Demonstrated sensitivity of two ALK-mutated lung cancer cell lines to two ALK inhibiting drugs.
Developed a comprehensive machine learning approach to identify mutations under natural selection.
Used hierarchical cluster analysis on lung cancer datasets with the goal of minimizing noise or over-fitting.
Optimized a technology called ChIP-seq that is used for mapping protein distribution across the genome.
Optimized RNA library preparation using a microfluidic chip.
Analyzed the citrullination pattern within the genome of human neutrophils to understand the role of histone citrullination in NETosis.
Investigated how different combinations and concentrations of drugs used to treat late-stage ovarian cancer affected the viability of cancer cells.
Treated prostate cancer cell lines with novel drugs to confirm predictions made by the Broad’s connectivity map.
Compiled and analyzed the whole-genome sequencing data for over 1,300 worldwide strains of Myocobacterium tuberculosis and related species.
Used microfluidics to identify methods of controlling harmful yeast infections in human hosts.
Implemented a simpler algorithm for predicting enhancer sequences from histone modification ChIP-Seq tracks.
Compared several datasets to find patterns and relationships among oncogenes.
Analyzed protein interactions regulating VGCCs to understand the role of specific genetic variants in neuropsychiatric disorders.
Examined a set of machine learning methods to classify potential genetic variations as true variants or false positives.
Broad Summer Scholars Program