School of Medicine
Showing 61-80 of 123 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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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 -
Chase A. Ludwig, MD, MS
Assistant Professor of Ophthalmology (Research/Clinical Trials)
Masters Student in Biomedical Data Science, admitted Autumn 2023Current Research and Scholarly InterestsMy research focuses on understanding high and pathologic myopia and their retinal sequelae, including retinal detachments, myopic traction maculopathy, and myopic macular degeneration. By leveraging informatics and big data analytics, I aim to uncover strategies that prevent and treat the progression of these complex and devastating conditions. My work takes advantage of the retina’s unique role as the only visible portion of the central nervous system, allowing for discoveries in ophthalmology that have the potential to impact broader fields of medicine.
I am actively seeking medical students and residents interested in ophthalmology or vitreoretinal surgery to assist with writing projects and data analytics. If you are passionate about advancing the understanding and management of myopia, I invite you to join me in tackling one of the most pressing global challenges in eye care. -
Matthew Lungren
Adjunct Professor, Biomedical Data Science
BioDr. Lungren is Chief Data Science 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 a part-time clinical practice at UCSF while also continuing his 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 150 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 on Coursera where his AI in Healthcare course designed especially for learners with non-technical backgrounds has been completed by more than 20k students around the world - enrollment is open now: https://www.coursera.org/learn/fundamental-machine-learning-healthcare -
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.
