Shamma Nasrin
Papers
1
Total Citations
32
H-Index
1
About
Shamma Nasrin is a rising star in the field of energy-efficient artificial intelligence, with research focused at the intersection of Bayesian deep learning and hardware design for edge computing. Her most cited work, "MC-CIM: Compute-in-Memory With Monte-Carlo Dropouts for Bayesian Edge Intelligence" (2022, 32 citations), introduces a groundbreaking compute-in-memory (CIM) framework that enables robust, low-power Bayesian inference directly on edge devices. This innovation addresses a critical limitation of conventional deep neural networks—their inability to quantify prediction uncertainty—which poses serious risks in high-stakes applications like autonomous systems and medical diagnostics. By integrating Monte Carlo dropout techniques with CIM architectures, Nasrin's work paves the way for trustworthy AI that can operate reliably under real-world constraints. Her contributions are particularly notable for bridging the gap between algorithmic Bayesian methods and practical hardware implementation, offering a path toward safer, more interpretable edge intelligence. As an emerging leader in this domain, Nasrin's research continues to shape how we deploy uncertainty-aware AI in resource-constrained environments, making her a key figure to watch in the evolution of robust, energy-efficient machine learning systems.
Research Focus
Key Achievements
Top Papers
- 1