Nazanin Moradinasab

Nazanin Moradinasab serves as a Postdoctoral Research Associate in the School of Data Science at the University of Virginia, focusing on innovative machine learning methodologies for data-scarce environments.

Her academic credentials include:

• Ph.D. in Systems Engineering, University of Virginia
• Ph.D. in Industrial Engineering, Tarbiat Modares University, Iran
• M.Sc. in Industrial Engineering, K. N. Toosi University of Technology
• B.Sc. in Industrial Engineering, K. N. Toosi University of Technology

Her research pioneers solutions for limited-labeled data scenarios through multivariate time series classification (e.g., SupCon-TSC), weakly supervised learning, and domain adaptation for noisy datasets, while developing multimodal frameworks to integrate heterogeneous data sources. Earlier work established expertise in optimization techniques applied to energy systems and sustainable production.

Key recognitions include:

• American Heart Association Predoctoral Fellowship (2023–2024) as Principal Investigator
• Endowed Fellowship (2022–2023) for doctoral academic excellence

As primary investigator for the AHA fellowship, she has secured competitive research funding while publishing in high-impact venues including IEEE ICIP, Data Mining and Knowledge Discovery Journal, and the Journal of Cleaner Production (IF: 9.7). Her scholarly output spans machine learning conferences and energy-focused journals, demonstrating interdisciplinary impact across computer science and industrial engineering domains.