Kaike Sa Teles Rocha AlvesSUMMARY:
Ph.D. candidate in Computational Modeling at the Federal University of Juiz de Fora (UFJF), I work as a researcher in artificial intelligence, studying, developing, and applying fuzzy inference models. Furthermore, I was awarded a scholarship to conduct one year of my Ph.D. as a student at the University of Nottingham/UK, where I became a member of the LUCID - Lab for Uncertainty in Data and Decision Making under the supervision of Professor Christian Wagner. Nonetheless, my degree in Industrial Engineering provided me with knowledge and tools that give me a holistic view of the company to propose efficient processes and reduce waste. During my undergraduate studies, I worked as a scholarship monitor in the subjects of programming and production planning and control, totaling two years. I was also a volunteer member of the tutorial education group (GET-Production) for one year, carrying out activities that integrate academic theory and practice. Finally, I worked as a logistics intern in a medium-sized food company, applying performance indicator techniques to improve the company's performance. The following personal experience can be highlighted: - strong knowledge of programming languages, especially Python, SQL, and Matlab; - strong knowledge of Python libraries, such as Pandas, Numpy, Scikit-learn, TensorFlow, and Keras; - more than five years of experience with machine learning, including applications to energy and financial; - experience with data cleaning, process, and presentation of the results; - knowledge of statistical tools and hypothesis tests; - the domain of interval-valued data, a valuable technique for dealing with data uncertainty.- the domain of interval-valued data, a valuable technique for dealing with data uncertainty. RESEARCH INTERESTS:
- fuzzy inference systems; - interval-valued data; - time series forecasting; - financial and energy datasets; - deep learning; - computer vision. PUBLICATIONS:
A new Takagi–Sugeno–Kang model for time series forecasting | Engineering Applications of Artificial Inteligence A novel rule-based evolving Fuzzy System applied to the thermal modeling of power transformers | Applied Soft Computing. An enhanced set-membership evolving participatory learning with kernel recursive least squares applied to thermal modeling of power transformers | Electric Power Systems Research. A New evolving Fuzzy System with Mechanisms to Deal With Uncertainties in Times Series Forecasting | IEEE The complete list of publications can be seen on the Google Scholar webpage. |