Stanford University
Showing 151-200 of 228 Results
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Kuang Xu
Associate Professor of Operations, Information and Technology at the Graduate School of Business and, by courtesy, of Electrical Engineering
BioKuang Xu is an Associate Professor of Operations, Information and Technology at Stanford Graduate School of Business, and Associate Professor by courtesy with the Electrical Engineering Department, Stanford University. Born in Suzhou, China, he received the B.S. degree in Electrical Engineering (2009) from the University of Illinois at Urbana-Champaign, and the Ph.D. degree in Electrical Engineering and Computer Science (2014) from the Massachusetts Institute of Technology.
His research primarily focuses on understanding fundamental properties and design principles of large-scale stochastic systems using tools from probability theory and optimization, with applications in queueing networks, healthcare, privacy and machine learning. He received First Place in the INFORMS George E. Nicholson Student Paper Competition (2011), the Best Paper Award, as well as the Kenneth C. Sevcik Outstanding Student Paper Award at ACM SIGMETRICS (2013), and the ACM SIGMETRICS Rising Star Research Award (2020). He currently serves as an Associate Editor for Operations Research and Management Science. -
Kun Xu
Postdoctoral Scholar, Mechanical Engineering
Current Research and Scholarly InterestsMaterials characterization by using advanced electron microscopy
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Maya Emily Xu
Bachelor of Science, Honors, Biology with Honors
Masters Student in Biology, admitted Autumn 2022
Minor, Education
Stanford Student Employee, BiologyBioI'm an undergraduate ('25) and coterminal masters student majoring in biology (concentrating in ecology, evolution and environment). I previously completed a minor in education, a Notation for Science Communication, and will co-instruct BIO 121/221 (Ornithology) for the third time this spring.
Broadly, I'm interested in three main topics (which all have to do with birds!): 1) how birds can be used as indicator or sentinel species for environmental disturbance; 2) how interactions between humans and birds are shifting thanks to gradients of anthropogenic change; and 3) how these interactions can be shaped to better promote wider ecological health and beneficial services. I'm currently in the middle of a year-long study with Marty Freeland, funded by Jasper Ridge Biological Preserve's ('Ootchamin 'Ooyakma) (JROO) Mellon Grant, to compare the riparian bird communities at JROO and TomKat Ranch using three different survey methodologies (in-person transects, passive acoustic monitoring, and mob tape deployments). I'm also working closely with the San Francisco Bay Bird Observatory (SFBBO), where I volunteer as a bird banding trainee, and the Stanford SIGMA lab to quantify heavy metal contamination in the feathers of songbirds caught at the bird banding stations in JROO and the SFBBO's main station in Milpitas.
I previously conducted my senior honors thesis on how heavy metals affect raptors on the North American Pacific coast. My primary study species were the peregrine falcons (Falco peregrinus) breeding on top of Stanford University’s Hoover Tower, and the golden eagles (Aquila chrysaetos) breeding at JROO, where I'm a docent and former avian transect leader. -
Pei Xu
Postdoctoral Scholar, Computer Science
Current Research and Scholarly Interestscharacter animation, physics-based character control, crowd simulation
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Renyuan Xu
Assistant Professor of Management Science and Engineering
BioRenyuan Xu is an assistant professor of Management Science and Engineering (MS&E) at Stanford University. Prior to joining Stanford, she held positions at New York University (2024-2025) and the University of Southern California (2021–2024), and was a Hooke Research Fellow at the Mathematical Institute, University of Oxford (2019–2021). She received her Ph.D. in Operations Research from the University of California, Berkeley in 2019. Renyuan's current research interests include mathematical finance, stochastic analysis, stochastic controls and games, and machine learning theory. She received an NSF CAREER Award in 2024, the SIAM Activity Group on Financial Mathematics and Engineering Early Career Prize in 2023, and two JP Morgan AI Faculty Research Awards in 2022 and 2025.
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Sheng Xu
Professor of Anesthesiology, Perioperative & Pain Medicine (Department Research) and, by courtesy, of Electrical Engineering
BioDr. Sheng Xu is a tenured professor and the inaugural Director of Emerging Technologies in the Department of Anesthesiology, Perioperative and Pain Medicine at Stanford University, with a courtesy appointment in Electrical Engineering. He earned his B.S. degree in Chemistry from Peking University and his Ph.D. in Materials Science and Engineering from the Georgia Institute of Technology. Subsequently, he pursued postdoctoral studies at the Materials Research Laboratory at the University of Illinois at Urbana-Champaign. He then spent 10 years on the faculty at UC San Diego before joining Stanford in 2025. His research group is interested in developing new materials and fabrication methods for soft electronics. His research has been presented to the United States Congress as a testimony to the importance and impact of NIH funding.
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Shiqin Xu
Senior Program Manager, HEART Lab, Medicine - Med/Cardiovascular Medicine
Current Role at StanfordSenior Program Manager, HEART Lab
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Weize Xu
Postdoctoral Scholar, Genetics
BioDr. Weize Xu is a postdoctoral researcher in Dr. Xiaojie Qiu's laboratory, where he focuses on advancing computational biology and genomics research. He earned his Ph.D. in Dr. Gang Cao's lab, where he made significant contributions to the development of computational methods and pipelines for spatial transcriptomics (MiP-Seq) and single-cell Hi-C (sciDLO Hi-C). His work during this time centered on enhancing data analysis frameworks, providing more precise insights into complex biological systems.
Dr. Xu is also an expert in the development of bioimaging processing softwares. During his Ph.D., he developed the U-FISH method, a deep learning-based approach for detecting signal points in FISH images. This innovative project involved curating a high-quality dataset from diverse sources, ensuring robust performance across various FISH data types. The resulting model demonstrated outstanding generalizability and included a user-friendly Web and LLM interface, making it accessible to researchers worldwide.
In addition to his Ph.D. research, Dr. Xu further honed his skills at SciLifeLab, where he worked under the mentorship of Dr. Wei Ouyang. There, he focused on web programming and developing key components for the Bioimage.IO deep learning platform, gaining valuable experience in creating innovative tools for computational biology.
With a solid foundation in computational biology, deep learning, and bioinformatics, Dr. Xu is passionate about driving cutting-edge research and contributing new perspectives to his field. He brings a unique combination of technical expertise and a collaborative mindset to his role in Dr. Xiaojie Qiu’s lab.
