School of Medicine
Showing 11-20 of 26 Results
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Sheng Liu
Postdoctoral Scholar, Biomedical Data Sciences
BioSheng Liu is a postdoctoral fellow at Stanford University. In May 2023, He received a Ph.D. degree from New York University, majoring in Data Science and Machine Learning. His background is in the area of robust and trustworthy machine learning, machine learning for healthcare.
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Fateme (Fatima) Nateghi
Postdoctoral Scholar, Biomedical Informatics
BioAs a postdoc researcher at the Division of Computational Medicine, I find myself at the exciting intersection of machine learning and healthcare. My journey began with a PhD in Biomedical Sciences from KU Leuven in Belgium, where I explored the complexities of machine learning algorithms and their transformative potential in clinical settings. My research focused on adapting these algorithms for time-to-event data, a method used to predict when specific events may occur in a patient’s future.
At Stanford, my work centers on building trustworthy AI systems to enhance healthcare delivery. I develop and evaluate machine learning models that integrate structured electronic health records (EHRs) and unstructured clinical notes to support real-world clinical decision-making. My recent projects include predicting treatment retention in opioid use disorder, improving antibiotic stewardship for urinary tract infections, and enabling digital consultations through large language models (LLMs). I'm particularly interested in embedding-based retrieval and retrieval-augmented generation (RAG) methods that help bridge cutting-edge AI research with clinical practice.
My role involves not just advancing the integration of machine learning in healthcare but also collaborating with a diverse team of clinicians, data scientists, and engineers. Together, we're striving to unravel complex healthcare challenges and ultimately improve patient outcomes. -
Humaira Noor
Postdoctoral Scholar, Biomedical Informatics
BioDr. Humaira Noor is a postdoctoral researcher in the Gevaert Lab with a PhD in glioma genomics from University of New South Wales, Australia. Her expertise spans biomarker discovery, with particular emphasis on prognostic and molecular determinants of glioma treatment-response, radiogenomic model development for early high-risk patient stratification, and the integration of multi-omics and biomedical imaging to advance precision oncology
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Namu Park
Postdoctoral Scholar, Biomedical Informatics
BioDr. Park is a Postdoctoral Scholar at the Division of Computational Medicine at Stanford University, where he is co-advised by Dr. Tina Hernandez-Boussard and Dr. Yair Bannet. He received his PhD in Biomedical and Health Informatics from the University of Washington.
His research focuses on clinical natural language processing and large language models for healthcare. He develops clinically grounded information extraction methods and evaluation frameworks that reflect real-world clinical workflows. His work examines how large language models can be aligned with clinical reasoning and rigorously evaluated for safe and effective deployment in health systems.
Through interdisciplinary collaboration, Dr. Park aims to bridge advances in foundation models with measurable clinical impact, emphasizing reliability, transparency, and scalability in AI-driven healthcare applications. -
Soumyadeep Roy
Postdoctoral Scholar, Biomedical Informatics
BioI am a postdoctoral scholar at the Center for Biomedical Informatics Research of Stanford University, advised by Prof. Tina Hernandez-Boussard.
My primary area of research is natural language processing, with expertise in medical and healthcare applications. My research areas of interest are Foundation Models for Medicine, Generative AI, Text Summarization, and Efficient Pretraining.
I hold a PhD in Computer Science and Engineering from the Indian Institute of Technology Kharagpur, where I worked with Prof. Niloy Ganguly and Prof. Shamik Sural. Here, I was part of the Complex Networks Research Group (CNeRG). My PhD thesis is titled “Domain Adaptation for Medical Language Understanding”, where I developed novel domain adaptation techniques to effectively and efficiently adapt open-domain AI models to the medical domain.
In summary, I have six years of experience working with medical NLP data, which includes clinical trial registry data (2018-2021), medical forum questions (2020-2021), DNA sequence data (2021-2024), biomedical scientific literature (2023 - 2025), clinical data (2021-2023) and EHR clinical notes (2025). My medical AI research experience includes 2.5 years at L3S Research Germany collaborating with Hannover Medical School as well as a 7-month research internship at GE HealthCare Technology and Innovation Center (HTIC) in Bangalore, India. I also presented a tutorial on March 10, 2025 titled "Building Trustworthy AI Models for Medicine" at WSDM 2025 held in Germany.
In my free time, I like hiking, and playing chess or table tennis.
