Alberto Costa Nogueira Junior

Overview

Alberto Costa Nogueira Junior

Title

Research Scientist - SciML specialist

Location

IBM Research - Brazil São Paulo, Brazil

Bio

Alberto graduated in Mechanical Engineering from the State University of Campinas (1994), having participated in the Exchange Program for Engineering Students at Institut National Des Sciences Appliquees de Lyon - France (1992-1993). He graduated with his Master's and PhD. in Mechanical Engineering from the Faculty of Mechanical Engineering, State University of Campinas (1998 and 2002, respectively) in the following areas: High Order Spectral/hp Finite Element formulations for 3D unstructured meshes applied to non-linear Elasticity, Algebraic Multigrid schemes and scientific software coding in C++ language. Alberto also concluded two post-doctorates in the Computational Fluid Dynamics (CFD) area, one at the Faculty of Civil Engineering, State University of Campinas (2004), and the other at the Brazilian Aerospace Institute, Department of Aerospace Science and Technology - DCTA (2006). For nearly a decade (2004-2013), Alberto was the CEO of his startup and accumulated significant industrial and innovation experience working as a technology entrepreneur. His areas of interest include High-order Discontinuous Galerkin Method (DGM), Spectral Element Method (SEM), Finite Element Method (FEM), Finite Volume Method (FVM), compressible inviscid and viscous flows at high Mach and high Reynolds numbers, turbulence modeling, incompressible flows applied to water circulation, High-Performance Computing (HPC) for engineering simulation and scientific programming using Phyton. Since May 2013, he has been a Research Scientist member of the Dynamical Systems Modeling (DSM) team within IBM Research in Brazil. In the last couple of years, he federated the Scientific Machine Learning local initiative based on Physics Informed Neural Networks (PINNs) to solve direct and inverse problems in seismic applications. From May 2020 to the present, he has led the IBM Physics-Informed Machine Learning (PIML) squad developing state-of-the-art tools to tackle climate change.