Gustavo Batista

Associate Professor Gustavo Batista is a prominent researcher in the School of Computer Science and Engineering at the University of New South Wales, where he joined in 2018 after more than a decade at the University of Sao Paulo (USP). He previously served as a visiting researcher at the University of California, Riverside (2010-2012) working with Professor Eamonn Keogh.

Education:

• Habilitation in Computer Science, University of São Paulo at São Carlos (2016)
• PhD in Computer Science, University of São Paulo at São Carlos (2003)
• MSc in Computer Science, University of São Paulo at São Carlos (1997)
• BS in Computer Science, São Paulo State University (1994)

Professor Batista's research focuses on practical applications of Machine Learning, particularly in supervised machine learning, data mining, time series analysis, data streams, and imbalanced data. His work bridges theoretical computer science with real-world applications, especially in developing lightweight models for embedded devices and sensors. His research approach emphasizes identifying gaps in literature through challenging applications, leading to contributions in both Computer Science and application domains.

His publication record demonstrates consistent contributions across time series analysis, data streams, and quantification, with notable emphasis on developing algorithms that function effectively in resource-constrained environments. His work on the UCR suite for time series matching under warping earned the KDD Best Research Award in 2012, while his more recent work on quantification algorithms received the Best Research Paper Award at DSAA-2020.

Scientific Awards:

• Best Research Paper Award, IEEE International Conference on Data Science and Advanced Analytics (2020)
• Research Fellow, level 2, National Council for Scientific and Technological Development, CNPq (2017-2020)
• Research Fellow, level 2, National Council for Scientific and Technological Development, CNPq (2014-2017)
• Google Research Award in Latin America (2015-2016)
• Best Research Paper Award, ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2012)

Professor Batista has successfully secured significant grant funding as principal investigator, including a $500,000 USAID Combating Zika and Future Threats Grand Challenge award and multiple FAPESP and CNPq grants. He currently supervises PhD students Tiago Pinho da Silva (working on Election Forensics) and Antonio Parmezan (working on Hierarchical Classification of Data Streams), contributing to the next generation of data science researchers.

His research group has developed innovative tools like EmbML, which converts scikit-learn and Weka classifiers into C++ code for low-power microcontrollers, demonstrating his commitment to practical implementations of machine learning in resource-constrained environments.