Multi-Modal AI for Open-World Learning: From Vision to Smart Health
Speaker: Salimeh Sekeh, Ph.D.
Bio: Salimeh Sekeh is an associate professor in the Computer Science Department of San Diego State University (SDSU). Prior to SDSU, she was an assistant professor of computer science at the University of Maine (UMaine) from 2019-2024. She joined UMaine from Michigan, where she was a postdoctoral researcher working with Alfred O. Hero. She held a CAPES-PNPD Post-Doctoral Fellow appointment with the Federal University of São Carlos (UFSCar), Brazil, in 2014 and 2015. She was a Visiting Scholar with the Polytechnic University of Turin, Turin, Italy, from 2011 to 2013. She was a recipient of the CAREER award and CISCO research gift both in 2022. Prof. Sekeh is the Director of the Sekeh Laboratory, and she received the Maine College of Engineering and Computing (MCEC) Early Career Research Award in 2023. Her research has been funded through NSF, Cisco, UMaine AI initiative, Maine Space Grant Consortium (MSGC)/NASA, and UMaine Space Initiative.
Dr. Sekeh’s research interests are at the intersection of machine learning and computer vision, with a particular focus on the foundation of AI models and representation learning in feature map perception problems. Her primary focus is on design, improvement, and analysis of AI techniques with emphasis on generalization and robustness. Further, she works on continual learning methods, open-world learning, adversarial robustness, domain adaptation and generalization, and graph learning. From the applied side of her research, Dr. Sekeh has focused on both interdisciplinary and multidisciplinary projects with a strong contribution to understanding and prediction of real-world problems such as biological imaging, healthcare, forestry, and robotics. Her work has been published in IEEE Trans. on Neural Networks and Learning Systems (TNNLS), IEEE International Conference on Intelligent Data and
Security, IEEE Trans. on Intelligent Vehicles, ECCV, ICLR, ICPR, BMVC, ICASSP, ISIT, and other AI journals and conferences