Pranav Reddy Sambidi is a Master’s student in Artificial Intelligence Systems at the University of Florida with interests in AI/ML, computer vision, generative AI, and full-stack development. His experience spans agentic AI, retrieval-augmented generation, deep learning, image processing, and automation, including work with UF/IFAS Extension developing AI agents and data-driven solutions. His research background includes disruptive technologies, autonomous systems, and emerging computing paradigms. Through the NVIDIA-sponsored Anatomy-to-Asset project, Pranav is particularly interested in applying medical image segmentation, 3D reconstruction, and OpenUSD workflows to transform anatomical imaging data into accurate, simulation-ready digital assets for next-generation applications.

Andrew Dodds is a second year Master’s student in Applied Data Science at the University of Florida. His primary focus is improving patient outcomes through computer vision, deep learning, or data driven solutions. He has a background in Biomedical Engineering spending his time as an undergraduate working in UF’s CSSALT lab with mixed reality and ultrasound simulation. Andrew is interesting in building the final stages of the pipeline and publishing our research to further progress in robotic surgery.

Harshal Anil Patel is a Master’s student in Artificial Intelligence at the University of Florida with interests in medical image analysis, deep learning, and HPC-scale AI systems. He brings three years of software engineering experience at Temenos and Accenture, building high-throughput Python microservices, data pipelines, and real-time ML inference systems. As a Graduate Research Assistant with UF Health, he builds analytics pipelines over EHR data to improve patient safety. Through the NVIDIA-sponsored Anatomy-to-Asset project, Harshal has deployed a GPU-accelerated TotalSegmentator pipeline on UF’s HiPerGator supercomputer, segmenting 100+ anatomical structures from CT scans on NVIDIA Blackwell GPUs, and is focused on transforming these segmentations into simulation-ready 3D assets through mesh reconstruction and OpenUSD workflows.

Chanakya Vasantha is a Master’s student in Artificial Intelligence Systems at the University of Florida with interests in agentic AI, generative AI, multimodal systems, computer vision, and full-stack AI development. His experience spans voice-based AI agents, retrieval-augmented generation, LLM evaluation, cloud deployment, and human-AI interaction through research at UF and UF Health. He has developed production AI systems including PHIL and AI-powered educational platforms, with work involving LiveKit, large language models, AWS, and real-time multimodal interfaces. Through the NVIDIA-sponsored Anatomy-to-Asset project, Chanakya is particularly interested in applying medical image segmentation, 3D reconstruction, NVIDIA Isaac Sim, and OpenUSD workflows to transform anatomical imaging data into accurate, physics-ready digital assets for robotic ultrasound and healthcare simulation.

Megha Suhanth Royal Sarvepalli is a Master’s student in Artificial Intelligence Systems at the University of Florida with interests in machine learning, computer vision, deep learning, and generative AI. His previous work includes medical image classification, image segmentation, and retrieval-augmented generation, with published research involving deep learning for biomedical imaging and real-world image segmentation. Through the NVIDIA-sponsored Anatomy-to-Asset project, Megha is focused on investigating methods for transforming medical image segmentations into accurate, high-quality, simulation-ready 3D meshes, with particular interest in mesh generation, geometric quality assessment, and preparing anatomical assets for OpenUSD and NVIDIA Isaac Sim workflows.

Adithya Vardhan Selvam is a Master’s student in Artificial Intelligence Systems at the University of Florida with interests in AI/ML, deep learning, computer vision, medical imaging, generative AI, and digital twin technologies. His experience includes developing and evaluating deep learning models for medical image analysis, working with CNNs, ResNet-based architectures, attention mechanisms, transformers, and time-series models, as well as deploying workflows on HiPerGator and Linux environments. His research background includes pancreatic cancer detection, brain tumor analysis, and other AI-driven healthcare applications. Through the NVIDIA-sponsored Anatomy-to-Asset project, Adithya is particularly interested in medical image segmentation, 2D-to-3D anatomical reconstruction, mesh generation and repair, and OpenUSD-based pipelines for creating accurate, simulation-ready digital assets from medical imaging data.