Stanford University
Showing 23,921-23,930 of 34,972 Results
-
Keven Pi
Undergraduate, Vice Provost for Undergraduate Education
Bio650 788 4801
-
Pamela Piacente-Rhodes, PA-C, MA
Affiliate, Central Mgmt-Misc AR
BioPamela Piacente-Rhodes, PA-C, MA, is Physician Assistant (PA) in the Department of Urology at Stanford Healthcare. She is a graduate of D'Youville College PA Program in Upstate New York. She also obtained a Masters Degree in Microbiology from the State University of New York at Buffalo. Initially trained in Urologic Oncology Robotic Surgery at Roswell Park Comprehensive Cancer Center in Buffalo, New York. She continues to clinically practice in the area of Urologic Oncology, specializing in Robotic Assisted Laparoscopic Surgery.
-
Piero Pianetta
Professor (Research) of Photon Science and of Electrical Engineering
BioPianetta's research is directed towards understanding how the atomic and electronic structure of semiconductor interfaces impacts device technology pertaining to advanced semiconductors and photocathodes. His research includes the development of new analytical tools for these studies based on the use of synchrotron radiation. These include the development of ultrasensitive methods to analyze trace impurities on the surface of silicon wafers at levels as low as 1e-6 monolayer (~1e8 atoms/cm2) and the use of various photoelectron spectroscopies (X-ray photoemission, NEXAFS, X-ray standing waves and photoelectron diffraction) to determine the bonding and atomic structure at the interface between silicon and different passivating layers. Recent projects include the development of high resolution (~30nm) x-ray spectromicroscopy with applications to energy materials such as Li batteries.
-
Tanmoy Sarkar Pias
Postdoctoral Scholar, Urology
BioI am currently working on multimodal, multi-task foundation models to detect cancer and improve surgery. I am exploring image segmentation models, foundation models, and reinforcement learning with agents. My previous work spans a range of directions, including knowledge-guided machine learning models, systematic evaluation of high-risk models, mitigation of deficiencies and biases, automatic generation of gradient-based test cases, decision boundary estimation and analysis of deep learning models, and developing approaches to make machine learning models more fair and reliable.
