Associate Research Scientist
Affiliation: NYU Abu Dhabi
Education: BSc Information Systems, Technion Israel Institute of Technology; MSc Computer Science, New York University
Research Areas: high throughput sequence analysis and classification. VDJ antibody sequence classification. Machine Learning approaches to object identification in microscopy images. Advanced programming languages and code optimization.
Lior Galanti is an experienced software engineer developing bioinformatics algorithms and software solutions for processing and analyzing deep sequencing at the Gunsalus lab at NYU. His tools often emphasize speed optimization, reusable code, and use of modern programming languages.
His research interest is the efficient and accurate classification of high throughput sequence reads based on noisy synthetic barcode sequences, reconstruction of V(D)J somatic recombination repertoires events from sequence reads, and application of Machine Learning to object identification in microscopy images. During his work at the Durbin lab at the Wellcome Trust Sanger Institute, he developed a tool for investigating coalescent and recombination events in ancestral recombination graphs that can visualize the graph structure and plot pairwise correlation based on the Felsenstein tree metric.
Lior is currently developing Pheniqs, a generic high throughput genetic barcode classifier that caters to a wide variety of experimental designs. He is currently preparing online manuals and code documentation to supplement the upcoming publication of the tool.