Biomedical Data Science
Showing 101-120 of 179 Results
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Liam Edward Mulhall
Software Developer, Biomedical Data Science
Current Role at StanfordLiam develops and maintains the HLA Curation Interface, a tool that supports the assessment of HLA alleles and haplotypes for use in precision medicine and research. He also works on internal tools used by the Stanford ClinGen team.
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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. -
Jeff Nirschl
Affiliate, Biomedical Data Science
BioJeff Nirschl, M.D., Ph.D. is an Instructor in Pathology at Stanford University, Stanford, CA with clinical expertise in Neuropathology. He completed his Ph.D. in Neuroscience at the University of Pennsylvania under the supervision of Dr. Erika Holzbaur. During his thesis research, he investigated axonal transport and genetic forms of parkinsonism. He also developed computational image analysis workflows for fluorescence microscopy and digital pathology. His research interests include molecular motors and the neuronal cytoskeleton, the regulation of axonal transport in neurodegeneration, digital pathology, and quantitative image analysis using machine learning.
https://orcid.org/0000-0001-6857-341X -
Akira Nishii
Masters Student in Biomedical Data Science, admitted Autumn 2024
Current Research and Scholarly InterestsI'm interested in the challenges that arise in healthcare and biomedicine when applying machine learning to data-scarce and safety-critical settings. This broad interest motivates me to work on topics surrounding self-supervised learning and synthetic data.
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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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Marcus Oehlrich, PhD
Masters Student in Biomedical Data Science, admitted Autumn 2025
BioI am a Professor of Finance, Accounting, and Taxation at accadis University of Applied Sciences in Bad Homburg, Germany, and I advise on startups, business valuation, and corporate finance. My consulting projects range from small startups in biotechnology and M&A transactions in the pharmaceutical and biotech sectors to the preparation of the business plan for the IPO of an international music and movie production group.
During my business studies, I became interested in law and medicine (especially cancer research), which is why I continued my education in these fields (including a Master of Science in Pharmaceutical Medicine, a Certificate in Pharmaceutical Law, and an MPH at Harvard T.H. Chan School of Public Health). Subsequently, I have started the M.S. in Biomedical Data Science at the Department of Biomedical Data Science at Stanford University School of Medicine.
I also had the privilege of serving as the CEO of the Institute for German, European, and International Medical Law, Health Law, and Bioethics of the Universities of Heidelberg and Mannheim (IMGB) from 2005 to 2011. At accadis University of Applied Sciences, I am Head of the Health Care Management Research Group and responsible for the Master of Arts in International Health Care Management. From 2010 to 2020, I served as the Honorary Consul of the Republic of San Marino in Frankfurt, Germany.
Most of my publications (12 books and more than 70 articles) are related to business topics. However, I also authored three books on cancer "Internetkompass Krebs [Internet compass cancer]" (Springer 2001), "Recombinant humanized monoclonal antibody Trastuzumab for the treatment of metastatic breast cancer with tumors overexpressing the HER2/neu proto-oncogene: A systematic review" (dissertation.de 2003) and "Krebs vorbeugen und bekämpfen [Preventing and combating cancer: All about prevention, early detection, therapy]" (Reader's Digest 2012). The latter was translated into Polish, Czech, Croatian, Slovenian, and Hungarian. -
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.
