Maryam Vareth is an enthusiastic engineer who is passionate in applying mathematics and physics to solve unmet needs in healthcare and life sciences. She is an advocate for “data-driven” medicine and keen on meaningfully extracting clinically relevant insights from large-scale medical data, more specifically to directly link medical imaging data to medical diagnostics and therapeutics and moving her community towards open source and open research practices.
She received her PhD, MS and BS training from UC Berkeley. Her doctoral work focused mainly in developing new techniques and algorithms for acquisition, reconstruction and quantitative analysis of Magnetic Resonance Spectroscopy Imaging (MRSI) with the goal of improving the speed, sensitivity and specificity of the data obtained for the management of patients with brain tumor. Her post-doctoral research was the continuation of her PhD work with emphasis in using a combination of structural, physiological, and metabolic imaging data from large clinical trials to quantitatively characterize heterogeneity within malignant brain tumors.
As an associate specialist, her research will be focusing in exploring the role of machine learning and other big data approaches in extracting contributors to brain tumor and developing tools for predicting pathologic & molecular characteristics of tumor aggressiveness using multi-parametric MRI in patients with Glioma with ultimate goal of developing a completely data-driven model that is able to extract imaging features and use them to identify risk factors and predict outcomes and translate this knowledge into the clinic.