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{first name}souza{last name} at gmail dot com
I'm an independent researcher and engineer at 3778 Healthcare working with Machine Learning for Healthcare. Previously, I was Head of Data Science at Linx Impulse and a Researcher at the CERTI Foundation where I worked with Signal Processing and Embedded Systems.
I'm interested in Statistical/Computational Learning and Information Theory.
Originally, I am from Florianópolis, Brazil but I've lived in New Jersey, Orlando, Toronto and São Paulo as well as other smaller cities in the south of Brazil. I enjoy reading, playing american football and KSP
I'm interested in Statistical/Computational Learning and Information Theory.
Originally, I am from Florianópolis, Brazil but I've lived in New Jersey, Orlando, Toronto and São Paulo as well as other smaller cities in the south of Brazil. I enjoy reading, playing american football and KSP
Interests
- Statistical Learning
- Information Theory
- Privacy in Machine Learning
Education
-
Exchange Program, 2013
University of Toronto
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BSc in Electronics Engineering, 2015
Federal University of Santa Catarina
News
- 03/Dec/2019 - Proof of Novelty was awarded by Blockchain@UBC!
- 15/Nov/2019 - Finished writing Proof of Novelty.
- 21/Oct/2019 - Preprint of Ward2ICU posted on arXiv.
- 02/Oct/2019 - This page was created!
- 19/Sep/2019 - Claimed a paper for the Reproducibility Challenge @ NeurIPS 2019
Papers
- Severo, D., et al. “Ward2ICU: A Vital Signs Dataset of Inpatients from the General Ward.” arXiv preprint arXiv:1910.00752 (2019).
- Severo, D. “Proof of Novelty”. figshare preprint 10324883
Blog
- Exercises and Problems from Learning from Data: A Short Course (Problem 1.17)
- Mathematical proof of functionality, of a highly efficient pseudo-random number generator: The Ziggurat Method
- How can we keep the simplicity and power of pandas, while extending it to be out-of-core and parallel? Ad hoc Big Data Analysis with Dask
- An Introduction to Handling Time-series in Pandas and numpy
Open Source Contributions
- dask/Dask: Parallel computing with task scheduling
- dask/Dask-ML: Scalable Machine Learn with Dask
- 3778/Ward2ICU: Source code for arXiv preprint arXiv:1910.00752 (2019).
- 3778/ml-challenge: 3778’s Machine Learning Challenge.