Gabriele Cesa
Associate Researcher
Amsterdam, North Holland, Netherlands
Summary
👤
Senior🎓
Top SchoolGabriele Cesa is an Associate Researcher at Qualcomm AI Research and a PhD candidate in Artificial Intelligence at the University of Amsterdam, bringing a decade of experience at the intersection of deep learning research and applied ML. He holds an MSc in AI from UvA and a distinguished Computer Science degree from the University of Trento, and has taught and contributed to the MSc Deep Learning course as a teaching assistant. His work spans steerable CNNs and representation-theoretic approaches—evidenced by a documented tutorial contribution to the widely used UvA deep learning notebooks—bridging theoretical insights with practical implementations. Comfortable in both academic and industrial research settings, he combines rigorous mathematical grounding with hands-on engineering to push model expressivity and equivariance. Outside research, he pursues interests in science and martial arts, reflecting a disciplined, curiosity-driven approach to problem solving.
10 years of coding experience
Bachelor's Degree, Computer Science, 110/110 (with honors), Bachelor's Degree, Computer Science, 110/110 (with honors) at University of Trento (Italy)Master of Science - MSc, Artificial Intelligence, Master of Science - MSc, Artificial Intelligence at University of AmsterdamScientific High School, 100/100, Scientific High School, 100/100 at E. Fermi, Mantua
Italian, English
Github Skills (12)
mask-rcnn10
user-manual10
faster-rcnn10
pytorch10
jupyter-notebook10
deeplearning-ai10
deep-learning10
pytorch-lightning4
optim4
flax4
jax4
opt4
Programming languages (4)
Github contributions (5)
phlippe/uvadlc_notebooks
Apr 2022 - Apr 2022
Repository of Jupyter notebook tutorials for teaching the Deep Learning Course at the University of Amsterdam (MSc AI), Fall 2023
Role in this project:
ML Engineer
ML EngineerContributions:7 commits, 2 PRs, 14 comments in 1 day
Contributions summary:Gabriele's contributions primarily involve implementing and documenting a tutorial on Steerable CNNs within the context of a deep learning course. The commits demonstrate the integration and explanation of key concepts in representation theory and Fourier analysis relevant to the implementation of steerable CNNs. The changes include adding the tutorial with the required imports, explanations, and implementation.
jupyter-notebookflaxpytorch-lightningamsterdamfall
QUVA-Lab/escnn
Mar 2022 - Dec 2022
Equivariant Steerable CNNs Library for Pytorch https://quva-lab.github.io/escnn/
Contributions:149 commits, 12 PRs, 74 pushes in 9 months
pytorchgeometric-deep-learningdeep-learningcnnscomputer-graphics
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