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    Sayeri Lala, PhD

    Stanford University

    Sayeri Lala, PhD, is a Postdoctoral Scholar at Stanford University in the Quantitative Sciences Unit and Department of Neurosurgery, where she develops AI-driven tools to improve outcomes for patients with spine disease and cancer. She completed her PhD in Electrical and Computer Engineering at Princeton University, with a minor in Neuroscience; her thesis focused on designing efficient clinical randomized controlled trials with limited data using artificial intelligence. Previously, she earned her S.B. and MEng in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology, concentrating in AI.

    At MIT, Sayeri worked in the Magnetic Resonance Imaging Group, training convolutional neural networks to detect and mitigate artifacts in fetal brain MRI. During her PhD, she developed AI-based frameworks that combine deep learning and causal inference to improve the efficiency and power of randomized controlled trials, leading to peer-reviewed publications and patents pending. She also completed AIML internships at Apple, where she created machine-learning frameworks for health applications, including behavioral phenotyping and sleep staging from wearable data. At Stanford, she is developing predictive tools to guide clinical decision making using rich multimodal data sources, including wearable sensor data, clinical records, and electronic health records, for applications such as spine surgery recovery and cancer recurrence risk prediction.

    Sayeri has authored or co-authored peer-reviewed papers and delivered presentations on AI and its intersection with health across domains including medical imaging, clinical trial methodology, and AI for physiological signals, with work featured at MICCAI, ISMRM, IEEE and NeurIPS conferences, and in journals such as the Journal of Medical Artificial Intelligence, Trials, and Contemporary Clinical Trials Communications. Her work bridges methodological innovation and real-world deployment of decision-support tools in close collaboration with clinicians and biostatisticians. She is member of several honor societies, including Tau Beta Pi and IEEE Eta Kappa Nu, and has received invitations from others (e.g., Sigma Xi).

    Disclosure(s): No financial relationships to disclose

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