Arpita Joshi
Papers
1
Total Citations
3
H-Index
1
About
Arpita Joshi is a researcher whose work centers on data instance reduction and dimensionality reduction techniques, with applications spanning structural bioinformatics, machine learning, robotics, and artificial intelligence. Her most cited paper, "A Novel Data Instance Reduction Technique using Linear Feature Reduction" (2020, 3 citations), addresses a critical challenge in modern research: the need to efficiently represent structurally significant data while minimizing computational overhead. Joshi’s key contribution lies in developing methods that reduce data instances without sacrificing essential information, enabling more effective analysis in high-dimensional spaces. Her work has implications for improving the efficiency of machine learning algorithms and robotic systems, where data volume often poses a bottleneck. Though early in her career, Joshi’s focus on balancing data reduction with structural integrity positions her as a promising voice in the field, with potential for significant impact as her techniques gain broader adoption in AI and bioinformatics.
Research Focus
Key Achievements
Top Papers
- 1A Novel Data Instance Reduction Technique using Linear Feature Reduction3 citations · 2020