Stanford University
Showing 781-790 of 6,037 Results
-
Ying Chih Chang
Adjunct Professor, Chemical Engineering
BioDr. Ying Chang is the Chair of the Taiwan Science and Technology Hub and an Adjunct Professor in the Department of Chemical Engineering at Stanford University. She is also the Founder and CEO of Acrocyte Therapeutics, Inc.
Her former faculty appointments include Research Fellow (-Associate Fellow) at the Genomics Research Center, Academia Sinica, Taiwan, and Assistant Professor in the Departments of Chemical Engineering and Materials Science, and Biomedical Engineering at the University of California, Irvine. The research highlights include integrating nanomaterials, microfluidics, and bioreactors to control cell fates for tissue engineering, as well as developing circulating tumor cell 3D culture for cancer diagnostics and precision medicine. She holds multiple patents in DNA microarray constructs (assigned to Affymetrix), circulating tumor cell isolation and cancer screening (assigned to Cellmax Life Inc.), and single cell derived scaffold free 3D culture platform RCE and circulating tumor cell liquid biopsy drug test (assigned to Acrocyte Therapeutics Inc. www.acrocyte.com).
Dr. Chang’s work was recognized by the Young Investigator Award from the Whitaker Foundation, FDA breakthrough device designation for pre-cancer and cancer detection (2021), Future Tech Award (2021), 18th National Innovation Award, Taiwan (2021), and Finalist at Startup Stadium at BIO 2024, among other. She is also on the board of trustees of Kaohsiaung Medical University, and Chang Chau-Ting Memorial Foundation, a non-profit organization for science education and awareness in Taiwan. She received her BS from National Taiwan University, and PhD from Stanford University, both in Chemical Engineering. -
Moses Charikar
Donald E. Knuth Professor
Current Research and Scholarly InterestsEfficient algorithmic techniques for processing, searching and indexing massive high-dimensional data sets; efficient algorithms for computational problems in high-dimensional statistics and optimization problems in machine learning; approximation algorithms for discrete optimization problems with provable guarantees; convex optimization approaches for non-convex combinatorial optimization problems; low-distortion embeddings of finite metric spaces.
-
Masoud Charkhabi
Graduate, Stanford Center for Professional Development
BioFull-time data scientist <mcharkhabi@apple.com>
Part-time CS student <masoudc@stanford.edu>
