Stanford University
Showing 21,621-21,630 of 34,991 Results
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Koosha Nassiri Nazif
Affiliate, Program-Pop, E.
BioDr. Koosha Nassiri Nazif received his Ph.D. in Electrical Engineering (Jan 2022) and his M.S. in Mechanical Engineering (2016) from Stanford University. Along the way, he worked at Apple (2019) on OLED/LCD displays and at HP Labs (2017) on 3D electronics thermal management. He is currently a post-doctoral scholar at Stanford developing novel flexible optoelectronic devices, including solar cells and wearable sensors, based on 2D transition metal dichalcogenides. https://www.arinna.xyz.
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Adi Natan
Staff Scientist, Energy Sciences
Current Role at StanfordPrincipal investigator, Stanford PULSE Institute
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Arutselvan Natarajan
Senior Research Scientist - Basic Life, Rad/Molecular Imaging Program at Stanford
Current Role at StanfordSenior Scientist
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Fateme (Fatima) Nateghi
Postdoctoral Scholar, Biomedical Informatics
BioAs a postdoc researcher at the Division of Computational Medicine, I find myself at the exciting intersection of machine learning and healthcare. My journey began with a PhD in Biomedical Sciences from KU Leuven in Belgium, where I explored the complexities of machine learning algorithms and their transformative potential in clinical settings. My research focused on adapting these algorithms for time-to-event data, a method used to predict when specific events may occur in a patient’s future.
At Stanford, my work centers on building trustworthy AI systems to enhance healthcare delivery. I develop and evaluate machine learning models that integrate structured electronic health records (EHRs) and unstructured clinical notes to support real-world clinical decision-making. My recent projects include predicting treatment retention in opioid use disorder, improving antibiotic stewardship for urinary tract infections, and enabling digital consultations through large language models (LLMs). I'm particularly interested in embedding-based retrieval and retrieval-augmented generation (RAG) methods that help bridge cutting-edge AI research with clinical practice.
My role involves not just advancing the integration of machine learning in healthcare but also collaborating with a diverse team of clinicians, data scientists, and engineers. Together, we're striving to unravel complex healthcare challenges and ultimately improve patient outcomes.
