Evolution is a process of change where a mutated genome is selected based on its fitness for survival. What can we say about the history of a genome from its current state? In other words, does a single genome have any evolutionary memory? If yes, can we computationally decode it? This talk will shed light on these questions from computing and information theoretic perspectives.  The insights derived from our theoretical findings, augmented with data driven techniques lead us to applications in future cancer prediction, DNA storage, and evolution of viruses. In addition, we address data analysis challenges we encountered stemming from sampling bias, noisy and limited data. In particular, we developed rigorous approaches to ensure robustness to sampling biases and techniques for using existing data-driven models to synthesize new models when access to data is limited.

 Evolution is a process of change where a mutated genome is selected based on its fitness for survival. What can we say about the history of a genome from its current state? In other words, does a single genome have any evolutionary memory? If yes, can we computationally decode it? This talk will shed light on these questions from computing and information theoretic perspectives.  The insights derived from our theoretical findings, augmented with data driven techniques lead us to applications in future cancer prediction, DNA storage, and evolution of viruses. In addition, we address data analysis challenges we encountered stemming from sampling bias, noisy and limited data. In particular, we developed rigorous approaches to ensure robustness to sampling biases and techniques for using existing data-driven models to synthesize new models when access to data is limited.