Automatic Segmentation of Battery Cells of Electric Vehicles
Automatic detection and segmentation of battery cells of Electric Vehicles.
Professional Summary
About Me
Computer vision researcher and ML engineer with 15+ years of experience, from publishing in top medical imaging journals to deploying production AI systems in medical diagnostics, drone delivery, and industrial inspection.
PhD from Radboud University Medical Center (world’s #2-ranked ML lab for medical imaging), with research cited over 2600 times on Google Scholar. Winner of Brazil’s national COVID-19 CT segmentation challenge. Currently working as Senior AI Researcher at AstraZeneca and ML Consultant at Deep Design Systems.
Proven track record taking projects end-to-end: from requirements and R&D through training, optimisation, and deployment on embedded hardware (Jetson AGX Xavier, TensorRT, Docker). Expertise spans medical imaging, autonomous drone vision, diffusion models, NeRFs, and LLM fine-tuning.
Education
PhD in Machine Learning for Medical Imaging
Radboud University Medical Center
MSc in Computer Science (Computer Vision)
University of Sao Paulo -
BSc (Hons) in Computer Science
National University of San Agustin
Interests
Computer Vision
Deep Learning
Medical Imaging
Autonomous Systems
Production ML Deployment
Experience
Senior AI Researcher
AstraZenecaApplying deep learning and computer vision to pharmaceutical research and drug discovery pipelines.Senior ML Researcher
Deep Design SystemsDeveloped AI pipelines for 3D product generation from text using diffusion models and neural radiance fields (NeRFs). Implemented multi-view neural rendering to create photorealistic 3D environments and improved pipeline efficiency.Technical Project Manager of Computer Vision
EmbentionLed an 8–10 member R&D team to develop an autonomous drone-delivery vision system, achieving a 30% reduction in detection latency through algorithm optimisation. Recovered a project with a 5-month delay risk, delivering on time without scope reduction. Reported directly to CEO and COO.Senior Data Scientist
Instech Netherlands B.V.Applied state-of-the-art Deep Learning end-to-end: from prototype to optimisation and production deployment. Developed 2D and 3D CT scan algorithms for classification, regression, and segmentation. Deployed models using Docker, TensorRT, and ONNX on embedded hardware including Jetson AGX Xavier.Mentor of LiveProjects
Manning Publications Co.Guided 10 students to develop Deep Learning projects from data collection, training, testing, and final reporting.Teaching Assistant
Radboud UniversityTeaching assistant for the Intelligent Systems in Medical Imaging course for Master students. Supervised a Master student’s graduation project.Team Leader & Software Developer
BTC & Motorola MobilityCreated and led a 5-person Android Automation team. Delivered Google acceptance tests 80% faster and 200% more precise than previous approaches. Developed a Macbeth ColorChecker algorithm for the Computer Vision team of Motorola Mobility & Lenovo.Computer Vision Consultant
OrkaPod Inc.Reduced the false positive rate of a face recognition algorithm using OpenCV.Researcher and Developer in Computer Vision
PCT Consulting Sao Paulo - New YorkEngineered a real-time video surveillance prototype to detect and track individuals using Computer Vision.Assistant Professor and Researcher
San Pablo Catholic UniversityAssistant professor and researcher in Machine Learning and Computer Vision.Developer and Junior Researcher
Cathedra CONCYTEC UNSADeveloped feature extraction algorithms using computer vision. Results published in 3 scientific papers.
Education
PhD in Machine Learning for Medical Imaging
Radboud University Medical CenterThesis on Deep Learning for Localization and Segmentation in Thorax Abdomen CT. Lab ranked #2 in the world for ML applied to medical imaging. Research contributed to publications cited 2600+ times.Read ThesisMSc in Computer Science (Computer Vision)
University of Sao Paulo -Dissertation on Supervised Feature Selection by Ranking to Process Similarity Queries in Medical Imaging. Completed 4 months ahead of deadline.Read DissertationBSc (Hons) in Computer Science
National University of San AgustinDissertation on Feature Extraction and Distance Function Selection to Retrieve Microscopic Images of Parasites.
Skills
Computer Vision & Imaging
OpenCV
SimpleITK
TensorRT
ONNX
Deep Learning
Python
TensorFlow
Keras
PyTorch
Engineering & DevOps
C++
Docker
Git
CMake
Data & Analysis
NumPy
Data Science
Scikit-Learn
SQL
Selected Publications
Fully Automatic Volume Measurement of the Spleen at CT Using Deep Learning
Automatic spleen segmentation using deep learning. This method reaches radiologist performance.
Deep Learning for Localization and Segmentation in Thorax Abdomen CT
Deep Learning for Localization and Segmentation in Thorax Abdomen CT.
Efficient organ localization using multi-label convolutional neural networks in thorax-abdomen CT scans
Automatic organ localization using Deep Learning.
All Publications
(2023).
Transfer learning from a sparsely annotated dataset of 3D medical images.
arXiv, arXiv:2311.05032.
(2023).
Kidney abnormality segmentation in thorax-abdomen CT scans.
arXiv, arXiv:2309.03383.
(2023).
The Liver Tumor Segmentation Benchmark (LiTS).
Medical Image Analysis 84, 102680.
(2020).
Fully Automatic Volume Measurement of the Spleen at CT Using Deep Learning.
Radiology: Artificial Intelligence, 2020;2(4):e190102.
Recent Posts
End-to-End Computer Vision: From Requirements to Deployed System
Reflections on 15 years taking CV systems from research prototype to production - what the gap really looks like, and how to close it.
From Research to Production: Deploying Deep Learning on Embedded Hardware
Practical lessons from deploying TensorFlow and PyTorch models to TensorRT on Jetson AGX Xavier - quantisation, latency trade-offs, and ONNX conversion.
Winning Brazil's National COVID-19 AI Challenge
How I built an automated CT scan segmentation system that won 1st place in Brazil's government-sponsored national AI competition.
News & awards
Winner of the Brazilian COVID-19 Detection/Segmentation Challenge
Government of the State of São Paulo / Brazil ∙
August 2020
Won 1st place in Brazil’s national COVID-19 CT scan segmentation challenge, beating 30 teams. The winning system was interviewed by the University of São Paulo and was one of two finalists in the IdeiaGov innovation challenge.
Languages
100%
Spanish
100%
English
100%
Portuguese
10%
Dutch