This work lead by current student, Alexandria Van Grouw, entitled “Toward Machine Learning Electrospray Ionization Sensitivity Prediction for Semiquantitative Lipidomics in Stem Cells”, was published in ACS affiliated journal, Journal of Chemical Information and Modeling. This work addresses the challenges that arise from variability in electrospray ionization response when comparing multiple batches of both targeted and non-targeted mass spectrometry datasets. As patient recruitment is a long, arduous process among stem cell studies, this is batch-to-batch phenomenon is encountered frequently. In this publication, a machine learning model was developed that predicts electrospray ionization sensitivity for lipid classes that have shown correlation with stem cell potency to help bridge the gaps present between batches and between targeted and non-targeted datasets.