Ishini Sangarasekara

Graduate Research Assistant
Ph.D. in Biosystems Engineering
Department of Agricultural and Biological Engineering
Mississippi State University

Lab #: 325, Agricultural and Biological Engineering, MSU
Email: ius5@msstate.edu 

Overview

Ishini Sangarasekara is a graduate student from Sri Lanka with a strong interest in machine learning, sensing technologies, and sustainable agriculture. She completed her bachelor’s degree in Computer Engineering at the University of Peradeniya, where she built a solid foundation in machine learning, embedded systems and software engineering. Her undergraduate research and project experience focused on applying intelligent systems to real-world challenges in agriculture and healthcare. After graduation, Ishini worked as a Software Engineer, where she developed point-of-sale and sales management systems while gaining experience in software development and quality assurance. These experiences strengthened her expertise in building reliable, data-driven software systems and reinforced her interest in precision agriculture and intelligent sensing technologies. Ishini joined the Advanced Soil and Plant Sensing (APSS) Laboratory at Mississippi State University to pursue her Ph.D. studies under the guidance of Dr. Nuwan Wijewardane. Her research mainly focuses on spectroscopy based sensor development for precision agriculture.

Education

  • Ph.D. in Biosystems Engineering, Department of Agricultural and Biological Engineering, Mississippi State University, Mississippi State, MS, USA, in progress
  • B.Sc. (hons) in Engineering specialized in Computer Engineering, University of Peradeniya, Sri Lanka

Experience Record

  • May 2026 - Present, Graduate Research Assistant, Agricultural and Biological
    Engineering, Mississippi State University, Mississippi State, MS, USA
  • January 2024 - January 2025, Software Engineer, Enactor Ltd, Sri Lanka

Publications

  • Amarasinghe, A., Sangarasekara, I., Silva, N.D. et al. Advancing food sustainability: a case study on improving rice yield prediction in Sri Lanka using weather-based,
    feature-engineered machine learning models. Discov Appl Sci 6, 603 (2024). https://doi.org/10.1007/s42452-024-06300-7