I am excited about creating new physics-inspired AI solutions for modeling complex dynamical systems.

– Tales Imbiriba, Sr. Research Scientist, EAI

Tales Imbiriba is a senior research scientist at the Institute for Experiential AI and an assistant research professor at the Department of Electrical & Computer Engineering at Northeastern University (NU). Before joining NU, he attended top Brazilian federal universities, where he completed his bachelor’s, master’s, and doctoral degrees in electrical engineering. He joined the university in 2019 as a postdoctoral researcher, becoming an associate research scientist in 2021 and a research assistant professor in July 2023.

Imbiriba’s research interests concern statistical signal processing and machine learning, focusing on hybrid physics-data-driven models, nonlinear dynamical systems modeling, Bayesian filtering, estimation theory, and time-series analysis. Current projects include physiological data analysis for predicting the onset of aggressive behavior in youth with autism spectrum disorder, development of hybrid physiological-data-driven dynamic models for modeling the urinary system (BLADDER), detection of retinopathy of prematurity (ASSIT), motor cortex mapping, interference source localization and tracking in navigation systems (RESILIENT), online learning of AI-enhanced dynamical physical models applied for navigation systems, remote sensing, hyperspectral unmixing, and sensitive resource monitoring. In these projects, he and fellow researchers analyze different types of data, including physiological time-series, multispectral and hyperspectral images, and radar data, among others.