Stanford University
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Tianying Liu
Postdoctoral Scholar, Mechanical Engineering
BioDr. Tianying Liu is currently a Postdoctoral Scholar at Stanford University, focusing on the development of scalable, low-Iridium loading catalysts for cost-effective and durable PEM water electrolyzers. He earned his Ph.D. in Chemistry from Boston College in 2025, where his dissertation research investigated water oxidation mechanisms on Iridium dinuclear heterogeneous catalysts. During his doctoral studies, he served as an ALS Doctoral Fellow at Lawrence Berkeley National Laboratory, applying synchrotron-based ex situ and in situ soft X-ray absorption spectroscopy to uncover the structural dynamics of Iridium catalyst electrodes during water oxidation.
Before his doctoral work, Dr. Liu completed his M.S. and B.S. degrees in Materials Science and Engineering at Central South University. His earlier research experience includes developing Mo-based electrocatalysts for hydrogen evolution, engineering lithium-ion battery cathodes via atomic layer deposition at ShanghaiTech University, and characterizing molybdenum carbide catalysts as a visiting researcher at Northwestern University. His research interests broadly cover electrocatalysis, photoelectrochemistry, energy conversion, and materials design, with a strong focus on renewable energy applications. -
Zhiquan Liu
Postdoctoral Scholar, Ophthalmology
BioDr. Liu’s research focuses on:
1.Developing and optimizing novel genome-editing technologies.
2.Generating humanized animal models of disease using advanced genome-editing approaches.
3.Applying innovative genome-editing technologies to the treatment of ocular diseases. -
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.
