This project introduces an interactive, web-based 3D data viewer specifically designed for the analysis of large-scale scientific cosmological datasets. The OpenCosmo Compute Portal [1], developed by Argonne National Laboratory’s Cosmological Physics and Advanced Computing group, provides easy access to cosmological simulations. Adding advanced visualization capabilities, such as those offered by this viewer, would significantly enhance the portal’s utility, allowing a broader audience to gain deeper insights into queried datasets without needing to build their own visualization tools.We address the critical need to democratize access to advanced scientific visualization, particularly for researchers who aren’t visualization experts. Leveraging modern web technologies, our viewer provides a fluid and responsive environment for exploring complex point cloud data. Key features include customizable visual properties, interactive selection, and robust state management, all accessible directly within a web browser, or virtual reality headset, if available.

Recent innovations in 3D reconstruction and the rise in popularity of Digital Twins present a unique opportunity for the integration of the two technologies for high quality data visualization. In this work, I present an architecture for integrating Gaussian Splat reconstructions with dynamic data and user interaction, implemented in Unreal Engine. I also explore the application of this method to digital twin creation and how it addresses specific visualization challenges.

We present a multi-GPU extension of the 3D Gaussian Splatting (3D-GS) pipeline for scientific visualization. Building on previous work that demonstrated high-fidelity isosurface reconstruction using Gaussian primitives, we incorporate a multi-GPU training backend adapted from Grendel-GS to enable scalable processing of large datasets. By distributing optimization across GPUs, our method improves training throughput and supports high-resolution reconstructions that exceed single-GPU capacity. In our experiments, the system achieves a 5.6× speedup on the Kingsnake dataset (4M Gaussians) using four GPUs compared to a single-GPU baseline, and successfully trains the Miranda dataset (18M Gaussians) that is an infeasible task on a single A100 GPU. This work lays the groundwork for integrating 3D-GS into HPC-based scientific workflows, enabling real-time post hoc and in situ visualization of complex simulations.

General Experimentation and Natural Interface Utility System (GENIUS), is an AI personal assistant specifically tailored for scientists engaged in experimentation and research. GENIUS integrates Large Language Models (LLM), with immersive mixed reality (XR) capabilities. Through natural speech recognition, users can interact effortlessly with GENIUS to ask questions, visualize and manipulate complex 3D models, and execute computational jobs on Argonne Leadership Computing Facility (ALCF) supercomputers.

Mixed reality (MR) enables immersive, intuitive engagement with scientific data. When paired with AI-driven assistants, it has the potential to transform traditional workflows. In this paper, we introduce a modular agentic architecture for scientific visualization in MR, designed to balance general-purpose flexibility with domain-specific extensibility. Our modular architecture supports composable tools, contextual reasoning, and dynamic task execution. We outline a three-layer design, domain module integration, and orchestration of multistep workflows. We demonstrate the system’s capabilities through use cases in biology and general-purpose scientific visualization, including protein interaction networks and remote ParaView-based rendering. The result is a flexible and extensible foundation for spatial scientific computing.