Salvatore Esposito

Salvatore Esposito

Machine Learning Researcher

Microsoft & University of Edinburgh

Biography

I am pursuing a Ph.D. in 3D Vision and Generative AI at the University of Edinburgh, deepening my understanding of these advanced fields. As a Visiting Researcher at Microsoft Mesh in Zurich, I blend 3D Vision, motion prediction, and graphics to improve mixed reality experiences. My previous role at Microsoft Research Cambridge involved applying computer vision and signal processing to biomedical research. Passionate about cloud infrastructure and Kubernetes, I also contributed at Huawei Research UK, where I used time series forecasting models to enhance system optimization and operational efficiency.

Interests
  • Signal processing
  • 3D Reconstruction
  • Cloud computing
Education
  • PhD in Artificial Intelligence, 2025

    University of Edinburgh

  • MRes in Artificial Intelligence, 2021

    University of Edinburgh

  • MSc in Bioinformatics, 2020

    University of Glasgow

Professional Experience

 
 
 
 
 
Visiting Researcher
Microsoft
October 2022 – June 2024 Zurich
Led the enhancement of the HMR 2.0 pipeline, focusing on the development of a multimodal transformer for avatars. Specialized in predicting motion and audio, significantly improving the realism and responsiveness of avatar interactions.
 
 
 
 
 
Research Intern
Huawei
February 2023 – August 2023 Edinburgh
Specialized in Systems Infrastructure Research with a focus on time series forecasting, predictive models for CPU utilization in VMs, significantly enhancing efficiency and performance.
 
 
 
 
 
Research Intern
Microsoft
June 2022 – October 2022 Cambridge
Developed and refined generative models, including GANs and neural radiance fields, focusing on enhanced surface representation quality. Innovated a novel generative model using a neural rendering architecture for improved extraction of surfaces from human facial meshes.
 
 
 
 
 
Quantitative Researcher
American Express
April 2022 – June 2022 London
Applied and advanced machine learning/AI and statistical learning methods for quantitative financial analysis. Utilized cloud and high-performance computing platforms to optimize data processing and analysis workflows.

Awards

CDT Doctoral Scholarship in Biomedical Artificial Intelligence

Contact

Feel free to reach out