Ensemble Learning for Evolving Data Streams
Talk at Harbin Institute of Technology, Shenzhen, China
CV
Service
Reviewer and/or PC member: Journals
Neurocomputing (Elsevier) - 2016, 2017; Transactions on Big Data (IEEE) - 2017; International Journal of Data Science and Analytics (Springer) - 2017; Information Systems - 2017; Data Mining and Knowledge Discovery - 2018, 2019, 2020, 2021; TKDE - 2018, 2019, 2020; IEEE Access - 2019; Journal of Machine Learning (JMLR) - 2019, 2020; Knowledge and Information Systems (KAIS) - 2019; Information Fusion 2023; Machine Learning (Springer) 2020-2023;
Reviewer and/or PC member: Conferences
SIGKDD - 2019, 2020, 2021, 2022; IJCAI - 2018-2023; AAAI - 2019-2023; ECML - 2019, 2020, 2022, 2023; PAKDD - 2018, 2019, 2020, 2021; ICDM - 2017; IEEE International Conference on Big Data - 2017; ICONIP 2019; ECAI 2020; ESANN 2019; ACM SAC (Data Streams Track) -2017, 2018, 2019, 2020, 2021;
Others
Ad-hoc Reviewer for Chile Research Commission CONICYT - 2017; Udacity Machine Learning Engineer Nanodegree - 2016-2021;
PhD Examiner
2023 Examiner for Katharina Dost, University of Auckland. Thesis title: Selection Bias Identification and Mitigation With No Ground Truth Information. 2022 Examiner for Ocean Wu, University of Auckland. Thesis title: Learning and Adapting Continuously for Data Streams
Chair, Co-chair or Organizer
Editorial board. Machine Learning journal - Springer, 2022-2023.
AusDM’22 Research PC Chair, Sidney, Australia, 2022.
IEEE International Conference on Data Mining (ICDM) 2021. Online Experience/Virtual Chair. Auckland, New Zealand. Website
ACM Symposium on Applied Computing Data Streams Track. In conjunction with ACM Symposium on Applied Computing: The 36th Annual ACM Symposium on Applied Computing 2021. Gwangju, Korea. Website.
Second International Workshop on Energy Efficient Scalable Data Mining and Machine Learning. Co-located with ECML PKDD 2019. Würzburg, Germany. Website.
Funding
- 2023-2026, “Exploring Disagreement-Based Learning for partially labelled data stream classification”, 22-UOW-066, Marsden Fund (Fast-Start), $360,000 NZD (Sole PI)
- 2023-2024, “Tackling energy demand forecast drifts”, VUW University Faculty Research Establishment Grant Application, $27,640 NZD (Sole PI)
- 2023-2024, “Time Series Analysis with Ordinal Patterns: Transformative Theoretical Advances and New Applications”, VUW Faculty Strategic Research Grant, $47,613 NZD. (Co-PI)
- 2022-2027, Time-Evolving Data Science/Artificial Intelligence for Advanced Open Environmental Science (TAIAO), MBIE SSIF Fund on Data Science. $13,000,000 NZD. (Key Researcher)
- 2020-2021, University of Waikato Strategic research fund ($29,000 NZD) (Sole PI)
Grants, Awards and Honors
- Added to the Honor Roll of Outstanding Reviewers PAKDD 2020. Website
- PhD Grants. From September 2014 until March 2017 I was recipient of a scholarship from CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) and from Fundação Araucária.
- Best Paper Award Information Systems (ACM SAC 2014). Best paper award for the paper entitled SAE2: Advances on the Social Adaptive Ensemble Classifier for Data Streams.
Education
- Ph.D. degree in Computer Science, Graduate Program in Informatics (PPGIa), Pontifícia Universidade Católica do Paraná, Brazil, 2013-2017.
- M.Sc. degree in Computer Science, Graduate Program in Informatics (PPGIa), Pontifícia Universidade Católica do Paraná, Brazil, 2011-2012.
- B.Sc. degree in Computer Science, UTP, Brazil, 2007-2010.
Publications
See my publications page.
Talks
StreamDM: Advanced data science with Spark Streaming
Tutorial at ExCel London, London, UK
Machine Learning for Spark Streaming with StreamDM
Tutorial at Barra da Tijuca Beach Windsor, Rio de Janeiro, Brazil
Big Data Stream Mining using Spark Streaming
Tutorial at Croke Park Conference Centre, Dublin, Ireland
Machine learning for nonstationary streaming data using Structured Streaming and StreamDM
Talk at Javits Center, New York, USA
Streaming Random Forest Learning in Spark and StreamDM
Talk at ExCel London, London, UK
Machine learning for streaming data: Practical insights
Talk at Javits Center, New York, USA
Streaming Random Patches for Evolving Data Stream Classification
Seminar at INESC-TEC, Porto, Portugal
Lecture at the IOT Stream Data Mining course
Lecture at Telecom Paris, Paris, France
Machine learning for data streams with scikit-multiflow
Tutorial at International Joint Conference on Artificial Intelligence, Yokohama, Japan
Machine learning for streaming data: state of the art, challenges, and opportunities
Tutorial at AIRA 2021, Hobbiton, New Zealand
Machine Learning for Data Streams Master Class
Presentation at ENZ Latin America Masterclass, Online
Semi-Supervised Learning for Delayed Partially Labelled Data Streams
Presentation at UoW Machine Learning Seminar, Online
Machine Learning for Streaming Data
Presentation at Cardiff University seminar, Online
Aprendizagem de Máquina para fluxos de dados contínuos (Machine learning for continuous data streams)
Presentation at Itau Data Science Talk, Online
Introduction to MOA
Presentation at International WEKA user conference, Hamilton, New Zealand
Learning for Delayed Partially Labelled Data Streams
Presentation at ICDM workshop (IncrLearn 2021), Auckland, New Zealand
Machine Learning for Streaming Data
Presentation at Michigan Technological University, Online
Semi-Supervised Learning for Delayed Partially Labelled Data Streams
Tutorial at AIRA 2022, University of Canterbury, New Zealand
Concept Drift Detection and Applications
Presentation at Stats NZ data science seminar, Online
Machine Learning for Streaming Data
Presentation at China University of Mining and Technology, Online
SSL and Delayed Labelling
Presentation at University of Waikato, Online