Stanford University
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Yang Merik Liu
Postdoctoral Scholar, Psychiatry
BioDr. Yang Merik Liu is currently a postdoctoral scholar (and an incoming Instructor) with the Department of Psychiatry and Behavioral Sciences, Stanford University, and is affiliated with the Center for Machine Vision and Signal Analysis, University of Oulu, Finland. He is a Co-I of the NIH/NIA R33 Grant, and was a PI of the North Ostrobothnia Regional Fund of the Finnish Cultural Foundation and the Instrumentarium Science Foundation, carrying out research on digital measures with affective intelligence. Dr. Liu coordinated and managed "AI Forum" and "ICT 2023 TrustFace" projects during his postdoctoral research in University of Oulu since Jan. 2022, led by Academy Professor Guoying Zhao, member of Academia Europaea, member of the Finnish Academy of Sciences and Letters, IEEE/IAPR/ELLIS Fellow. He was also a former researcher with the Haaga-Helia University of Applied Sciences, in 2023, and was a visiting scholar with Hong Kong Baptist University (Prof. Pong Chi Yuen) and University of Cambridge (Prof. Hatice Gunes), in 2023 and 2024, respectively. Dr. Liu has published more than 40 papers in reputable journals and proceedings. He served as the Session Chair of IEEE FG 2025, the Track Chair of IEEE COINS 2026, the Guest Associate Editor of Frontiers in Psychology and Frontiers in Human Neurosciences, and organized tutorials and workshops in international conferences, i.e., HHAI 2024 and IEEE FG 2025. Dr. Liu was an Assistant Lecturer of the "Affective Computing" course in University of Oulu, in 2023. He mentored junior doctoral researchers and co-supervised post-/undergraduate students. His research interests include affective computing, cognitive computation for cross-species behavioral, and AI for aging medicine.
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Zhuo Liu
Postdoctoral Scholar, Earth and Planetary Sciences
BioZhuo Liu is a postdoctoral scholar at the Stanford Doerr School of Sustainability, sponsored by Mineral-X. His expertise is in multi-geophysical data interpretation with both traditional methods and machine-learning-based methods for critical mineral exploration.
Zhuo Liu earned his PhD degree in Geophysics, with a minor degree in Geology, from the Colorado School of Mines (CSM), USA. His doctoral and postdoctoral research focused on advancing geophysical data interpretation methods and incorporating geologic prior information into the interpretation process in machine-learning and geostatistical approaches for mineral resources exploration.
Previously, Zhuo earned his Bachelor's degree in Applied Geophysics from the Central South University, China, and a Master's degree in Geophysics from the Colorado School of Mines (CSM), USA. He also visited the University of Science and Technology of China (USTC) as a Student Visiting Scholar under the mentorship of Dr. Xinming Wu in 2021. -
Michael LoCascio
Postdoctoral Scholar, Civil and Environmental Engineering
BioMichael's work focuses on wind energy at the intersection of computational fluid dynamics, controls, and optimization. He is interested in wake modeling, wind farm layout optimization, and large eddy simulations of wind farm flows. He is currently working on a low-cost model for the annual energy production of wind farms. Michael is also a graduate researcher at the National Wind Technology Center, a research facility of the National Renewable Energy Laboratory. He received his Master of Science in Mechanical Engineering from Stanford in 2023 and his Bachelor of Science in Mechanical Engineering from UCLA in 2020.
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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.
