Nanopore-based DNA Sequencing Development

  • Developed key technologies and provided key guidance to enable accomplishment of a product goal (minimum 10X multiplier to sequencing yield via improvements to hardware lifespan) a year ahead of schedule using a combination of system engineering and failure analysis requiring interdisciplinary knowledge and collaboration in chemistry, physics, molecular biology, engineering, and experimental design. In addition, the increased throughput early in development will potentially save the company millions of dollars in R&D cost over time
  • Developed a 95% accurate, robust, scalable, and adjustable signal processing algorithm that identifies/quantifies a key signal pattern using a range of statisical methods. Variant included an ensemble algorithm with a machine learning random forest classifier trained with artificially generated training data with a sliding window application. Used numpy, scipy, pandas, and scikit-learn
  • Determined areas and methodologies to improve the reliability and consistency of the system through identifying necessary improvements in existing filtering algorithms, identifying necessity of new filtering algorithms, various improvements to our fluidic system, and laboratory protocols
  • Failure analyzed other new hardware solutions, in particular automated reagent handling hardware, resulting in additional valuable knowledge of our system or suggested experiments leading to significant progress
  • Co-Inventor in two patents in progress