Jhonatan Oliveira

PhD Student

University of Regina

About

I am a Brazilian who, in December 2019, completed all degree requirements for my Ph.D. in Artificial Intelligence (AI) at the University of Regina in Canada. I completed my undergrad in Electrical Engineering at the Universidade Federal de Viçosa in Brazil, one of the best universities in the country. I have been awarded numerous scholarships worth over $100K CDN in total. My graduate research focused on probabilistic graphical models such as Bayesian networks (BNs) and deep learning models, including sum-product networks (SPNs). I have been active in research since 2013. I have published 25 peer-reviewed papers, including one paper at AAAI-2019. The interest in this paper led to a formal invitation to visit the University of Cambridge for three months. I was awarded the 2019 Best Doctoral Dissertation Award by the Canadian Artificial Intelligence Association (CAIAC).

Please, see my resume or cover letter for more details.

Interests

Artificial Intelligence
Deep Learning
Probabilistic Graphical Models

Education

PhD in Computer Science, 2019

University of Regina
MSc in Computer Science, 2016

University of Regina
BSc in Electrical Engineering, 2014

Universidade Federal de Viçosa

Skills

Research

Machine Learning

Bayesian Statistics

Web Development

Python

Java

C++

JavaScript

Coffee

Publications

Refereed Conference Papers

C.J. Butz, J.S. Oliveira, A. dos Santos, A.L. Teixeira (2019). Deep Convolutional Sum-Product Networks. Thirty-Third AAAI Conference on Artificial Intelligence (AAAI).

A. dos Santos, J.S. Oliveira, C.J. Butz, A. L. Madsen (2019). Exploiting Symmetry of Independence in d-Separation. Thirty-second Canadian Conference on Artificial Intelligence (AI).

C. J. Butz, A. L. Teixeira, J. S. Oliveira, and A. dos Santos (2019). On the Tree Structure of Deep Convolutional Sum-Product Networks. Thirty-Second International Florida Artificial Intelligence Research Society Conference (FLAIRS).

C.J. Butz, J.S. Oliveira, A.L. Madsen, A. dos Santos (2019). Solving Influence Diagrams with Simple Propagation. Thirty-second Canadian Conference on Artificial Intelligence (AI).

C.J. Butz, J.S. Oliveira, A. dos Santos, A.L. Teixeira, P. Poupart, A. Kalra (2018). An Empirical Study of Methods for SPN Learning and Inference. Ninth International Conference on Probabilistic Graphical Models (PGM).

C.J. Butz, A. dos Santos, J.S. Oliveira, J. Stavrinides (2018). Efficient Examination of Soil Bacteria using Probabilistic Graphical Models. Thirty-first International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems.

DOI

A.L. Madsen, C.J. Butz, J.S. Oliveira, A. dos Santos (2018). Simple Propagation with Arc-Reversal in Bayesian Networks. Ninth International Conference on Probabilistic Graphical Models (PGM).

A. dos Santos, C.J. Butz, J.S. Oliveira (2017). On Converting Sum-Product Networks into Bayesian Networks. Thirtieth Canadian Conference on Artificial Intelligence (AI).

DOI

C.J. Butz, A. dos Santos, J.S. Oliveira (2017). On Finding Relevant Variables in Discrete Bayesian Network Inference. Thirtieth International Florida Artificial Intelligence Research Society Conference (FLAIRS).

C.J. Butz, J.S. Oliveira, A.E. dos Santos (2017). On Learning the Structure of Sum-Product Networks. IEEE Symposium Series on Computational Intelligence.

DOI

J.S. Oliveira, C.J. Butz, A. dos Santos (2017). Resolving Inconsistencies of Scope Interpretations in Sum-Product Networks. Thirtieth Canadian Conference on Artificial Intelligence (AI).

DOI

C.J. Butz, J.S. Oliveira, A. dos Santos, A.L. Madsen (2016). Bayesian Network Inference with Simple Propagation. Twenty-ninth International Florida Artificial Intelligence Research Society Conference (FLAIRS).

C.J. Butz, J.S. Oliveira, A.E. dos Santos, A.L. Madsen (2016). On Bayesian Network Inference with Simple Propagation. Eighth International Conference on Probabilistic Graphical Models (PGM).

A.L. Madsen, C.J. Butz, J.S. Oliveira, A. dos Santos (2016). On Tree Structures used by Simple Propagation for Bayesian Networks Inference. Twenty-ninth Canadian Conference on Artificial Intelligence (AI).

C.J. Butz, A.E. dos Santos, J.S. Oliveira (2016). Relevant Path Separation: A Faster Method for Testing Independencies in Bayesian Networks. Eighth International Conference on Probabilistic Graphical Models (PGM).

C.J. Butz, A. dos Santos, J.S. Oliveira, C. Gonzales (2016). Testing Independencies in Bayesian Networks with i-Separation. Twenty-ninth International Florida Artificial Intelligence Research Society Conference (FLAIRS).

C.J. Butz, J.S. Oliveira, A. dos Santos (2015). Darwinian Networks. Twenty-eighth Canadian Conference on Artificial Intelligence (AI).

C.J. Butz, J.S. Oliveira, A. dos Santos (2015). Determining Good Elimination Orderings with Darwinian Networks. Twenty-eighth International Florida Artificial Intelligence Research Society Conference (FLAIRS).

A.L. Madsen, C.J. Butz (2015). Exploiting Semantics in Bayesian Network Inference Using Lazy Propagation. Twenty-eighth Canadian Conference on Artificial Intelligence (AI).

DOI

C.J. Butz, J.S. Oliveira, A.L. Madsen (2014). Bayesian Network Inference Using Marginal Trees. Seventh European Workshop on Probabilistic Graphical Models (PGM).

Publications

Refereed Journals Papers

C.J. Butz, J.S. Oliveira, A. dos Santos, A.L. Madsen (2018). An Empirical Study of Bayesian Network Inference with Simple Propagation.. International Journal of Approximate Reasoning.

DOI

C.J. Butz, A. dos Santos, J.S. Oliveira, C. Gonzales (2018). An Empirical Study of Testing Independencies in Bayesian Networks using rp-Separation.. International Journal of Approximate Reasoning.

DOI

C.J. Butz, A. dos Santos, J.S. Oliveira, C. Gonzales (2018). On a Simple Method for Testing Independencies in Bayesian Networks.. Computational Intelligence.

DOI

C.J. Butz, J.S. Oliveira, A. dos Santos (2017). On Darwinian Networks.. Computational Intelligence.

DOI

C.J. Butz, J.S. Oliveira, A.L. Madsen (2016). Bayesian Network Inference using Marginal Trees.. International Journal of Approximate Reasoning.

DOI

Jhonatan Oliveira

PhD Student

About

Interests

Education

Skills

Research

Machine Learning

Bayesian Statistics

Web Development

Python

Java

C++

JavaScript

Coffee

Publications

Publications

Selected Experience

Senior Software Engineer

Teaching Assistant

Researcher

Software Engineer Intern

Projects

Accomplish­ments

Convolutional Neural Networks

Coursera

Structuring Machine Learning Projects

Coursera

Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

Coursera

Neural Networks and Deep Learning

Coursera

Probabilistic Graphical Models 1: Representation

Coursera

Build a Modern Computer from First Principles: From Nand to Tetris (Project-Centered Course)

Coursera

Machine Learning

Coursera

Data Mining with Weka

University of Waikato

Contact

Accomplishments