Ghoreishi and Asadi Received the Conference Best Paper Award at MECC 2024
Awardee and interdisciplinary PhD student, Negar Asadi (center) with Simona Onori (left), conference program chair and associate professor of energy science engineering at Stanford University, and Hosam Fathy (right), conference general chair and professor of mechanical engineering at the University of Maryland.
CEE/Khoury Assistant Professor Fatemeh Ghoreishi and her interdisciplinary PhD student, Negar Asadi, PhD’27, received the Conference Best Paper Award at the 2024 Modeling, Estimation, and Control Conference (MECC 2024) for their paper, “Active Learning for Efficient Data Acquisition in Coupled Multidisciplinary Systems.”
Their work addresses the challenges in coupled multidisciplinary systems, such as cyber-physical systems, where interactions among various disciplines lead to complex and uncertain behaviors. Accurate modeling of each discipline is crucial for effective design, control, and analysis of these systems. However, the high cost of data from experiments or simulations poses a significant challenge. To address this, their work presents a data-driven algorithm based on probabilistic models that account for data uncertainty and scarcity. They propose an efficient active learning approach to identify the most impactful data points for accurate uncertainty analysis of these systems, without requiring exhaustive data collection.