We will discuss my paper “Forecasting dengue fever in Brazil: An assessment of climate conditions.” covering the theory and performing hands-on activities. The classes will be designed for students and organized in two different sessions. Session 1: Introduction to the problem and overview of our computational methods. Session 2: Data collection and Implementing a basic version of our machine learning approach. In this presentation we will cover Session 1. Section 2 will be covered in another meeting. (Prerequisites: A basic coding level in Matlab, Python, or R is expected)
Texto informado pelo autor.
* Os participantes dos seminários não poderão acessar às dependências da FGV usando bermuda, chinelos, blusa modelo top ou cropped, minissaia ou camiseta regata. O uso da máscara é facultativo, porém é obrigatória a apresentação do comprovante de vacinação (físico ou digital).
Apoiadores / Parceiros / Patrocinadores
Lucas M. Stolerman
Bio: I am an assistant professor at the Department of Mathematics at Oklahoma State University and an visiting professor at FGV EMAp. My research interests lie at the interface of math, biology, and data-driven methods, focusing on epidemic forecasting and mathematical modeling of infectious diseases. In 2021, I held a Research Fellow position at the Machine Intelligence Lab - Boston Children's Hospital /Harvard Medical School under the supervision of Mauricio Santillana. From 2018 to 2020 was a postdoctoral scholar in the Laboratory for Computational Cellular Mechanobiology at the Department of Mechanical and Aerospace Engineering - University of California San Diego, under the supervision of Padmini Rangamani. In 2017, I joined the Scientific Computing Program (PROCC) at Fundação Oswaldo Cruz (FIOCRUZ), as a postdoctoral researcher advised by Cláudia Codeço. I earned my Ph.D. degree from the Instituto Nacional de Matemática Pura e Aplicada (IMPA) under the supervision of Roberto Imbuzeiro de Oliveira (IMPA) and J. Nathan Kutz (University of Washington). My thesis focused on dimensionality reduction techniques and machine-learning algorithms for Epilepsy and Dengue Epidemiology problems.