Emanuele Olivetti
Quantitative Research & Development Lead
Abu Dhabi, Abu Dhabi Emirate, United Arab Emirates
Summary
👤
Senior🎓
Top SchoolEmanuele Olivetti is a Quantitative Research & Development Lead at ADIA with 15 years of experience bridging academic research and production quantitative systems. He holds a PhD in Computer Science and a summa cum laude M.Sc. in Physics from Università di Trento, and maintains an active research role at Fondazione Bruno Kessler while lecturing on machine learning for neuroimaging. His background spans embedded and adaptive systems, scientific Python, and advanced C++, and he has led performance-sensitive implementations—evidenced by his optimized Cython contribution to the widely used DIPY medical imaging library. At ADIA he applies rigorous, research-driven methods to quantitative investing problems, translating complex algorithms into reliable, tested code. Known for combining deep domain knowledge with pragmatic engineering, he often surfaces algorithmic improvements that yield measurable speed and accuracy gains.
15 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Università di Trento
English, French, Chinese
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (6)
python10
cython10
numpy10
testing9
medical-imaging9
machine-learning8
Programming languages (7)
Github contributions (5)
dipy/dipy
Aug 2010 - Nov 2011
DIPY is the paragon 3D/4D+ medical imaging library in Python. Contains generic methods for spatial normalization, signal processing, machine learning, statistical analysis and visualization of medical images. Additionally, it contains specialized methods for computational anatomy including diffusion, perfusion and structural imaging.
Role in this project:
Data Scientist
Data ScientistContributions:5 commits, 1 comment in 1 year 3 months
Contributions summary:Emanuele contributed significantly to the `dipy` repository, focusing on the computation of distance matrices between bundles of fiber tracks, a core functionality in diffusion MRI analysis. They implemented both the standard and a faster, optimized version of this computation, utilizing Cython for performance gains. Furthermore, the user added unit tests to ensure the correctness of the distance matrix calculations. Their work primarily involved Python and related scientific libraries, with a direct impact on the efficiency and reliability of tractography analysis within the project.
signalpythonmicrostructurespatialtractography
emanuele/kernel_two_sample_test
Jan 2013 - Sep 2015
Contributions:19 commits, 4 pushes in 2 years 8 months
kernelpython-implementationjmlrpython
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