Nikolas Adaloglou
PHD Candidate
Dusseldorf, North Rhine-Westphalia, Germany
Summary
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Rockstar🎓
Top SchoolNikolas Adaloglou is a Human-Centered PhD AI researcher and machine learning engineer based in Düsseldorf with nine years of experience applying unsupervised and self-supervised deep learning to medical and visual domains. Currently a PhD candidate at the University of Düsseldorf, he develops representation learning, image clustering, OOD detection, and image synthesis methods with practical applications in medical imaging. He co-founded AI Summer to make deep learning accessible, growing it into a high-traffic resource with thousands of readers and extensive educational material. His hands-on engineering includes contributions to MedicalZooPytorch—implementing 3D UNet architectures, TensorBoard support, and full-volume segmentation pipelines—demonstrating strong expertise in model development and training infrastructure. Past roles include industry collaboration at Bayer on large-scale cell-painting pretraining and building multi-modal radiotherapy datasets, highlighting his ability to bridge research and production data challenges. Outside academia he has an analytical, results-driven background that even supported his studies through competitive online poker coaching and play.
9 years of coding experience
3 years of employment as a software developer
Master's degree (MSc) Biomedical Engineering, Master's degree (MSc) Biomedical Engineering at University of Patras
English, Greek, French, Spanish, German
Github Skills (8)
enet10
pytorch10
image-segmentation10
deep-learning10
segmentation10
resnet10
medical-image-segmentation10
tensorboard8
Programming languages (5)
Github contributions (5)
black0017/MedicalZooPytorch
Jul 2019 - Aug 2021
A pytorch-based deep learning framework for multi-modal 2D/3D medical image segmentation
Role in this project:
ML Engineer
ML EngineerContributions:3 reviews, 100 commits, 1 PR in 2 years 1 month
Contributions summary:Nikolas primarily contributed to the development of a PyTorch-based deep learning framework for medical image segmentation. Their commits demonstrate the implementation of various 3D convolutional neural networks, including a UNet3D architecture. They also added support for TensorBoard, full 3D segmentation volume generation, and experimentation with VAE-ResNet models, indicating a focus on model development and training pipelines.
medical-imageimage-segmentationunet-image-segmentationmedical-image-segmentationframework-learning
black0017/black0017
Jul 2020 - Mar 2024
Contributions:35 pushes, 1 branch in 3 years 8 months
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