Computer Vision News 38 Congrats, Doctor Nandita! Nandita Bhaskhar was a PhD candidate in the Department of Electrical Engineering at Stanford University. She recently defended her thesis successfully on Robust, Dataefficient and Trustworthy Medical AI . Nandita is broadly interested in developing machine learning methodology for medical applications. Her current research focuses on developing alternate sources of supervision for medical data such as observational supervision using passively-collected event logs and self-supervision strategies for dataefficient medical representation learning. She deeply cares about model performance in the wild and works on developing strategies for quantifying model trust and mitigating distribution shifts for reliable clinical deployment. Outside of research, her curiosity lies in a wide gamut of things including but not restricted to biking, social dance, traveling, creative writing, music, getting lost, hiking and exploring new things. Congrats, Doctor Nandita! Robust, Data-efficient and Trustworthy Medical AI Artificial intelligence (AI) has revolutionized multiple fields including safetycritical domains such as healthcare. It has shown remarkable potential for building both diagnostic and predictive models in medicine using various types of healthcare data. However, despite its potential, there are two major barriers to medical AI development and subsequent adoption to healthcare systems: 1) Training AI models that perform well with a limited amount of labeled data is challenging. However, curating large labeled datasets is costly; and might not be possible in several cases; 2) Even welltrained, state-of-the-art models, with impressive accuracies on their test sets - developed with rigorous validation and testing, may fail to generalize to new patients when deployed and they may be brittle under distribution shifts.
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