Stanford University
Showing 5,051-5,060 of 34,952 Results
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Yinghan Chen
Undergraduate, Computer Science
BioI am currently working with The Movement Lab (TML) at the Department of Computer Science, advised by Dr. Karen Liu.
My research lies at the intersection of robot learning, physics-based simulation, grasping and manipulation, and multimodal perception. I am broadly interested in enabling embodied agents to understand physical structures, reason about dynamics, and perform dexterous manipulation through integrated multimodal sensing. My recent work spans visual–tactile sensing and learning, dexterous manipulation, tool-use and design, and differentiable simulation. I have published or submitted papers to top venues in robotics and embodied AI including RA-L, IROS and CoRL, and I hope to continue probing the deeper principles underlying intelligent robotic systems!
My long-term goal is to develop general-purpose robotic intelligence capable of perceiving, planning, and acting in the physical world with human-level adaptability and finesse, ultimately enabling robots to assist humans in everyday, unstructured environments.
For more details, please visit: www.yinghanchen.com -
Yiyun Chen
Postdoctoral Scholar, Stanford Cancer Institute
BioYiyun Chen is a computational cancer immunologist whose interdisciplinary training spans structural biology, computational genomics, and cancer immunotherapy. During her doctoral training at HKUST, she developed multi-omic frameworks to decode the molecular landscape of brain tumors, gastric cancer, and B cell lymphoma — including the discovery of a tumor-associated monocyte population in the glioma microenvironment that drives mesenchymal transformation through the FOSL2-EREG/AREG-EGFR signaling axis.
As a postdoctoral fellow in the Crystal Mackall Laboratory at Stanford Cancer Institute, she extended this focus to the co-evolution of glioma and the immune system during CAR T cell therapy, uncovering multiple mechanisms of acquired resistance: anti-CAR humoral and cellular immunity, proinflammatory-to-immunosuppressive phenotypic shifts in macrophages, and tumor antigen escape. Her future research program will build an AI-powered platform that integrates longitudinal single-cell and spatial transcriptomics to model tumor-immune co-evolution in silico — constructing patient-level digital twins that simulate treatment trajectories, predict resistance, and identify real-time monitoring biomarkers.
