Matteo Boschini
Chief AI Officer at Symboolic
Emilia-Romagna, Italy
Summary
👤
Senior🎓
Top SchoolMatteo Boschini is a Chief AI Officer and computer engineer with nine years of experience specializing in computer vision, deep learning, and VR, who transitioned from co-founding a VR studio to leading AI strategy and research-driven productization. He holds a PhD in Deep Learning and has combined academic rigor with hands-on engineering—contributing bug fixes and architecture improvements to a prominent continual learning PyTorch codebase (aimagelab/mammoth). Based in Emilia-Romagna, Italy, Matteo has moved quickly through roles from postdoctoral research to head of AI and now executive leadership, evidencing both technical depth and organizational impact. He’s comfortable across research, production ML pipelines, and immersive tech, and brings a practical eye for robustness and dataset tooling informed by his open-source contributions.
9 years of coding experience
5 years of employment as a software developer
Master's degree, Computer Engineering (Artificial Intelligence curriculum), Master's degree, Computer Engineering (Artificial Intelligence curriculum) at Alma Mater Studiorum – Università di BolognaMaturità Classica, PNI, Maturità Classica, PNI at Liceo Classico L. A. Muratori, ModenaDoctor of Philosophy - PhD, Deep Learning, Doctor of Philosophy - PhD, Deep Learning at Università degli Studi di Modena e Reggio Emilia
English, Italian, German
Github Skills (13)
pytorch10
machine-learning10
deep-learning10
python10
modeling9
model-driven-development9
model-driven9
model-building9
distill8
data-set8
datasets8
data-management8
dis8
Programming languages (6)
Github contributions (5)
aimagelab/mammoth
Nov 2020 - Dec 2022
An Extendible (General) Continual Learning Framework based on Pytorch - official codebase of Dark Experience for General Continual Learning
Role in this project:
ML Engineer
ML EngineerContributions:28 commits, 4 PRs, 29 pushes in 2 years 2 months
Contributions summary:Matteo primarily contributed to bug fixes and code improvements within the continual learning framework. They addressed issues in training scripts, including missing callbacks and minibatch size configurations. Moreover, they implemented fixes related to model architectures, such as missing ReLU activations and return statements. Their work also included updates to dataset loading and management, specifically fixing MNIST downloads and integrating a OneDrive download for the TinyImagenet dataset. Furthermore, they modified the XDer continual learning model, including fixes and code cleanup.
pytorchknowledge-distillationdeep-learningdercontinual-learning
mbosc/scartch
Jan 2017 - May 2019
Contributions:164 commits, 1 push, 1 branch in 2 years 4 months
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