Summary
Mahsa Yazdani is a bioinformatics and spatial multiomics scientist with 7 years of interdisciplinary experience bridging wet-lab spatial proteomics/transcriptomics and computational analysis. She has led DBiT-seq studies on human lung and brain organoid tissues, integrated scRNA-seq/CITE-seq datasets, and developed analysis pipelines using R, shell scripting, and Python to drive insights into disease biology and therapeutic screening. Her industry experience includes applying machine learning, deep-learning segmentation (Cellpose), and rigorous benchmarking approaches to Optical Pooled CRISPR screens at Merck, and she has built interactive Streamlit tools to make complex results accessible to biologists. Trained across biomedical engineering, pharmaceutical science, and computer engineering programs, she combines microfluidics and simulation background with spatial omics expertise—a mix that helps translate experimental nuance into robust computational workflows.
7 years of coding experience
9 years of employment as a software developer
Interdisciplinary Program, Mechanical Engineering/ Pharmaceutical Science, Interdisciplinary Program, Mechanical Engineering/ Pharmaceutical Science at University of Missouri-Kansas City
Bachelor's degree, Food Science and Technology - Quality Control, Bachelor's degree, Food Science and Technology - Quality Control at Shahid Beheshti University of Medical Sciences
Doctor of Philosophy - PhD, Biomedical/Medical Engineering, Doctor of Philosophy - PhD, Biomedical/Medical Engineering at University at Buffalo
Master of Science (MS), Pharmaceutical Engineering, Master of Science (MS), Pharmaceutical Engineering at University of Tehran
English, Persian