Sam Budd, Phd Email & Phone Number

London, Gb

London Area, United Kingdom

At Fraiya Ltd, our mission is to revolutionize the field of ultrasound through cutting-edge artificial intelligence technology. We are committed to developing innovative products and services that empower healthcare professionals, improve care for patients, and enhance the efficiency of health services. Our primary goal is to provide AI tools that improve medical diagnosis while streamlining the experience for patients, clinicians, and healthcare providers.

London, England, United Kingdom

Leading the translational efforts to bring fetal screening research into the clinic through AI and Machine Learning tools, Medical Image Analysis and Full-stack Developement

London, United Kingdom

Thesis: 'Symbiotic Deep Learning for Medical Image Analysis with Applications in Real-Time Diagnosis for Fetal Ultrasound Screening'2021 - 'Can Non-specialists Provide High Quality Gold Standard Labels in Challenging Modalities?'. FAIR'212021 - 'Detecting Hypo-plastic Left Heart Syndrome in Fetal Ultrasound via Disease-Specific Atlas Maps.' In: MICCAI'212020 - ‘Surface Agnostic Metrics for Cortical Volume Segmentation and Regression’, Best Paper Honourable Mention, Machine Learning in Clinical Neuroimaging in Conjunction with MICCAI’20. A novel deep learning method for extraction of cortical segmentations and metrics from whole-brain MRI images.2019 - ‘Confident Head Circumference Measurement from Ultrasound with Real-Time Feedback for Sonographers’, International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI’19. A novel method to provide visual feedback to sonographers in real-time for automated biometric measurements in fetal screening.2019 - ‘A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysis’, arXiv:abs/1908.02582. Survey paper covering state-of-the-art techniques in Active Learning, automated prediction interpretation and refinement, Human-in-the-loop computing, practical considerations and related areas of research.2017 - ‘The Cortical Explorer: A Web-based User-interface for the Exploration of the Human Cerebral Cortex’, Eurographics Workshop on Visual Computing for Biology and Medicine, EG VCBM

Mentor for the Astronaut Health Team as part of the Frontier Development Lab USA, a co-operative agreement between NASA and the SETI Insititute. I was a member of the faculty team leading the ideation and project management for the Astronaut Health project titled Space Medic: Causal Inference for out-of-distribution generalization. Successful project outcomes include a robust platform for causal inference developed on the Intel OpenFL platform and several NeurIPS papers submitted to relevant workshops. Collaboration between the Mayo Clinic and NASA GeneLab has enabled deeper insights into radiation exposure impacts on humans and mice in space.

Lead Machine Learning engineer for the ML4CC (Machine Learning for Climate Change) MLOps toolkit development project (Part of the SpaceML organisation). Responsible for engineering ML pipelines to train flood detection models taking satellite imagery and producing flood segmentation maps. Responsible for integrating existing and new models with MLOps and Cloud Computing Tools for rapid development and deployment of global flood segmentation models. Official time with the project has ended but I provide occasional support to teams as UNICEF who have taken on development and hosting of the project.

Machine Learning architecture consultant for a sports betting computer vision project. Developed several cost-sensitive approaches to build a real-time ML based solution for one of the world’s largest sports betting companies. Successful pitch and client workshop resulted in the client moving forward with proposed solutions and the project is ongoing.

Researcher at Frontier Development Lab USA, a co-operative agreement between NASA and the SETI Institute. A part of the Astronaut Health Team developing CRISP: The Causal Relation and Inference Search Platform, an ensemble of causal machine learning methods to discover causal relationships in heterogenous, high dimension, low sample size biological data such as cancer genomics and microbiome contents.

London, United Kingdom

Internal Java project to provide automated data synchronisation between Zuhlke’s ERP and CRM systems. Client project writing Swift iOS app update for smart home solutions company. Two work experience students placed under my supervision.

London, United Kingdom

Worked in an Agile development team for the User-Centric Computing business unit following TDD principles. Internet of Things web/ mobile application in Javascript/ES6 and Unity3D to create an interactive 3D UI of the office tracking employee desk locations, meeting room occupancy and air quality, extending to live tracking of employee location within the office.

Master’S Degree, Computing, First Class Honours

Distinguished Project (Master's Project). Topics group project prize winner 2014. Business and Management for Computing Engineers business.

Master Of Research - Mres, Medical Imaging Sciences, Pass With Distinction

Study of Active Learning and Deep Learning enabled web annotation tools. Attended the 2019 Medical Imaging Summer School and presented.

Education record

A* Mathematics A Further Mathematics A Physics A* 3D Design Esher College Battle of the Bands winner 2012 Surrey's Got Talent winner 2012