Aris Duani Rojas is a Ph.D. student in Computer Science at Florida International University (FIU) and a Department of Energy (DOE) Fellow at the Applied Research Center. His research focuses on deep learning, computer vision, and artificial intelligence for scientific and environmental applications. He is particularly interested in leveraging AI to address complex real-world challenges, such as nuclear waste detection, rainwater seepage monitoring, Electrical Resistivity Tomography (ERT) inversion for subsurface imaging, and predicting contaminant concentrations in groundwater wells.
As part of the DOE Fellows Program, Aris has worked on projects aimed at improving environmental monitoring and nuclear waste management. His work includes developing computer vision algorithms to detect and classify low-level nuclear waste, automating seepage detection in F-Area basins at Savannah River National Laboratory (SRNL), creating deep learning models to enhance geophysical inversion techniques for contamination assessment at Pacific Northwest National Laboratory (PNNL), and applying machine learning to predict radionuclide concentrations in SRNL wells. Additionally, he has developed novel preprocessing, ensemble, and explainable AI algorithms to improve model performance on real-world problems while enhancing trust and transparency for stakeholders.
Beyond his research, Aris actively participates in academic discussions. He has presented his findings at conferences such as Center for the Remediation of Complex Sites (RemPlex) 2023 and Waste Management (WM) Symposia 2024, contributing to ongoing efforts to apply AI in scientific domains.