Stanford University
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Siavash Moghadami
Ph.D. Student in Chemical and Systems Biology, admitted Summer 2022
BioSiavash Moghadami is a Ph.D. student in Chemical and Systems Biology at Stanford University School of Medicine, co-mentored by Professors Carolyn Bertozzi and Longzhi Tan. His work sits at the intersection of chemical biology, neuroscience, artificial intelligence (AI), and aging, with a long-term vision of building programmable brain–body physiology for healthy longevity so that fewer families have to watch their loved ones age in frailty.
Before Stanford, Siavash earned his B.Sc./M.Sc. in Biochemistry and Chemical Biology from the University of California, San Diego, graduating summa cum laude with highest departmental distinction and honors.
A proud immigrant pursuing the American dream, Siavash feels a profound sense of love and gratitude for the United States, which gave him a new home and a path into higher education and scientific discovery. His research on brain–body physiology and healthy longevity is, in many ways, his way of giving back—honoring the opportunities he found in America and working to protect the health and independence of his own and others’ loved ones. -
Ariam Mogos
Lecturer
BioAriam Mogos leads emerging technology initiatives at Stanford's Hasso Plattner Institute of Design (d.school), where she helps students and educators work with emerging technologies like AI and blockchain, and shapes conversations around the tech’s ethical implications on humans and nature. Her design work and research also investigates the ways that technology can foster playful learning experiences that bridge communities and cultures.
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Shayan Mohajer Hamidi
Postdoctoral Scholar, Electrical Engineering
Current Research and Scholarly InterestsReasoning in large language models (LLMs) and improving their systematic generalization
Post-training and fine-tuning methods for alignment, reliability, and efficiency
Autonomous agent architectures built on top of foundation models
Generative modeling with diffusion models and their multimodal applications
Theory and optimization methods for modern deep learning systems
