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Algorithms and Machine Learning for Digital Health
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:
- IEEE BigData, Sketch and Scale: Geo-distributed tSNE and UMAP
with Viska Wei, Nikita Ivkin, Alexander Szalay
Full version here
- BMC Neurology, Longitudinal functional and imaging outcome measures in FKRP limb-girdle muscular dystrophy
with Doris Gay, Yee Leung, Michael A. Jacobs, Shivani Ahlawat, Alex E. Bocchieri, Vishwa S. Parekh, Katherine Summerton, Jennifer Mansour, Genila Bibat, Carl Morris, Shannon Marraffino, Kathryn R. Wagner,
Full version here
- MIDL, Multitask radiological modality invariant landmark localization using deep reinforcement learning
with Vishwa S. Parekh, Alex E. Bocchieri, Michael A. Jacobs
Full version here
- MIDL, Multiparametric Deep Learning Tissue Signatures for Muscular Dystrophy: Preliminary Results
with Alex E. Bocchieri, Vishwa S. Parekh, Kathryn R. Wagner, Shivani Ahlawat, Doris G. Leung, Michael A. Jacobs
Full version here