Faculty Friday with Sarah Ostadabbas: Enabling Advanced AI Algorithms to Solve Visual Perception Problems in the Small Data Domain

Sarah Ostadabbas


In our latest Faculty Friday video, Sarah Ostadabbas, Assistant Professor of Electrical and Computer Engineering at Northeastern University, discusses her research in machine learning and pattern recognition and its relation to infant behavior. She highlights how the Institute for Experiential AI’s multidisciplinary collaboration can impact the fields of behavioral scientists, psychologists, computer scientists, engineers, and more.


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.