I am Anurag Limdi, a Data Scientist at Apriori Bio, where I merge ML modeling and analyses of deep mutational scanning experiments of proteins to understanding genotype to phenotype mapping and inform vaccine design for infectious diseases.
Previously I was a PhD Candidate in Michael Baym's lab in the Department of Biomedical Informatics at the Harvard Medical School where I explored how biological systems function and evolve, through a combination of high-throughput experiments, theory and computational approaches.
In my thesis research, I investigated how fitness landscapes change over thousands of generations of bacterial evolution by generating transposon insertion libraries (with >100,000 mutations) in ancestral and evolved states, and analyzing statistical patterns of changes in fitness effects and gene essentiality (read our preprint here). In the process, I developed an approach for detecting and correcting PCR amplification bias in transposon sequencing by addition of unique molecular identifiers (github repo and paper forthcoming), and wrote a simulations/review paper on tradeoffs in the design of high-throughput sequencing based fitness assays (read our preprint here).
You can find all my papers and preprints here!