Many advances in AI have focused on Big Data. But to truly enable the potential of AI at individual and societal levels in a beneficial way, we need to leverage its power with customized AI tools in Small Data domains for greater accessibility.
– Sarah Ostadabbas, Core Member, EAI
Sarah Ostadabbas is an assistant professor in the Department of Electrical and Computer Engineering and director of the Augmented Cognition Laboratory. Her research focuses on enhancing human information-processing capabilities through the design of adaptive interfaces via physical, physiological, and cognitive state estimation.
Ostadabbas has developed augmented and virtual reality tools for both the assessment and enhancement portions of interfaces, based on rigorous models adaptively parameterized using machine learning and computer vision algorithms.
Her work extends to medical and military applications in the small data domain where data collection and labeling is expensive, individualized, and protected by stringent privacy or classification laws. She has developed learning frameworks with deep structures that work with limited labeled training samples. Her work has also involved integrating domain knowledge into prior learning and synthetic data augmentation and maximizing generalized learning across domains by learning invariant representations.
Ostadabbas has co-authored more than 70 peer-reviewed journal and conference articles. Her research has received funding from the National Science Foundation, Mathworks, Amazon Web Services, Biogen, and NVIDIA. Within the Institute of Electrical and Electronics Engineers (IEEE), she is a member of the Computer Society, Women in Engineering, the Signal Processing Society, Engineering in Medicine & Biology Society, and the Young Professionals group. She serves on the International Society for Virtual Rehabilitation and the Association for Computing Machinery Special Interest Group on Computer-Human Interaction. She has helped organize workshops on topics ranging from multimodal data fusion to deep learning in small data.
Ostadabbas is now associate editor of IEEE’s Transactions on Biomedical Circuits and Systems journal, on the editorial board of both IEEE’s Sensors Letters and the Digital Biomarkers journals, and has been technical and session chair for several signal processing and machine learning conferences. She completed her postdoctoral research at Georgia Institute of Technology after earning her doctoral degree at the University of Texas at Dallas.