Stanford University
Showing 10,961-10,970 of 13,033 Results
-
Valerie Sugiyama, MD
Clinical Associate Professor, Obstetrics & Gynecology - Gynecologic Oncology
BioDr. Valerie Sugiyama is a Clinical Assistant Professor of Obstetrics & Gynecology - Gynecologic Oncology and a double-boarded specialist in obstetrics and gynecology and gynecology oncology.
Dr. Sugiyama has multiple peer-reviewed journal publications in high impact journals such as the Journal of Clinical Oncology and Obstetrics and Gynecology. She has also given presentations on the effect of socioeconomic status on the survival of ovarian cancer patients.
Dr. Sugiyama is passionate about treating patients who have gynecological malignancies or who have surgical specialty needs. She has worked both in the academic and community hospital settings since 2009. -
Ayesha Sujan
Postdoctoral Scholar, Anesthesiology, Perioperative and Pain Medicine
BioAyesha C. Sujan, PhD, is a licensed clinical psychologist with specialized clinical training in pediatric pain psychology and extensive research experience in pharmacoepidemiology, particularly in using large administrative datasets to study central nervous system medication and substance use during pregnancy. She is currently a postdoctoral fellow under the primary mentorship of Dr. Jennifer Rabbitts, Chief of Pediatric Pain, in the Department of Anesthesiology, Perioperative and Pain Medicine at Stanford University School of Medicine, and is supported by an NIH T32 training grant (T32GM089626) focused on developing leaders in academic anesthesiology and pain medicine research. In addition to contributing to Dr. Rabbitts’ NIH-funded research on mechanisms and treatment of pain in youth undergoing surgery, she leads independent studies on pediatric chronic abdominal pain and disorders of gut–brain interaction, with a growing focus on central nervous system medication treatment for these conditions. Clinically, she conducts psychosocial assessments and provides evidence-based pain psychology treatment one day per week (20% FTE) in the outpatient pediatric pain management clinic at Stanford University School of Medicine, ensuring strong clinical grounding and translational relevance of her research program.
-
Walter Sujansky
Adjunct Professor, Division of Computational Medicine
BioWalter Sujansky, MD PhD is an Adjunct Professor of Biomedical Informatics at the Stanford Center for Biomedical Informatics Research in the Stanford Department of Medicine. Dr. Sujansky co-teaches BMI-210 Modeling Biomedical Systems, where he lectures on a variety of topics, including deep neural networks, probabilistic reasoning, electronic health records, and health data integration and interoperability. He also advises students in the Biomedical Data Science Graduate Program, an interdisciplinary graduate and postdoctoral training program that is part of the Department of Biomedical Data Science. His research interests include the modeling of biomedical concepts based on formal logic and the engineering of features for biomedical machine learning algorithms.
Dr. Sujansky earned an M.D. and a Ph.D. in Medical Informatics from Stanford University, where his doctoral research addressed heterogeneous database integration and clinical decision support. He also earned a B.A. in Economics from Harvard University.
Dr. Sujansky is also the managing consultant at Sujansky & Associates, LLC, a consulting firm that specializes in the representation, management, and analysis of clinical data in information systems. In this capacity, his work focuses on the modeling of complex biomedical data related to patient phenotyping, clinical genomics, quality measurement, automated decision support, and machine learning. His firm has helped to develop shared computing resources such as the California Joint Replacement Registry and the Laboratory Interoperability Data Repository. The firm's clients include the federal and state governments, non-profit organizations, health information system developers, and drug/device manufacturers. Dr. Sujansky also provides forensic analysis of health information technologies for medical malpractice and intellectual property litigation.
