Andrea Flores Perez - Bruce and Elizabeth Dunlevie Fellow
Awarded in 2025
Home Department: Bioengineering
Faculty Advisors: Yunzhi Yang (Orthopaedic Surgery), Guillem Pratx (Radiation Oncology), and John Sunwoo (OHNS/Head & Neck Surgery Divisions)
Awarded in 2025
Home Department: Bioengineering
Faculty Advisors: Yunzhi Yang (Orthopaedic Surgery), Guillem Pratx (Radiation Oncology), and John Sunwoo (OHNS/Head & Neck Surgery Divisions)
Dr. Goldstein-Piekarski directs the Computational Psychiatry, Neuroscience, and Sleep Laboratory (CoPsyN Sleep Lab) as an Assistant Professor in the Department of Psychiatry and Behavioral Sciences at Stanford University School of Medicine and PI within the Sierra-Pacific Mental Illness Research, Education and Clinical Center (MIRECC) at the Palo Alto VA. She received her PhD in 2014 at the University of California, Berkeley where she studied the consequences of sleep on emotional brain function.
Dr. Andrea Montanari is a Professor in Statistics and Mathematics at Stanford University. He received a Laurea degree in Physics in 1997, and a Ph.D. in Physics in 2001 (both from Scuola Normale Superiore in Pisa, Italy). He has been post-doctoral fellow at Laboratoire de Physique Théorique de l'Ecole Normale Supérieure (LPTENS), Paris, France, and the Mathematical Sciences Research Institute, Berkeley, USA. From 2002 to 2010 he was Chargé de Recherche (with Centre National de la Recherche Scientifique, CNRS) at LPTENS.
Awarded in 2004
Home Department: Bioengineering, Medicine
Faculty Advisor: Sanjiv Sam Gambhir (Radiology, Bioengineering)
Home Department: Psychology
Faculty Advisor: Brian Wandell
Talk Title: Abstracting visual information: Reading dynamic word forms
Event: Society for Neuroscience 2010
Dr. Andreas Tolias's lab works on the interface of neuroscience and AI research. They combine systems and computational neuroscience with machine learning approaches to decipher the network level principles of intelligence focusing on perceptual inference and decision making. Engineering these principles in AI systems provides a powerful platform to mechanistically test our understanding of brain function under natural complex tasks and develop the next-generation of less artificial and more intelligent algorithms.