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Assistant Professor
Department of Agricultural and Biological Engineering
Purdue University


Somali Chaterji (pronounced shoh-MAH-lee CHA-ter-jee) is an Assistant Professor in the Department of Agricultural and Biological Engineering at Purdue University, where she specializes in developing algorithms and statistical models for biological engineering, precision medicine, and digital agriculture.

Dr. Chaterji got her PhD in Biomedical Engineering from Purdue University, winning the Chorafas International Award (2010), College of Engineering Best Dissertation Award (2010), and the Future Faculty Fellowship Award (2009). She did her Post-doctoral Fellowship at the University of Texas at Austin in the Department of Biomedical Engineering, where her work was supported by an American Heart Association award. She also received the Seed-for-Success Award from Purdue University's Excellence in Research Awards program in recognition of her NIH R01 award in 2016.  Dr. Chaterji is a technology commercialization enthusiast and has been consulting for the IC2 Institute at the University of Texas at Austin, since Spring 2014.

Here is a succinct vision statement for my research. [ WWW ] And here is me presenting a summary of the research activities at Cells and Machines Innovatory. [ YouTube ]

From my post-doctoral days, I honed my expertise in designing and annotating high-throughput and diverse genomics experiments, developing and testing nanosensor architectures for cell-based therapeutics, and data integration from diverse omics datasets. In my PhD work, I developed testing frameworks for optimizing the design and development of cardiovascular devices. I have collaborations with both wet-lab experimentalists (UT Austin, U of Washington, UNC Medical School) and computer engineers (Purdue, U of Maryland, U of Chicago).

Somali Chaterji's Extended BioExtended Bio (for talks and other engagements)


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Innovatory for Cells and Neural Machines

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Non-Linear Computation

ICAN is publishing a podcast on Spotify, Non-linear computation - a podcast about domain-inspired machine learning and data engineering, focusing on genomics and IoT areas.