I am driven to tackle complex interdisciplinary challenges at the intersection of biotechnology and data science.
With a strong background in biochemistry and hands-on experience in both academic and industrial laboratories,
I bring a unique combination of skills to the table that can provide insights and tangible value to any team.
While bioinformatics has traditionally focused on genomics and drug development,
I am interested in exploring
untapped territories enabled by recent advancements in
sensing technology, computational power, and artificial intelligence.
Eager to tackle intellectually stimulating problems, I aspire to use my skillset in machine learning
and understanding of various research fields to pioneer and optimize solutions that revolutionize
biological science and its industrial application.
Engineered a Python-based machine intelligence platform for automatic data collection, clustering, visualization, and analysis of research papers and their citation networks ranging from 1,000 to 10,000 papers in total. Aimed to help researchers verify the validity of academic claims.
Developed and implemented a Google Apps Script-based scientific laboratory notebook, improving work processes and collaboration within the lab. Liaised between the data science team and lab scientists managing data flow and contributing to automated data analysis pipelines. Designed protocols for experiments using the Hamilton STAR, a cutting-edge liquid-handling robot. Synthesized, optimized, and validated magnetic nanoparticle-based nucleic acid extraction kits.
Collaborated with software developers to design standardized data entry protocols adhering to Canadian federal guidelines on environmental standards with the goal of simplifying compliance tasks for client companies, ensuring accurate data entry and improving consistency across datasets.
Université de Montréal, Montreal, Canada
24-week part-time intensive coding bootcamp focused on mastering Data Science using Python.
Concordia University, Montreal, Canada