Stanford University
Showing 1-6 of 6 Results
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Giovanni Marco Saladino
Postdoctoral Scholar, Radiology
BioI am a Postdoctoral Scholar in the Department of Radiology at Stanford University. I graduated in Engineering Physics with a BSc at Politecnico di Milano (Italy) and an MSc at KTH Royal Institute of Technology (Sweden). In 2024, I obtained my PhD in Biological and Biomedical Physics from the Department of Applied Physics at KTH Royal Institute of Technology.
My research interests lie at the intersection of molecular imaging, nanomedicine, and nanomaterials. Specifically, I focus on developing novel contrast agents and exploring advanced imaging techniques. During my PhD studies, I designed hybrid multimodal contrast agents for complementary imaging using X-ray fluorescence computed tomography, magnetic resonance imaging, and optical fluorescence imaging. I am currently involved in investigating theranostic applications of nanomaterials, which hold great promise for personalized medicine and targeted therapies. -
Shailja
Postdoctoral Scholar, Radiological Sciences Laboratory
BioShailja is an engineer and computational scientist interested in the modeling of the human brain to study neurological diseases and guide neurosurgeries. As a Wu Tsai Neuroscience Institute’s postdoctoral fellow with Prof. Jennifer A. McNab and Prof. Josef Parvizi, she investigates tractography-based neurosurgical targeting. She is interested in mapping the whole brain structural connectivity network from diffusion MRI to functional connectivity in the human brain. Shailja received her PhD in Electrical and Computer Engineering from the University of California, Santa Barbara and BS from Electrical Engineering Department, Indian Institute of Technology, Kharagpur. Her doctoral research is on Reeb graphs for modeling white matter fibers in the human brain, which was awarded the Winifred and Louis Lancaster Best PhD Dissertation at UC Santa Barbara.
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Liyan Sun
Postdoctoral Scholar, Radiological Sciences Laboratory
Current Research and Scholarly InterestsPhysics-driven deep learning algorithms for MRI/CT reconstruction and analysis:
(1) MRI acceleration with partial measurements.
(2) Medical image segmentation under limited data resources.
(3) Unsupervised/supervised medical image synthesis for MRI or CT.
(4) Longitudinal medical data analysis with deep learning models.
(5) PET image reconstruction and analysis.
