Jose聽Andreotti
Data Scientist
Yemen
Summary
馃ぉ
Rockstar馃帗
Top SchoolJose Andreotti is a seasoned Data Scientist and senior AI engineer with over a decade of hands-on experience and more than 20 years in software engineering, currently shaping ML solutions at John Snow Labs. He blends deep systems knowledge鈥攆rom low-level C/C++ optimization and embedded development to distributed Scala/Akka backends鈥攚ith practical data science tooling and production ML work. His contributions to a well-known deep learning toolbox show expertise in performance optimization (FFT-based convolutions) and cross-platform bug fixes, plus experience in audio feature acquisition (MFCC). Prior roles at Intel, Samsung and Motorola highlight a track record building big-data infrastructures, context-aware systems, and recommender engines. Based in Yemen, he brings a global, cross-domain perspective that spans research, industry, and open-source collaboration. Colleagues rely on him for pragmatic solutions that bridge algorithmic depth and production-grade engineering.
12 years of coding experience
10 years of employment as a software developer
Computer Engineer, Engineering., Computer Engineer, Engineering. at Universidad Nacional de C贸rdobaUniversidad Nacional de La Plata
German, Italian, English, Spanish
Stackoverflow
512reputation
44kreached
25answers
2questions
Github Skills (22)
fftw10
convolutional-neural-networks10
matlab10
octave10
fft10
deep-learning10
neural-network10
algorithm9
code-optimization9
algorithms9
optimisation9
numerical-optimization9
optimization9
data-acquisition8
nlp6
Programming languages (7)
Github contributions (5)
rasmusbergpalm/DeepLearnToolbox
Jul 2013 - Nov 2013
Matlab/Octave toolbox for deep learning. Includes Deep Belief Nets, Stacked Autoencoders, Convolutional Neural Nets, Convolutional Autoencoders and vanilla Neural Nets. Each method has examples to get you started.
Role in this project:
Backend Developer
Backend DeveloperContributions:21 commits in 4 months
Contributions summary:Jose primarily contributed to the backend of the deep learning toolbox, focusing on addressing platform-specific bugs and optimizing the convolution operations. This involved creating wrapper functions for the `convn` function to overcome limitations in Octave, and implementing an FFT-based convolution to improve performance. Their work extended to modifying the core `cnnbp` function to integrate these changes. Additionally, the user added a script to fetch MFCC data, demonstrating an understanding of data acquisition and processing for the project's domain.
autoencodersneural-netsoctavetensorflowstacked
albertoandreottiATgmail/PLN-2015
Aug 2015 - Aug 2016
Contributions:32 pushes, 6 branches in 1 year
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