Ensemble Learning for Evolving Data Streams
Talk at Harbin Institute of Technology, Shenzhen, China
CV
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.
Work experience
- May 2022 – Current, Lecturer (Assistant Professor) in Artificial Intelligence at Victoria University of Wellington, Wellington, New Zealand.
- November 2019 – May 2022, Senior Research Fellow and Co-Director of the AI Institute at the University of Waikato, Hamilton, New Zealand.
- May 2019 – November 2019, Postdoctoral Fellow at the University of Waikato in the machine learning group, Hamilton, New Zealand.
- May 2017 – May 2019 (2 years), Postdoctoral Researcher at Télécom ParisTech, Paris, France.
- September 2014 – 2017 (2 years 5 months) PhD scholarship student at Pontifícia Universidade Católica do Paraná, Curitiba, Brazil.
- March 2016 – June 2016 (3 months) PhD internship at Télécom ParisTech, Paris, France.
- January 2015 – December 2015 (1 year) Mentoring of students for the Brazilian Educational Program in iOS Development (BEPiD) at Pontifícia Universidade Católica do Paraná, Curitiba, Brazil.
- February 2012 - September 2014 (2 years 8 months) Computer Science Assistant Professor (Lecturer) at UTP University, Curitiba, Brazil
- January 2011 – January 2012 (1 year 1 month) SAP Security Analyst at ExxonMobil, Curitiba, Brazil
- January 2009 – December 2010 (2 years) Trainee at ExxonMobil, Curitiba, Brazil
- June 2008 – January 2009 (8 months) Developer & System analyst at AM&RM, Curitiba, Brazil
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;
Reviewer and/or PC member: Conferences
SIGKDD - 2019, 2020, 2021, 2022; ECML - 2019, 2020, 2021, 2022; PAKDD - 2018, 2019, 2020, 2021; IJCAI - 2018, 2019, 2020, 2021, 2022; AAAI - 2019, 2020, 2021; ICDM - 2017; IEEE International Conference on Big Data - 2017; ICONIP 2019; ESANN 2019; ACM SAC - 2017, 2018, 2019, 2020, 2021;
Others
Ad-hoc Reviewer for Chile Research Commission CONICYT - 2017; Udacity Machine Learning Engineer Nanodegree - 2016-2021;
Chair, Co-chair or Organizer
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
Recipient of the University of Waikato Strategic fund for research (Jan-2020 - Dec-2020).
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.
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