Stanford University
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Leina'ala Song, MD
Clinical Assistant Professor, Orthopaedic Surgery
BioDr. Song is a double board-certified sports medicine physician with Stanford Health Care Orthopaedics and Sports Medicine. She is a clinical assistant professor in the Department of Orthopaedic Surgery at Stanford University School of Medicine. Dr. Song completed fellowship training in orthopaedics and sports medicine at the University of Washington School of Medicine in Seattle, Washington.
Dr. Song specializes in managing a wide range of sports and musculoskeletal injuries. She performs ultrasound-guided injections including corticosteroid, hyaluronic acid, and PRP. She is also skilled at using high-resolution ultrasound to perform other minimally invasive interventions, such as ultrasound guided percutaneous tenotomies and peripheral nerve hydrodissections. She is currently the primary team physician for numerous Division 1 athletic teams at Stanford University, including men’s volleyball, women’s volleyball, beach volleyball, lacrosse, open weight crew, light weight crew, softball, artistic swim, and sailing.
Dr. Song’s research interests include the long-term outcomes of percutaneous ultrasound-guided tenotomy, orthobiologics, and the health of the female athlete. She has taught sports medicine fellows as well as primary care residents. She has provided sideline coverage at multiple athletic events, as well as pre-participation screenings for professional teams such as the Seattle Seahawks and Seattle Seawolves. -
Peiyang Song
Affiliate, Psychology
BioPeiyang Song is a rising senior studying Computer Science at California Institute of Technology (Caltech), advised by Prof. Steven Low, with a minor in Robotics advised by Prof. Günter Niemeyer. He is a researcher in Berkeley AI Research (BAIR) Lab, advised by Prof. Dawn Song and Dr. Jingxuan He. He also works in Stanford AI Lab (SAIL), advised by Prof. Noah Goodman and Dr. Gabriel Poesia in the Computation & Cognition Lab (CoCoLab). His current research interest is mainly in LLM reasoning, especially neuro-symbolic AI for formal math and verifiable code generation. In the past, he also published on neuro-symbolic methods for energy-efficient ML systems and neural machine translation.
