School of Medicine
Showing 151-160 of 187 Results
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Michelle Lo
Clinical Assistant Professor, Medicine
BioDr. Michelle Lo MD, FACP is a Clinical Assistant Professor in the Division of Hospital Medicine and Stanford School of Medicine. Growing up in Taiwan and in the Bay Area, she received her undergraduate degree in Molecular and Cellular Biology at University of California Berkeley, and her medical degree at David Geffen School of Medicine at UCLA. She then moved to NYU Grossman School of Medicine for her residency in Internal Medicine. She continued as Clinical Assistant Professor at NYU Grossman School of Medicine-Tisch Hospital from 2019-2020. After working in NYC during the COVID-19 pandemic, she returned to California to continue her career at Kaiser Permanente Santa Clara as a Hospitalist and affiliate Clinical Instructor at Stanford School of Medicine from 2020-2025, where she co-developed the Point of Care Ultrasound curriculum and was awarded the Hospitalist Teaching Award 3 years. She joined the Stanford School of Medicine Faculty in 2025. Her interests include medical education, curricular development, and use of Point-of-Care Ultrasound in the care of hospitalized patients.
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Nathan Lo
Assistant Professor of Medicine (Infectious Diseases) and, by courtesy, of Epidemiology and Population Health
Current Research and Scholarly InterestsOur research group is interested in studying the transmission of infectious diseases and impact of public health interventions with an ultimate goal of informing public health policy. We study a diverse set of pathogens, both domestically and internationally, including vaccine-preventable infections (including COVID-19) and neglected parasitic diseases (such as schistosomiasis). Our group applies diverse computational methodologies, including tools from fields of epidemiology, mathematical and statistical modeling, simulation, and policy analysis.
A large emphasis of our work is translating scientific evidence into public health policy. Our track record includes multiple studies that have changed policy in the fields of neglected parasitic diseases and COVID-19. We work closely with policy organizations like the World Health Organization and the California Department of Public Health. Nathan was the lead writer of the World Health Organization guidelines on schistosomiasis (2022) and strongyloidiasis (2024).
Our current research focuses on the following areas:
(1) Vaccine-preventable infectious diseases (including COVID-19 and measles) in the United States, with a focus on studying vaccines, transmission dynamics, and re-emergence of vaccine-eliminated diseases
(2) Public health strategies for control and elimination of globally important neglected infectious diseases, such as helminths infections (schistosomiasis, strongyloidiasis) and typhoid fever
Our current NIH funded projects include:
(1) Real-time predictive modeling for public health departments to control infectious diseases (DP2 AI170485, PI: Lo)
(2) Precision mapping of Schistosoma mansoni risk for targeted public health control and elimination (R01 AI179771, PI: Lo)
Hiring
We are seeking to fill multiple research positions at all levels. Candidates interested in working on computational public health research related to infectious diseases with a strong quantitative background are highly encouraged to apply. If you an interested, please submit a cover letter, CV, and names of two references to Nathan.Lo@stanford.edu. -
Quentin Loisel
Postdoctoral Scholar, SCRDP/ Heart Disease Prevention
BioQuentin Loisel is a postdoctoral researcher at the Meta-Research Innovation Center at Stanford (METRICS), where his work focuses on how artificial intelligence is transforming scientific practice and how researchers can use AI to produce better, more robust, and more equitable science. His broader agenda is to help define a hybrid model of scientific inquiry that deliberately and transparently combines human judgment and artificial intelligence.
His research sits at the intersection of artificial intelligence, epistemology of science, and research systems. He studies how AI tools reshape knowledge production across the research lifecycle, from problem formulation and data analysis to writing, peer review, and governance, and examines the epistemic, methodological, and institutional consequences of human–AI collaboration in science. His work aims to move beyond risk-focused or purely technical perspectives by developing evidence-based, researcher-centric models for integrating AI into everyday scientific practice.
Before joining Stanford, he completed a Marie Skłodowska-Curie PhD on digital technologies for co-creation, combining cognitive science, collective intelligence, and participatory research. He has co-funded and is coordinating the Artificial Intelligence working group of the Marie Curie Alumni Association (MCAA), which is a researcher-driven community of practice on AI in research. He also advises a social company, called Health Cascade, on how to integrate AI in teams to solve complex societal problems.
