Salvatore Esposito
I'm a Researcher & Engineer working in autonomous robotic navigation of agents in real world environments, 3D Vision, and Generative AI. I earned my PhD in Geometric Deep Learning from the University of Edinburgh's CDT in Biomedical AI, where I worked under the supervision of Arno Onken, Oisin Mac Aodha, and Changjian Li. My research has been published at leading venues including CVPR, advancing the fields of Generative AI and 3D reconstruction. I've contributed to Microsoft's HoloLens and Teams Avatars initiatives, translating complex technical concepts into user-centered AR/VR experiences. My strength lies in bridging research innovation with practical applications, effectively collaborating across engineering, design, and business teams. I combine academic rigor with hands-on development to align technical possibilities with product vision, while fostering collaborative environments that accelerate R&D and drive strategic growth.
Interests
- Robotics and Navigation
- Systems Integration & Roadmapping
- Generative AI & Computer Graphics
- Data Engineering & Feature Pipelines
Education
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PhD in Geometric Deep Learning (Biomedical AI CDT) University of Edinburgh
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MRes in Artificial Intelligence University of Edinburgh
Professional Experience
- Developing novel navigation algorithms for robotic systems for minimally invasive procedures to enhance surgical precision and safety
- Leading interdisciplinary research team in creating AI-powered navigation solutions for autonomous surgical interventions
- Led the enhancement of the HMR 2.0 multimodal transformer pipeline for avatars
- Improved motion & audio prediction for more realistic avatar interactions
- Built GAN- and NeRF-based generative models with enhanced surface fidelity
- Proposed a neural rendering architecture for high-quality facial mesh extraction
- Time-series forecasting for CPU utilization in virtual machines
- Designed predictive resource-management solutions
- Integrated NeRFs with caching for improved rendering quality
- Applied ML/AI & statistical methods for financial modelling
- Optimized HPC & cloud pipelines for large-scale data analysis
Publications
VesselSDF: Distance Field Priors for Vascular Network Reconstruction
We propose VesselSDF, a novel approach for reconstructing a 3D signed-distance field from 2D cross-sections…
GeoGen: Geometry-Aware Generative Modeling via Signed Distance Functions
We introduce a new generative approach for synthesizing 3D geometry and images from single-view collections…