{"data":{"jobs":{"edges":[{"node":{"frontmatter":{"title":"Performance Research Intern","company":"D-Wave Systems","location":"Burnaby, BC","range":"September - December 2023","url":"https://www.dwavesys.com/"},"html":"\n- Designed, refined, and maintained the core Python codebase for D-Wave Ocean (open-source software development toolkit), optimizing for scalability and performance in quantum computing applications.
\n- Conducted benchmarking analysis to optimize graph embedding algorithms on D-Wave processor hardware, collaborating with the APT (Applications, Performance, and Tools) team to enhance overall system efficiency.
\n- Built a codebase to replicate recent quantum supremacy results, tested and debugged performance bottlenecks on emulators, and contributed to code reviews for continuous improvement.
\n
"}},{"node":{"frontmatter":{"title":"Summer Research Intern","company":"SFU MAGPIE Lab","location":"Burnaby, BC","range":"May - August 2023","url":"https://www.sfu.ca/magpie.html"},"html":"\n- Applied pairwise logistic regression to predict tuberculosis (TB) genetic cluster memberships using epidemiological data from 11,000 patients. Model published in Microbial Genomics , 2023
\n- Performed cluster analysis using SNP cutoff thresholds, untimed maximum likelihood phylogenetic tree construction, and spatial data; applied Principal Component Analysis (PCA) for dimensionality reduction.
\n- Utilized R for data cleaning, visualization, and analysis, and applied supervised learning models to benchmark quantitative predictions.
\n- Presented research findings monthly to the MAGPIE research group and delivered a seminar to SFU Math USRA students.
\n
"}}]}}}