Experience

4KST

Machine Learning Researcher & Co-Founder • January 2018 — current

At 4KST, I develop, evaluate and analyze machine learning methods for different applications, including mainly credit scoring and fraud detection systems. To know more about 4KST, you can click here! (English version is coming soon!)

Télécom ParisTech, Paris, France

Visitor Researcher • September 2017 — December 2017

During my time at Télécom ParisTech, I worked with Professor Albert Bifet on new adaptive feature selection techniques and evaluation metrics for data stream mining.

Udacity, Mountain View, USA

Project Reviewer and Classroom Mentor • Dec 2016 — current

Adhoc project reviewer and classroom mentor for the Data Analysis, Predictive Analytics for Business, Machine Learning and Deep Learning nanodegrees.

University of Waikato, Hamilton, New Zealand

Visitor Researcher • Mar 2016 — Aug 2016

During my time at the University of Waikato, I worked with Professor Bernhard Pfahringer on a dynamic filter for selecting features from data streams.

Pontifícia Universidade Católica do Paraná (PUC-PR), Curitiba, Brazil

Lecturer • Sep 2018 — Oct 2018

Lecturer on Big Data (Hadoop, MapReduce programming and HIVE) for the Big Data & Analytics MBA lato sensu course of the Polytechnic School

Pontifícia Universidade Católica do Paraná (PUC-PR), Curitiba, Brazil

Lecturer • Apr 2017 — Aug 2017

Lecturer on Big Data Stream Mining for the Big Data & Analytics lato sensu course of the Polytechnic School

Bernnet Informática Ltd., Curitiba, Brazil

Support Analyst • 2011 — 2014

Development and setup of computation environment and firewalls

Education

Pontifícia Universidade Católica do Paraná

Ph.D. in Informatics - ongoing • 2015 — current

Thesis focused on feature selection from ephemeral data streams

M.Sc. in Informatics • mar 2014 — mar 2015

Dissertation focused on data stream clustering

B.Sc. in Computer Science • 2010 — 2013

Marcelino Champagnat academic merit prize

Languages

Portuguese

Native speaker

English

Fluent

•Vancouver English Centre - Vancouver, BC, Canada (2008)

Publications

This list is not constantly updated, therefore, please refer to the following links for an updated version:
Lattes (in Portuguese)
DBLP
Google Scholar

Peer-reviewed Journal Papers

• BARDDAL, J.P., GOMES, H.M., ENEMBRECK, F., BIFET, A., PFAHRINGER, B.. Merit-guided dynamic feature selection filter for data streams. Expert Systems with Applications. 2019.
• GOMES, HEITOR MURILO , BARDDAL, JEAN PAUL , ENEMBRECK, F. , BIFET, A.. A Survey on Ensemble Learning for Data Stream Classification. ACM Computing Surveys. 2017.
• GOMES, HEITOR MURILO , BIFET, A. , READ, J. , BARDDAL, J. P. , Enembreck, F. , PFAHRINGER, B. , HOLMES, G. , ABDESSALEM, T.. Adaptive random forests for evolving data stream classification. Machine Learning. 2017.
• BARDDAL, J. P., GOMES, H. M. , ENEMBRECK, FABRÍCIO , PFAHRINGER, B.. A Survey on Feature Drift Adaptation: Definition, Benchmark, Challenges and Future Directions. The Journal of Systems and Software. 2016.
• BARDDAL, J. P., GOMES, HEITOR MURILO , ENEMBRECK, F. , BARTHÈS, JEAN-PAUL. SNCStream+ :Extending a high quality true anytime data stream clustering algorithm. Information Systems. 2016.
• BARDDAL, JEAN PAUL, GOMES, HEITOR MURILO , ENEMBRECK, FABRÍCIO. Advances on Concept Drift Detection in Regression Tasks Using Social Networks Theory. International Journal of Natural Computing and Research. 2015.

