Accelerated Discovery in Life Sciences
Dr. Laxmi Parida is an IBM Fellow, Master Inventor and heads Computational Genomics at the IBM Thomas J. Watson Research Center. She is also an ISCB Fellow and over the last fifteen years, she has led the IBM Science team on multiple efforts, across IBM global labs, that include Cacao Consortium (with MARS, USDA), Genographic Project with National Geographic, Sequence the Food Supply Chain Consortium (Bioinformatics) and Watson for Genomics, the personalized cancer medicine system.
Her research areas include population genomics, cancer genomics, plant genomics, bioinformatics, algorithms (including AI) and topological data analysis. She serves on the ACED Scientific Advisory Board, NYU Engineering School Advisory Board, ISCB Board of Directors and Editorial Board of BMC Bioinformatics, Senior Editor Journal of Computational Biology, SIAM Journal of Discrete Mathematics (emeritus), Associate Editor, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
Science is continuing to build on big-data and powerful-compute to enter an exciting era of accelerated discovery with new levels of speed and scale in scientific discovery. But many areas in life sciences grapple with sparse, small incomplete data, among other challenges. I will discuss how focused and innovative abstractions and representations not only help tackle these challenges but also pave the way for resource-efficient scientific discoveries.
