patient demographics), our method delivers robust results.

_posts/2025-02-20-vanguards.md

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+title: "Spack Builder Todd Gamblin of LLNL on the Why’s and How’s of Change in HPC"
+categories: story
+---
+
+In *insideHPC*'s continuing series on current and future leaders of HPC-AI, LLNL’s Todd Gamblin has a well-deserved reputation in the HPC software community as a passionate engineer who enjoys rolling up his sleeves and diving into technical problems. Read the full [Vanguards of HPC-AI](https://insidehpc.com/2025/02/vanguards-of-hpc-ai-spack-builder-todd-gamblin-of-llnl-on-the-whys-and-hows-of-change-in-hpc/) article.

_posts/2025-02-21-gula-new.md

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+---
+title: "New Repo: Gula"
+categories: new-repo
+---
+
+[Gula](https://github.com/LLNL/Gula) a 3D model of the Gula Regional Hospital, a hypothetical facility for studying the protection of high-activity radioactive sources against theft or sabotage. Description:
+
+> The facility includes 2 stories above ground and a basement. The basement contains two high-activity radiation sources, a blood irradiator (assumed to be using a cesium-137 or cobalt-60 source), and a teletherapy device (assumed to be cobalt-60) for treating cancer patients. These parts of the facility are hardened from a physical security perspective. The high-activity radiation sources are protected in keeping with ORS and international consensus. This includes systems for access control, physical protection, video monitoring, and radiation detection.  The combination of these systems ensures a robust detection and response against external and insider threats of theft or sabotage. The facility also contains a security department with a central alarm station (CAS), medical emergency room, treatment facilities, office space, etc.