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Nazanin Moradinasab is a Postdoctoral Research Associate in the School of Data Science at the University of Virginia. Her research focuses on machine learning, computer vision, and multimodal models, with a strong emphasis on designing innovative approaches for situations where access to limited or insufficiently labeled data presents a challenge. In particular, Nazanin's interests include multivariate time series classification, weakly supervised learning techniques, and domain adaptation methods tailored for handling noisy and partially labeled datasets, as well as developing multimodal models to effectively integrate diverse data sources.
Before her postdoctoral appointment, Nazanin completed her Ph.D. in Systems Engineering at the University of Virginia, where she developed novel methodologies such as SupCon-TSC for multivariate time series classification and a Generalized Multi-prototype Gaussian Mixture-based model for new domain adaptation settings. Her work has been published in well-known journals and conferences, including the IEEE International Conference on Image Processing (ICIP), the Data Mining and Knowledge Discovery Journal, and the International Conference on Machine Learning and Applications. Nazanin was awarded the American Heart Association (AHA) Predoctoral Fellowship (2023–2024) as the Primary Investigator, as well as the Endowed Fellowship (2022–2023), a merit-based award recognizing her academic excellence during her Ph.D.
Nazanin holds a Ph.D. in Industrial Engineering from Tarbiat Modares University in Iran, where her research primarily focused on optimization techniques, modeling, and their applications. Her contributions have been published in prestigious journals, including the Journal of Cleaner Production (IF: 9.7), Energy (IF: 9), and Energy Sources, Part B: Economics, Planning, and Policy (IF: 3.1). She also earned her M.Sc. and B.Sc. in Industrial Engineering from K. N. Toosi University of Technology, Iran.
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