Denis Wolf - Associate Professor

Denis Wolf

Associate Professor

São Carlos, São Paulo, Brazil

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Summary

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Denis Wolf is an Associate Professor and director of the Mobile Robotics Laboratory at ICMC/USP with 13+ years of experience building field-capable robotic systems and intelligent transportation solutions. He holds a PhD from USC and focuses on computer vision, sensor fusion and machine learning for terrestrial and aerial autonomy, with applied interests in urban, mining and agricultural automation. He combines academic leadership and hands-on system development, routinely translating research into deployed robotic platforms. Beyond robotics, his open-source contributions to flagship Python projects like scikit-learn, statsmodels and MNE demonstrate deep expertise in signal processing, statistical modeling and reproducible data science. Based in São Carlos, Brazil, he has complemented his research with visiting professorships in Europe, reflecting a global collaborative footprint.

13 years of coding experience

PhD, Computer Science/Robotics/Artificial Intelligence, PhD, Computer Science/Robotics/Artificial Intelligence at University of Southern California

University of São Paulo

BSc, Computer Science, BSc, Computer Science at Universidade Federal de São Carlos

English, French

Stackoverflow

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Github Skills (26)

spec10

python10

spectra10

data-science10

scikit10

statistics10

machine-learning10

econometrics10

spectrum10

spectral10

time-frequency10

numpy10

regression-models10

frequency-analysis10

scikit-learn10

Programming languages (15)

C++CSSCHolyCGoHTMLJupyter NotebookMATLAB

Github contributions (5)

mne-tools/mne-python

Aug 2012 - Oct 2021

MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python

Role in this project:

Backend Developer & Data Scientist

Contributions:61 reviews, 1899 commits, 200 PRs in 9 years 3 months

Contributions summary:Denis's commits primarily focus on enhancing and maintaining the functionality of the `mne-python` repository, which involves processing MEG and EEG data. They implemented new functionality to compute and analyse time-frequency representations (TFR) based on the Stockwell transform, including a multi-taper power spectral density (PSD) implementation and adding support for running TFR analysis. Furthermore, the commits demonstrate a strong understanding of data analysis and software development for signal processing and data analysis.

visualizationneuroimagingneurosciencepythonstatistics

scikit-learn/scikit-learn

Nov 2012 - Feb 2017

scikit-learn: machine learning in Python

Role in this project:

Data Scientist

Contributions:5 commits, 1 PR, 62 comments in 4 years 3 months

Contributions summary:Denis primarily contributed to the `scikit-learn` library, focusing on improvements and bug fixes related to the `decomposition` module, specifically the `FastICA` implementation. They addressed errors in custom function handling within `FastICA`, added tests, and updated documentation. Further, they performed refactoring and corrections to testing procedures within the `decomposition` module. The user's contributions also touched upon memory footprint reduction and updates in the documentation.

data-analysispythonstatisticsdata-sciencelearn-machine-learning

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