Stanford University
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Aussama Nassar, MD, MSc,FACS, FRCSC
Clinical Associate Professor, Surgery - General Surgery
BioDr. Nassar is board-certified in both General Surgery and Surgical Critical Care in the United States and Canada, underscoring his exceptional expertise in these fields. He holds a Master of Science in Health Science Education from McMaster University, Canada, further solidifying his strong academic foundation.
Currently, Dr. Nassar serves as the inaugural Director of the Emergency General Surgery Service and the Director of the Surgical Critical Care Fellowship Program at Stanford University. He is also the founder and director of the Surgery Resident Coaching Program. With over 12 years of clinical and academic experience at premier tertiary care referral centers across North America, Dr. Nassar brings unparalleled knowledge and leadership to his roles.
Dr. Nassar's clinical interests include trauma, acute care surgery, critical care, and a particular focus on abdominal wall reconstruction. As a dedicated clinician-educator, he is a certified simulation educator and serves as a course director for ATLS and ASSET programs. His research is centered on advancing surgical education, with an emphasis on fostering the development of non-technical skills in surgical training.
In addition to his educational initiatives, Dr. Nassar is actively engaged in developing innovative assessment tools for surgical trainees and addressing burnout among physicians and healthcare professionals. With a strong national and international presence, Dr. Nassar continues to shape the future of surgical education and healthcare delivery through his leadership, research, and advocacy. -
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.
