School of Medicine
Showing 51-100 of 135 Results
-
Asef Islam
Masters Student in Biomedical Data Science, admitted Winter 2023
Current Research and Scholarly InterestsAI in medicine and other fields, particularly ML and CV techniques
-
Clarissa Klein
Scientific Data Curator 2, Biomedical Data Science
BioScientific curator and coordinator for the Clinical Genome Resource (ClinGen) and the Pharmacogenomics Knowledgebase (PharmGKB) / ClinPGx. Program Manager for Stanford ClinGen.
Coordinator for ClinGen's Data Access, Protection, and Confidentiality (DAPC) Working Group, the Rheumatologic and Autoimmune Diseases Clinical Domain Working Group (RAD-CDWG), Multigenic Taskforce, HLA Working Group, and the Pharmacogenomics Working Group (PGxWG). Coordinator for PharmGKB's submission for FDA recognition of their clinical annotation database, and curator with a focus on PharmGKB Pediatric. -
Philip W. Lavori
Professor of Biomedical Data Science, Emeritus
Current Research and Scholarly InterestsBiostatistics, clinical trials, longitudinal studies, casual inference from observational studies, genetic tissue banking, informed consent. Trial designs for dynamic (adaptive) treatment regimes, psychiatric research, cancer.
-
Weixin Liang
Research Asst - Graduate, Biomedical Data Science
BioPlease visit my personal website for more detail: https://ai.stanford.edu/~wxliang/
-
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.
-
Pan Lu
Postdoctoral Scholar, Biomedical Data Sciences
Current Research and Scholarly InterestsMy research goal is to build machines that can reason and collaborate with humans for the common good. My primary research focuses on machine learning and NLP, particularly machine reasoning, mathematical reasoning, and scientific discovery:
1. Mathematical reasoning in multimodal and knowledge-intensive contexts
2. Tool-augmented large language models for planning, reasoning, and generation
3. Parameter-efficient fine-tuning for fondation models
4. AI for scientific reasoning and discovery -
Matthew Lungren
Adjunct Professor, Biomedical Data Science
BioDr. Lungren is Chief Scientific Officer for Microsoft Health & Life Sciences where he focuses on translating cutting edge technology, including generative AI and cloud services, into innovative healthcare applications. As a physician and clinical machine learning researcher, he maintains collaborative research and teaching roles as adjunct professor at Stanford University.
Prior to joining Microsoft, Dr Lungren was a clinical interventional radiologist and research faculty at Stanford University Medical School where he led the Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI). He later served as Principal for Clinical AI/ML at Amazon Web Services in World Wide Public Sector Healthcare, focusing on business development for clinical machine learning technologies in the public cloud.
His scientific work has led to more than 200 publications, including work on multi-modal data fusion models for healthcare applications, new computer vision and natural language processing approaches for healthcare specific domains, opportunistic screening with machine learning for public health applications, open medical data as public good, prospective clinical trials for clinical AI translation, and application of generative AI in healthcare. He has served as advisor for early stage startups and large fortune-500 companies on healthcare AI technology development and go-to-market strategy. Dr. Lungren's work has been featured in national news outlets such as NPR, Vice News, Scientific American, and he regularly speaks at national and international scientific meetings on the topic of AI in healthcare.
Dr. Lungren is also a top rated instructor leading AI in Healthcare courses designed especially for learners with non-technical backgrounds:
Stanford/Coursera: https://www.coursera.org/learn/fundamental-machine-learning-healthcare
LinkedIn Learning: https://www.linkedin.com/learning/an-introduction-to-how-generative-ai-will-transform-healthcare -
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.
-
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.
