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Daniel Lobo
I am a postdoc in the Biology department and work together with Prof. Michael Levin to create novel artificial intelligence methods for the automated discovery of models of development and regeneration. A major challenge in developmental and regenerative biology is the identification of models that specify the steps sufficient for creating specific complex patterns and shapes. Despite the great number of manipulative and molecular experiments described in the literature, no comprehensive, constructive model exists that explains the remarkable ability of many organisms to restore anatomical polarity and organ morphology after amputation. It is now clear that computational tools must be developed to mine this ever-increasing set of functional data to help derive predictive, mechanistic models that can explain regulation of shape and pattern. We use the Tufts Computer Cluster for running our heuristic searches for the discovery of comprehensive models that can explain the great number of poorly-understood regenerative experiments. Our method requires the simulation of millions of tissue-level experiments, comprising the behavior of thousands of cells and their secreted signaling molecules diffusing according to intensive differential equations. Using the compute cluster, we can massively parallelize the simulation of these experiments and the search for models of regeneration. Indeed, the cluster is an indispensable tool for us to apply cutting-edge artificial intelligence to biological science.
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Eric Kernfeld
My work with Prof. Shuchin Aeron (ECE) and Prof. Misha Kilmer (Math) centers around algebraic analysis of image and video data. Even a single video or a small collection of images can require an uncomfortable amount of time to process on a laptop, especially when it comes time to compare algorithms under different regimes. The high-performance computing tools(such as Matlab Distributed Computing Toolbox) at Tufts allowed us to run tests in a manageable time frame and proceed with our projects.
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