School of Medicine
Showing 1-10 of 15 Results
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Shaimaa Bakr
Postdoctoral Scholar, Biomedical Informatics
Masters Student in Biomedical Informatics, admitted Autumn 2020BioShaimaa is a graduate of the Ph.D. program, the Department of Electrical Engineering at Stanford. Shaimaa is a member of the Gevaert and RIIPL labs. Prior to Stanford, Shaimaa received her B.Sc. (Summa Cum Laude) from the American University in Cairo, where she studied Electronics Engineering and Computer Science. She obtained her MS degree in Electrical Engineering from Rensselaer Polytechnic Institute, working in the Cognitive and Immersive Systems lab, and advised by Professor Richard Radke. Shaimaa is interested in applying and developing machine learning methods for medical imaging and molecular data.
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Zepeng Huo
Postdoctoral Scholar, Biomedical Informatics
BioConducting research on Foundation Models for medicine
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Tushar Mungle
Postdoctoral Scholar, Biomedical Informatics
Current Research and Scholarly InterestsUse electronic health records (EHRs) to identify and classify common ocular diseases such as glaucoma, diabetic retinopathy, and macular degeneration. We aim to develop an approach to accurately identify these conditions using EHRs. This will be followed by cluster analysis to identify novel subtypes of these conditions that have not been recognized before. Finally, we will develop an approach to extract outcome data from EHRs for patients with these conditions in the primary care setting.
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Fateme Nateghi Haredasht
Postdoctoral Scholar, Biomedical Informatics
BioI am a postdoctoral scholar at the Stanford Center for Biomedical Informatics Research. I earned my PhD in Biomedical Sciences from KU Leuven, Belgium. My research during my PhD program focused on machine learning applications in healthcare, particularly in the field of survival analysis.
My doctoral thesis was centered on the development of predictive models for critically ill patients with acute kidney injury (AKI). By leveraging electronic health record (EHR) data, we created personalized risk profiles for AKI survivors upon ICU discharge, leading to tailored follow-up plans. Additionally, we developed machine learning-based models to predict outcomes post-AKI, including progression to chronic kidney disease (CKD) and mortality.
In another study, we investigated the utilization of unlabeled data to enhance the accuracy of survival time predictions. By integrating partial supervision from censored data within a semi-supervised wrapper approach, we consistently achieved superior results. This approach has the potential to significantly improve survival outcome predictions, offering valuable insights for clinical decision-making.
In my current role at Stanford, I continue to advance the integration of machine learning in healthcare, collaborating with experts to improve patient care and outcomes. -
Madelena Ng
Postdoctoral Scholar, Biomedical Informatics
BioMadelena is a postdoctoral scholar at the Stanford Center for Biomedical Informatics Research (BMIR). Her research aims to illuminate the evolving ethical and practical challenges among digital and emerging technologies (e.g., web- and app-based population health research, clinical AI solutions, blockchain for health data). Her work in the Boussard Lab focuses on discerning key factors for clinical AI solutions to flourish in practice—from the readiness of the datasets for machine learning research to the operational principles that are required for successful clinical deployment.
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Malvika Pillai
Postdoctoral Scholar, Biomedical Informatics
BioMalvika Pillai is a postdoctoral research fellow in the VA Big Data Scientist Training Enhancement Program (BD-STEP), jointly in Stanford University in Medicine (Biomedical Informatics) in the Boussard Lab and VA Palo Alto. She received her BS in Quantitative Biology and PhD in Health Informatics from the University of North Carolina at Chapel Hill. Her current work focuses on the development, evaluation and implementation of machine learning algorithms for clinical decision support.