Peer-reviewed Conference Papers

• SEARA, M. P.; MALUCELLI, A.; SANTIN, A. O.; BARDDAL, J. P.. (Accepted) Are fintechs really a hype? A machine learning-based polarity analysis of Brazilian posts on social media. IEEE 16TH INTERNATIONAL CONFERENCE ON INDUSTRIAL INFORMATICS (IEEE INDIN'18). 2018.
• YUAN, L.; PFAHRINGER, B.; BARDDAL, J. P.. Iterative Subset Selection for Feature Drifting Data Streams. ACM Symposium on Applied Computing (ACM SAC). 2018.
• FERREIRA, L. E. B. ; BARDDAL, J.P. ; GOMES, H. M. ; ENEMBRECK, F.. An Experimental Perspective on Sampling Methods for Imbalanced Learning from Financial Databases. International Joint Conference on Neural Networks (IJCNN). 2018.
• GOMES, H. M. ; BARDDAL, J. P. ; FERREIRA, L. E. B. ; BIFET, A.. Adaptive random forests for data stream regression. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN). 2018.
• FERREIRA, L. E. B. ; BARDDAL, J. P. ; ENEMBRECK, F. ; GOMES, H. M.. Improving Credit Risk Prediction in Online Peer-to-Peer (P2P) Lending Using Imbalanced Learning Techniques. IEEE International Conference on Tools with Artificial Intelligence (ICTAI). 2017.
• GRANATYR, JONES ; BARDDAL, JEAN PAUL ; ALMEIDA, ADRIANO WEIHMAYER ; ENEMBRECK, FABRICIO ; DOS SANTOS GRANATYR, ADAIANE PEREIRA. Towards emotion-based reputation guessing learning agents. International Joint Conference on Neural Networks (IJCNN). 2016.
• BARDDAL, JEAN PAUL; GOMES, HEITOR MURILO ; ENEMBRECK, FABRÍCIO ; PFAHRINGER, B. ; BIFET, A.. On Dynamic Feature Weighting for Feature Drifting Data Streams. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML-PKDD). 2016.
• BARDDAL, J. P.; GOMES, HEITOR MURILO ; BRITTO JR., A. S. ; Enembreck, F.. A Benchmark of Classifiers on Feature Drifting Data Streams. International Conference on Pattern Recognition. 2016.
• BARDDAL, J. P.; GOMES, HEITOR MURILO ; GRANATYR, J. ; BRITTO JR., A. S. ; Enembreck, F.. Overcoming Feature Weights via Dynamic Feature Weighted k-Nearest Neighbor Learning. International Conference on Pattern Recognition. 2016.
• GOMES, HEITOR MURILO ; BARDDAL, JEAN PAUL ; ENEMBRECK, FABRÍCIO. Pairwise combination of classifiers for ensemble learning on data streams. ACM Symposium on Applied Computing (SAC). 2015.
• BARDDAL, JEAN PAUL; PINZ BORGES, ANDRÉ ; DE SOUZA, ANDERSON JOSÉ ; ENEMBRECK, FABRÍCIO ; GOMES, HEITOR MURILO. Applying Ensemble-based Online Learning Techniques on Crime Forecasting. International Conference on Enterprise Information Systems (ICEIS). 2015.
• BARDDAL, J. P.; GOMES, HEITOR MURILO ; ENEMBRECK, FABRÍCIO. A Complex Network-based Anytime Data Stream Clustering Algorithm. International Conference on Neural Information Processing (ICONIP). 2015.
• BARDDAL, J. P.; GOMES, H. M. ; ENEMBRECK, FABRÍCIO. Analyzing the Impact of Feature Drifts in Streaming Learning. International Conference on Neural Information Processing (ICONIP). 2015.
• GOMES, H. M. ; CARVALHO, D. ; ZUBIETA, L. ; BARDDAL, J. P. ; MALUCELLI, A.. On the Discovery of Time Distance Constrained Temporal Association Rules. International Conference on Neural Information Processing (ICONIP). 2015.
• BARDDAL, JEAN PAUL; GOMES, HEITOR MURILO ; ENEMBRECK, FABRICIO. A Survey on Feature Drift Adaptation. International Conference on Tools with Artificial Intelligence (ICTAI). 2015.
• BARDDAL, JEAN PAUL; GOMES, HEITOR MURILO ; ENEMBRECK, FABRÍCIO. SNCStream. ACM Symposium on Applied Computing (SAC). 2015.
• BARDDAL, JEAN PAUL; GOMES, HEITOR MURILO ; ENEMBRECK, FABRÍCIO. SNCStream: a social network-based data stream clustering algorithm. ACM Symposium on Applied Computing (SAC). 2015.
• BARDDAL, JEAN PAUL; GOMES, HEITOR MURILO ; ENEMBRECK, FABRÍCIO. SFNClassifier: a scale-free social network method to handle concept drift. ACM Symposium on Applied Computing (SAC). 2014.

Skills

Computer Languages

C, C++, Java, Python, Delphi, Objective-C, Swift

Machine Learning

Pandas, Scikit-learn, Tensorflow, NumPy, SciPy

Expertise

Foundations of Computer Science, Mathematics, Information Theory

Awards

Marcelino Champagnat - Best student award

Pontifícia Universidade Católica do Paraná • 2013

Awarded as the best undergraduate student of the Computer Science bachelor degree (GPA 9.6 out of 10)