Prakhar Dutta awarded for best presentation at San Diego conference
Post-graduate student Prakhar Dutta from the Department of physics was recently given an award for best oral presentation at the SPIE-ETAI conference in San Diego, USA. His presentation demonstrates methods for atomic force microscopy (AMF) using a series of pedagogical, animated illustrations, rendering what is usually a quite abstract concept comprehensible and easier to understand.
“I made some animations to illustrate how atomic force microscopy actually works, and to show how the AI-enabled method we have developed is utilized. After the presentation, I got a lot of great feedback from fellow researchers who said the visualizations really helped them get a clear grasp on the subject,” says Prakhar Dutta.
Prakhar Dutta received a cash prize of $300 and a certificate for winning the Oral Presentation Award.
Abstract
Atomic force microscopy (AFM) resolves biological structure and mechanics at high resolution, but produces vast, heterogeneous datasets that are often noisy and very time-consuming to analyze. Although deep learning could automate quality control, segmentation and feature extraction, adoption is limited by scarce ground-truth training data and high technical barriers for experimentalists. Here we present ASAP, an open-source tutorial and pipeline implemented in DeepTrack to provide a reproducible foundation for AI-enabled AFM. We demonstrate the framework with three examples: (i) an unsupervised variational autoencoder for force-curve quality control; (ii) a dual-pathway simulation for DNA, offering both molecular dynamics and rapid, non-MD geometries to generate perfect ground truth for segmentation training; and (iii) a versatile simulation framework that generates synthetic force curves for user-defined surfaces by applying selectable physical models. By consolidating simulation and learning into a single modular ecosystem, this work enables users to build upon our pipeline to optimize AFM workflows for more efficient data acquisition and robust processing.