Arkopal Choudhury
About Arkopal Choudhury
Arkopal Choudhury is a Senior Biostatistician at Nektar Therapeutics, specializing in missing data imputation and machine learning applications in biomedical research. He holds a PhD in Biostatistics from the University of North Carolina at Chapel Hill and has developed advanced algorithms during his academic career.
Work at Nektar Therapeutics
Currently, Arkopal Choudhury serves as a Senior Biostatistician at Nektar Therapeutics, a position he has held since 2020. His role involves applying advanced statistical methods to support the development of therapeutic solutions. Nektar Therapeutics is known for its focus on innovative drug development, and Choudhury's expertise in biostatistics contributes to the company's research initiatives.
Education and Expertise
Arkopal Choudhury holds a Doctor of Philosophy (PhD) in Biostatistics from the University of North Carolina at Chapel Hill, where he studied from 2014 to 2020. His doctoral research focused on missing data imputation techniques for machine learning problems and natural language processing in Electronic Health Records (EHRs). He also earned a Master's degree in Statistics from the Indian Statistical Institute, completing his studies from 2012 to 2014, and a Bachelor of Statistics (B. Stat. (Hons.)) from the same institution from 2009 to 2012.
Background
Before joining Nektar Therapeutics, Arkopal Choudhury gained valuable experience in various academic roles. He worked as a Graduate Research Assistant at the University of North Carolina at Chapel Hill from 2014 to 2020, where he collaborated with different departments as a biostatistics consultant. He also served as a Teaching Assistant in 2016 and as a Grader in 2019. Earlier, he completed a two-month internship at Columbia University in 2013, where he developed a faster algorithm for computing the least squares estimator of multivariate convex regression.
Research Interests
Arkopal Choudhury's research interests encompass several areas, including missing data imputation, machine learning algorithms, text mining, and statistical applications in biomedical fields. His work aims to enhance the application of statistical methods in health data analysis, particularly in improving the accuracy and efficiency of data handling in medical research.