Prof. Sara Ostadabbas of Northeastern University

My main research interests are based on creating augmented cognition systems for healthcare providers and patients with chronic conditions. Conversations with doctors helped me understand that the number one problem doctors face is quickly sorting through and making sense of the massive data that modern sensing technologies provide. They do not want systems that make decisions for them, but rather systems that do a better job of presenting and organizing information. This aligns with the goal of augmented cognition systems, which is to enhance human cognition rather than replace it.  These systems have three primary components: the sensing element, the analytic element, and the feedback element. Robust analytics is the cornerstone of the system. I have used both machine learning models and biomechanically-inspired structural models. When possible, a structural model is preferred because it can incorporate existing knowledge and research into the model without requiring a large training set to gain such knowledge. In addition, structural models tend to be more transparent and easier to analyze for failure modes and edge cases. Careful design of the feedback element is also critical because unless the feedback is useful, timely, and understandable, the system will be unusable regardless of the quality of the other two components

Biography:

Sarah Ostadabbas is a first year assistant professor at the Electrical and Computer Engineering Department of Northeastern University (NEU). Sarah joined NEU from Georgia Tech, where she was a post-doctoral researcher following completion of her PhD at the University of Texas at Dallas in 2014. At NEU, Sarah has recently formed the Augmented Cognition Laboratory (ACLab) with the goal of enhancing human information-processing capabilities through the design of adaptive interfaces via physical, physiological, and cognitive state estimation.