Daniele Ciriello
Chief Technology Officer at Self-employed
Lombardy, Italy
Summary
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Rockstar🎓
Top SchoolDaniele Ciriello is a CTO and AI engineer with 14 years of experience building reliable, production-ready products at the intersection of deep learning and healthcare imaging from Lombardy, Italy. He leads technology strategy at DICOM Vision while consulting and running applied AI work through D/Vision Lab, translating research prototypes into robust software. A pythonista with an MSc in Computer Engineering, Daniele has hands-on experience optimizing PyTorch implementations for few-shot learning and has improved performance-critical components like batch samplers and loss functions. He blends academic rigor—earlier spatial data research at the University of Bergamo—with startup pragmatism, often shipping end-to-end ML systems and tooling. Outside work he’s a curious dad and doodler, traits that reflect a playful but disciplined approach to problem solving.
14 years of coding experience
4 years of employment as a software developer
High School Degree Electronics and Telecommunications, High School Degree Electronics and Telecommunications at ITIS G.Marconi, Gorgonzola (Milan Area, Italy)Master of Science (MSc) Computer Engineering, Master of Science (MSc) Computer Engineering at University of Bergamo (Bergamo Area, Italy)
Italian, English
Stackoverflow
5,070reputation
449kreached
29answers
6questions
selenium-webdriver
top-5%
selenium
top-5%
xpath
top-5%
Github Skills (13)
pytorch10
machine-learning10
python10
selenium9
selenium-webdriver9
xpath9
mask-rcnn9
faster-rcnn9
numpy7
key-event6
macos6
qt6
constructor6
Programming languages (12)
Github contributions (5)
orobix/Prototypical-Networks-for-Few-shot-Learning-PyTorch
Jan 2018 - Jun 2019
Implementation of Prototypical Networks for Few Shot Learning (https://arxiv.org/abs/1703.05175) in Pytorch
Role in this project:
ML Engineer
ML EngineerContributions:61 commits, 1 PR, 49 pushes in 1 year 4 months
Contributions summary:Daniele primarily contributed to the `prototypical-networks-for-few-shot-learning-pytorch` repository by modifying and improving the `prototypical_batch_sampler.py` and `prototypical_loss.py` files. They refactored the batch sampler to use PyTorch tensors instead of NumPy arrays and removed randomization. These changes indicate an effort to optimize the code for PyTorch and potentially improve performance. Further modifications included minor bug fixes and adding an experiment root for storing model data.
few-shot-learningpytorchpythoncnnprototypical-networks
dnlcrl/AlgorithmsAndDataStructures
Dec 2014 - Mar 2015
Contributions:22 commits, 11 pushes in 3 months
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