Stanford University
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Seena Dehkharghani MD FAHA
Clinical Professor, Radiology
BioMy research spans human, animal, and phantom studies of cerebral perfusion and metabolism, emphasizing thermometry and neuroenergetics. My investigations into ischemic thermometry were twice recognized with the Foundation of the ASNR Scholar Award in Neuroradiology Research and subsequently by a R01 awarded by the NIBIB. My group has developed novel computational models for cerebral hemodynamics (dynamic BOLD-CVR) and first-of-kind hyperportable microwave stroke detection tools using deep learning. I have obtained formal certification (Mathworks, Google) in machine learning and data analytics and remain engaged in self-learning in AI/ML methodologies. I have also been a post-graduate student through the Stanford University School of Engineering SCPD program in Computer Science, again with a focus in artificial intelligence through the Stanford University School of Engineering, with core competencies including machine learning, Bayesian Networks, Markov Decision Processes, and Deep Learning.
I formerly served as Director of Stroke and Cerebrovacular Imaging Programs at Emory University and New York University, leading basic, translational, and clinical cerebrovascular imaging programs in collaboration with academic and industry partners worldwide. I returned to Stanford in Feb 2025 as Professor and Associate Chair of Informatics, and as Affiliate Faculty of the Wu Tsai Neuroscience Institute, Stanford Artificial Intelligence in Medicine and Imaging (AIMI) Center, Integrated Biomedical Imaging Informatics at Stanford (IBIIS), Center for Digital Health (CDH), and Human-Centered AI (HAI) Institute.
Ongoing projects that I would like to highlight include:
1R01EB034820-01 (NIBIB).
Previously, I was funded in an application to the Google AI Social Impact Challenge for the development of portable ultra-wideband microwave imaging instrumentation (patents issued), work for which I was selected to the NIH-NIBIB c3i Concept to Clinic: Commercializing Innovation Program.
Presently, we have proposed to develop realistic computational models of ischemic stroke and bridge a heretofore unattempted multiscale and multiphysics view of neuroenergetic disturbance in the ischemic brain. The merger of a state of the art in silico neurobiological canvas with more than a decade of insights from rigorous experimental neuroscience and biophysical modeling points us to disturbed thermoregulation/dysthermia at the critical intersection of cerebral perfusion, metabolism, viability, and hence neuroenergetic balance.
Cerebral dysthermia will be formalized in a radical departure from the historically perfusion-centered frame of reference in cerebrovascular ischemia, the latter having shown unfit to capture fully the adaptive, dynamic metabolic changes that subserve the ischemic brain. We leverage the pathoanatomic realism of our complete multiscale vascular, flow, and perfusion model tuned to direct measures of cerebral perfusion, oximetry, and thermography across hemodynamics states in individuals subjected to hemodynamics provocation, linking two procedures developed by my lab: time-resolved BOLD cerebrovascular reactivity with acetazolamide and the brain thermal response (BTR). This work represents a new direction of cerebrovascular and neurometabolic research enabled by traditionally unattainable biomarker ensembles based in non-invasive, spatiotemporally-resolved brain thermography. -
Iván Deiana
Ph.D. Student in Geophysics, admitted Autumn 2023
BioPh.D. student in Geophysics at Stanford Earth Imaging Project (SEP). Research interests include Geophysical Inverse Problems, Quantitative Interpretation, and Earth Imaging, integrated with HPC and ML.
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Gregory Deierlein
John A. Blume Professor in the School of Engineering
BioDeierlein's research focuses on improving limit states design of constructed facilities through the development and application of nonlinear structural analysis methods and performance-based design criteria. Recent projects include the development and application of strength and stiffness degrading models to simulate steel and reinforced concrete structures, seismic design and behavior of composite steel-concrete buildings, analysis of inelastic torsional-flexural instability of steel members, and a fracture mechanics investigation of seismically designed welded steel connections.
