Machine learning researcher. Developer. Stem cell biologist.
Stanford University Class of 2020 (Computer Science) ezshen@stanford.edu
Using machine learning to predict stem cell aging.
- Obtained blood stem cell gene expression dataset
- Trained multiple supervised classifier models to identify young or old
- Selected critical subset of genes that predicted age with 98.5% accuracy
Currently exploring the therapeutic and rejuvenative implications of the study.
Building a workflow for 3D fluorescent microscopy analysis.
I realized that there were few tools in lab to quantify microscopic images.
- Created a workflow for analyzing 3D images from fluorescent microscopy
- Given surface rendering, relationships can be quantified (distance, density, branches)
Tool increased experimental precision, and was quickly adopted by the lab.
Dev
webapp dev (Ruby on Rails)
web dev (HTML/CSS/Javascript)
database design (SQL)
iOS dev (Swift)
unit/integration testing
machine learning (Scikit-Learn, Tensorflow)
Classes
programming abstractions (Java, C++)
computer systems (C)
machine learning (Python)
Hobbies
basketball
ultimate frisbee
traveling
Mechanoresponsive skeletal stem cells acquire developmental neural crest identity during distraction osteogenesis. Nature (in review, 2017)
Comparative Three-Dimensional Analysis of Human and Mouse Adipose Tissue. J. Mol. Endocrinol. (in review, 2017)
WNT-activated bone grafts repair osteonecrotic lesions in aged animals. Scientific Reports (2017)
Axin2-expressing cells execute regeneration after skeletal injury. Scientific Reports (2016)