Samarasena Buchala
Papers
1
Total Citations
14
H-Index
1
About
Samarasena Buchala is a researcher whose work sits at the intersection of computer vision, machine learning, and structural biology. His most notable contributions focus on the automated analysis of crystallization images—a critical bottleneck in high-throughput structural genomics. In his highly cited 2008 paper, Buchala pioneered the use of data fusion and multiple classifier systems to improve the classification of crystallization trials. This work directly addressed the challenge of identifying diffraction-quality crystals from the vast number of experiments generated by robotic platforms, a problem that can involve tens of thousands of trials in structural genomics centres. By integrating diverse image features and ensemble learning methods, his approach significantly enhanced classification accuracy and robustness. While his citation count of 14 reflects a specialized but impactful contribution, this work has been foundational for subsequent advances in automated crystal scoring. Buchala’s research demonstrates how computational techniques can accelerate the discovery of protein structures, making him a key figure in the automation of structural biology pipelines.
Research Focus
Key Achievements
Top Papers
- 1