Jeremy Rotman
Software Development Engineer at Amazon
Los Angeles Metropolitan Area United States
Summary
👤
Senior🎓
Top SchoolJeremy Rotman is a software development engineer in the Los Angeles area with 11 years of experience and a seven-year focus on building statistical models and data-driven methods for biological and genomic problems. Currently at Amazon, he brings production-grade engineering skills honed through roles as a data software engineer and researcher where he built pipelines and visualizations for large, noisy biological datasets on HPC clusters. His background blends Bayesian methods, microbial community classification, and benchmarking of genomic storage/query approaches with practical software construction and reproducible lab tooling. Jeremy has taught algorithms in bioinformatics and mentored students on ML topics and engineering fundamentals, reflecting strong communication and instructional instincts. He combines academic rigor from a BS and MS in Computer Science at UCLA with hands-on experience shipping data-intensive systems in industry. A detail that often surprises collaborators: he has deep experience turning complex read-alignment and causal-variant research into usable pipelines and publication-grade visualizations.
11 years of coding experience
6 years of employment as a software developer
High School, High School at Deer Valley High SchoolBachelor of Science (BS), Computer Science, Bachelor of Science (BS), Computer Science at University of California, Los Angeles
English
Github Skills (19)
decomposition9
arxiv9
lda8
evaluation8
genes7
survey7
immunology6
bioinformatics6
hla6
needle5
assembly4
genomics4
haystack3
databases2
traits2
Programming languages (3)
Github contributions (5)
Mangul-Lab-USC/ImReP_publication_old
Feb 2020 - Jul 2020
Contributions:40 commits, 38 pushes, 1 branch in 5 months
Mangul-Lab-USC/review-technology-dictates-algorithms
Jan 2020 - Apr 2020
A systematic survey of algorithmic foundations and methodologies across 107 alignment methods (1988-2021), for both short and long reads. We provide a rigorous experimental evaluation of 11 read aligners to demonstrate the effect of these underlying algorithms on speed and efficiency of read alignment. Described by Alser et al. at https://arxiv.org/abs/2003.00110.
Contributions:21 commits, 20 pushes, 1 branch in 2 months
decompositionarxivspeedabsfoundations
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