Algorithms and Machine Learning for Healthcare

We develop algorithmic and machine learning methods for applications in healthcare. Our current focus is on computational approaches in targeted cancer treatments, including immunotherapy, where we aim to combine multiple quantitative image-derived parameters to determine the best metrics for evaluating a treatment response in therapy. Other directions include radiomic approaches for faster COVID-19 diagnostics, scalable tSNE and UMAP for cancer cell analysis, and fast methods for single-Cell RNA-sequencing for data sets spanning millions of cells .

Relevant Publications: