Chang Chiann received a B.S. degree and a M.S. degree in Statistics from the University of São Paulo, São Paulo, Brazil, in 1989 and 1993, respectively. She got a Ph.D (1997) and a Postdoctoral (2000) degree, from the University of São Paulo, supervised by Prof. Pedro A. Morettin. She joined the International Cooperation Project (CNPq/NSF Agreement) with Professor David R. Brillinger, from the University of California, Berkeley, from 1997 to 2000. She is currently Associate Professor 3 at the University of São Paulo. Her research interests are focused on Time Series and Bioequivalence, working mainly on the following topics: Wavelets, Time Series, Bioavailability and Bioequivalence Analysis, Biosimilars, and Clinical Trials. (Source: Lattes Curriculum)
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The digital transformation is still little consolidated in Brazilian society. Improvements in performance and differentiation resulting of such transformation have not yet been fully absorbed in companies and in the public administration. In general, solutions required for implementing the digital transformation in cities and in manufacturing processes combine advanced connectivity and au…
Nowadays, one of the disruptive aspects of industrial automation is the machine learning technology, a segment based on programming languages and IoT (Internet of Things) platforms for system integration and digital interconnection. In industrial monitoring, it is possible to extract datasets from sensors and other equipments at fixed interval points in time in order to analyze different …
In this work, we will study the theoretical methodology of support vector regression (SVR), aimed at analyzing the data sets with high dimension. Our objective is to compare the performance of this technique with conventional parametric techniques. The parametric methods to be compared are: Ridge regression and Lasso. These parametric methods are most commonly used in high-dimensional pro…
The objective of this project is to explore a big data solution of the water industry for the unattended automation of the daily programming of the water adduction operational controls using some parametric time series models such as harmonic model with periodicity of 12 and 24 hours + autoregressive model; SARIMA model (Seasonal Autoregressive Moving Average Models); model ARFIMA (Long M…
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| 3 / 2 | Completed research grants |
| 5 / 4 | Completed scholarships in Brazil |
| 8 / 6 | All research grants and scholarships |
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