Ayana Ghosh

Papers

Total Citations

156

H-Index

About

Ayana Ghosh is a computational and experimental researcher at the forefront of integrating machine learning and artificial intelligence into materials characterization, with a particular focus on electron and scanning probe microscopy. Her work addresses one of the most pressing challenges in modern physics research: transforming microscopy from a labor-intensive, human-guided process into an automated, autonomous scientific workflow capable of accelerating discovery. Her most influential contribution, "Automated and Autonomous Experiments in Electron and Scanning Probe Microscopy" (2021), has garnered 134 citations, reflecting the field's enthusiasm for her vision of AI-driven experimentation. In this work and related studies, Ghosh demonstrates how ML methods — including Bayesian optimization and reinforcement learning — can be deployed to guide real-time experimental decision-making, reducing human bottlenecks while improving data quality and throughput. Her 2023 paper, "Probe Microscopy is All You Need," further advocates for microscopy as an ideal testbed for active learning algorithms, bridging fundamental AI research with tangible physical measurement contexts. Ghosh's research is particularly valuable for students entering the field, as it illustrates how domain-specific scientific environments can serve as rich proving grounds for next-generation autonomous systems, ultimately reshaping how materials are explored at the nanoscale.