About the PI
Christina Boucher is an Associate Professor at the Department of Computer and Information Science and Engineering at the University of Florida. Her research is on development algorithms and data structures that allow for large scale biological sequence analysis. She incorporates the latest sequencing technologies and biological analyses to her work. Two major biological themes recur in her research: alignment to pan-genomes (usually human) and understanding how microbial species move and evolve.
She is currently a standing member of NIH BDMA study section, and a Board Member of ACM SIGBIO
Her research lab greatly appreciates the funding that we receive from NSF, NIH, and USDA.
Christina Boucher’s Google Scholar.
Invited Speaker for SPIRE 2021
Standing Member of NIH BDMA
Board Member of ACM SIG BIO
BIOKDD 2021 PC
CPM 2021 PC
RECOMB 2021 PC
ISMB 2021 PC
WABI 2021 PC
BMC Bioinformatics AE
IEEE/ACM TCBB AE
- NSF EAGER: Solving the Bait Learning Problem ($180,995; PI: Boucher)
- NSF SCH: Enabling real-time surveillance of antimicrobial resistance ($1,187,778; PI: Boucher)
- NSF IIBR: An Efficient Pangenomics Graph Aligner ($700,361, PI: Boucher)
- NIH R01: Personal and panel references for improved alignment (PI: Langmead)
- NIH RO1: Developing Computational Methods for Surveillance of Antimicrobial Resistant Agents ($2,139,795; PI: Boucher)
- Dr. Boucher started as a Standing Member of NIH BDMA in July 2021.
- Kingshuk Mukherjee successfully defended his PhD thesis.
- Kingshuk’s final paper on clustering optical mapping data is accepted to BIOKDD 2021.
- Pan-genomic Matching Statistics for Targeted Nanopore Sequencing accepted to RECOMB SEQ 2021.
- Data structures based on k-mers for querying large collections of sequencing data sets accepted to Genome Research.
Bahar Alipanahi at PhD Commencement
joining the lab
Multiple postdoctoral fellowships and graduate fellowships in big data analysis, applied algorithm and genomics are available in my lab at the University of Florida in Gainesville, FL. I am looking for students with a diverse background in mathematics and computer science. Candidates should be interested in biology but not necessarily have a formal background.
I am specifically interested in (a) pangenomics and devising and implementing algorithms for indexing a large number of reference genomics, (b) developing methods to find structural variants from optical mapping data, and (c) detecting antimicrobial resistance from shotgun metagenomics data. My lab is balanced between applied and theoretical work: we not only create novel algorithms but we also implement them in a scalable manner.