Parth Kothiya

Background

About Parth Kothiya

I am a bio-health informatician, research fellow, and Ph.D. bio-health informatics student. I am recognized for my data-centric scientific creativity and innovative continuous improvement mindset, which enable me to develop novel analysis methods, build integrated data analysis pipelines, and enhance existing methodologies. I have over a decade of research experience, including success supporting, streamlining, and optimizing study initiatives for leading research organizations. What makes me effective is my diversified yet blended expertise in bio-health informatics, biotechnology statistics, computational laboratory, and data collection & modeling. I am highly skilled in big data analysis, predictive modeling, bio-health data management, and AI & machine learning. I also have a broad-based data analysis skillset that includes biological, clinical, and drug-drug interaction data and is complemented by my fluency in R/Python/R Shiny. I believe that collaboration and transparency across interdisciplinary teams are critical to successful problem solving, data integrity, and scientific rigor, so I frequently serve as the main point of contact between multiple stakeholders and teams to facilitate communication and partnership. My motivation, effectiveness, and data-led innovation are demonstrated in every aspect of my work. Experience performing drug-drug interaction modeling for up to three drugs and building three-dimensional visualization tools. Created integrated multi-data stream, multi-modal pregnancy database with MySQL, connecting EHR data to genomics and metabolomics. Developed visualization dashboard with data-specific descriptive statistics and illustrations and ability to trace data at biospecimen level. Automated and standardized process to extract individual data from PDF reports. Streamlined research operations by constructing Toxcast analytical pipeline for thousands of chemicals across hundreds of high-throughput toxicity testing assays. Honored with Scientific and Technology Achievement Award Level & Milestone Award.