Stanford University
Showing 821-840 of 1,659 Results
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Kerri E. Rieger, MD, PhD
Clinical Professor, Pathology
Clinical Professor, DermatologyBioDr. Rieger is a Clinical Professor of Pathology and Dermatology at Stanford University. She received her M.D., Ph.D. from Stanford University School of Medicine and completed her Dermatology Residency and Dermatopathology Fellowship at Stanford University. She is board certified in Dermatology and Dermatopathology. She evaluates skin specimens in the Pathology department, where her interests include histopathologic findings in cutaneous lymphoma, hospitalized patients, and patients with autoimmune disease. She also sees patients in the Stanford dermatology clinic in Portola Valley, where her clinical interest is adult general dermatology.
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Tracey Riesen
Student Services Officer, Language Ctr
BioTracey is the Student Services Officer for the Stanford Language Center. She is responsible for all undergraduate and graduate student-related activities in the Language Center; this includes language advising, certification of the Language Requirement, academic records for the 6000 students who take foreign language courses each year, language credit transfers, and administration of the Advanced Proficiency Notation. She is the primary contact person for students, as well as for language program coordinators within the Language Center. She also manages the English for Foreign Students (EFS) summer intensive English program for incoming international graduate students and visiting scholars. She greatly enjoys being of service to Stanford students and values working in such a diverse and dynamic community.
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Mouhssine Rifaki
Graduate Visiting Researcher Student, Electrical Engineering
Current Research and Scholarly InterestsI train embodied agents whose perception and foveation adapt while they act. Sensor modality switches and the placement of high-resolution attention are driven by prediction errors from a lightweight world model of near-term observations. The same prediction errors close the loop on control: the policy reads from its currently active sensors, acts, and reshapes what those sensors will see next.
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Fran Riley
Clinical Assistant Professor, Emergency Medicine
BioDr. Fran Riley is a physician engineer and Clinical Assistant Professor at Stanford University. She obtained an undergraduate degree in electrical engineering and robotics, she obtained her undergraduate degree from the University of Waterloo where she focused on robotics. Driven by her passion for merging medicine and technology, she pursued a Master's degree in Computer Science at The Johns Hopkins University, where she developed a motor controller for an early prototypes of the Da Vinci robot for ENT surgical applications. Dr. Riley's research also focused on utilizing real-time monitoring data to enhance the treatment of traumatic brain injuries in the pediatric intensive care unit.
Following her work in robotics and computer science, Dr. Riley transitioned to the healthcare industry, where she served as a product manager at Microsoft. In this role, she lead multidisciplinary teams to develop multiple features for an electronic medical record dedicated to data analytics. The product was then acquired by GE Healthcare.
Dr. Riley then pursued a medical degree at the University of Vermont, followed by a residency and chief residency at Maimonides Medical Center. She then completed a fellowship in emergency ultrasound at Columbia University Medical Center.
At Stanford, Dr. Riley is an integral part of the Stanford Emergency Medicine Partnership Program (STEPP), utilizing her technical expertise to identify industry partners for research collaborations and product development. She also actively contributes to a hospital-wide committee dedicated to evaluating the use of informatics for clinical care, prioritizing patient safety and high-quality care.
Dr. Riley's clinical research focuses on leveraging artificial intelligence in image recognition for regional wall motion abnormalities, specifically utilizing point-of-care ultrasound to diagnose acute coronary syndrome.
