Swapan Chakrabarty 

Position: Postdoctoral Research Associate 

Education History

• 2024 – Ph.D. Forest Molecular Genetics and Biotechnology, Michigan Technological University, USA
• 2021 – Ph.D. Agricultural Entomology and Pest Control, Chinese Academy of Agricultural Sciences (CAAS), China
• 2024 – M.S. in Data Science, Michigan Technological University, USA
• 2018 – M.S. Genetics and Plant Breeding, Gazipur Agricultural University (Formerly BSMRAU), Bangladesh
• 2015 – B.Sc. Agriculture, Sylhet Agricultural University, Bangladesh

Biography

Dr. Chakrabarty’s expertise encompasses plant breeding, molecular genetics, genomics and bioinformatics, data science and machine learning. His research interest focused on advancing the crop improvement systems for higher yield, quality, and resilience to stresses through deciphering the molecular mechanism and understanding systems biology of how plants respond during biotic and abiotic stresses, and unlocking the inherent potential of plant in the fight against rapid environmental change. Dr. Chakrabarty enjoys teaching and mentoring of graduate and undergraduate students, and serving the department and university for improving student’s experience. He had several leadership positions at Michigan Tech, including Research Chair and Department Representative of the Graduate Student Government, and the Founder President of Bioinformatics and Computational Biology Club at Michigan Tech. 

Current Project:  

Understanding the role of root system architectural traits in nutrition acquisition and carbon sequestration in soybean 

Plant root is the primary interface between plant and soil environment, playing essential role in nutrient acquisition. The project involves unravelling the intricate genetic components that govern the root system architecture (RSA), and useful for optimizing nutrient use efficiency. In this project, hydroponics system is used for screening of soybean accessions based on physiological and RSA traits. Genomic prediction and machine learning or deep learning-based selection model will be developed to identify key root system architecture trait for nutrient acquisition and carbon sequestration. The identified genotype will be used in breeding program to develop cultivar with better root traits and nutrient use efficiency.