Summary
André Gonçalves is a machine learning research scientist with eight years of experience applying state-of-the-art ML and deep learning methods to scientific problems at Lawrence Livermore National Laboratory. He holds a Ph.D. from Unicamp—where his thesis on multi-task learning was named best in Computer Engineering—and spent part of his doctorate at the University of Minnesota developing probabilistic models for tasks ranging from image classification to Alzheimer's progression and climate forecasting. André combines strong theoretical grounding in Bayesian and statistical modeling with hands-on expertise in predictive modeling, time series, signal processing, and uncertainty quantification, critical for trustworthy scientific ML. His work balances accurate prediction with interpretability and uncertainty assessment, reflecting a focus on models that offer scientific insight as well as performance. Proficient with Python (TensorFlow, PyTorch), Matlab, C++, and R, he has transitioned academic advances into mission-driven research at a national laboratory.
8 years of coding experience
9 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Electrical and Computer Engineering, Doctor of Philosophy (Ph.D.), Electrical and Computer Engineering at Universidade Estadual de Campinas / UNICAMP
Bachelor's degree, Computer Science, Bachelor's degree, Computer Science at Universidade Estadual de Londrina
Master's degree, Computer Engineering, Master's degree, Computer Engineering at Universidade Estadual de Campinas
University of Minnesota Twin Cities
Portuguese, English