research
∙
04/05/2023
A dynamic Bayesian optimized active recommender system for curiosity-driven Human-in-the-loop automated experiments
Optimization of experimental materials synthesis and characterization th...
share
research
∙
04/04/2023
Deep Learning for Automated Experimentation in Scanning Transmission Electron Microscopy
Machine learning (ML) has become critical for post-acquisition data anal...
research
∙
03/25/2023
Deep Kernel Methods Learn Better: From Cards to Process Optimization
The ability of deep learning methods to perform classification and regre...
research
∙
02/08/2023
Combining Variational Autoencoders and Physical Bias for Improved Microscopy Data Analysis
Electron and scanning probe microscopy produce vast amounts of data in t...
research
∙
01/06/2023
Discovery of structure-property relations for molecules via hypothesis-driven active learning over the chemical space
Discovery of the molecular candidates for applications in drug targets, ...
research
∙
10/18/2022
Enabling Autonomous Electron Microscopy for Networked Computation and Steering
Advanced electron microscopy workflows require an ecosystem of microscop...
research
∙
10/12/2022
Microscopy is All You Need
We pose that microscopy offers an ideal real-world experimental environm...
research
∙
08/20/2022
MLExchange: A web-based platform enabling exchangeable machine learning workflows
Machine learning (ML) algorithms are showing a growing trend in helping ...
research
∙
06/30/2022
Optimizing Training Trajectories in Variational Autoencoders via Latent Bayesian Optimization Approach
Unsupervised and semi-supervised ML methods such as variational autoenco...
research
∙
05/30/2022
Bayesian Active Learning for Scanning Probe Microscopy: from Gaussian Processes to Hypothesis Learning
Recent progress in machine learning methods, and the emerging availabili...
research
∙
03/18/2022
Active learning in open experimental environments: selecting the right information channel(s) based on predictability in deep kernel learning
Active learning methods are rapidly becoming the integral component of a...
research
∙
06/23/2021
Finding simplicity: unsupervised discovery of features, patterns, and order parameters via shift-invariant variational autoencoders
Recent advances in scanning tunneling and transmission electron microsco...
research
∙
05/24/2021
Semi-supervised learning of images with strong rotational disorder: assembling nanoparticle libraries
The proliferation of optical, electron, and scanning probe microscopies ...
research
∙
05/16/2021
AtomAI: A Deep Learning Framework for Analysis of Image and Spectroscopy Data in (Scanning) Transmission Electron Microscopy and Beyond
AtomAI is an open-source software package bridging instrument-specific P...
research
∙
04/20/2021
Decoding the shift-invariant data: applications for band-excitation scanning probe microscopy
A shift-invariant variational autoencoder (shift-VAE) is developed as an...
research
∙
03/22/2021
Automated and Autonomous Experiment in Electron and Scanning Probe Microscopy
Machine learning and artificial intelligence (ML/AI) are rapidly becomin...
research
∙
01/21/2021
Ensemble learning and iterative training (ELIT) machine learning: applications towards uncertainty quantification and automated experiment in atom-resolved microscopy
Deep learning has emerged as a technique of choice for rapid feature ext...
research
∙
05/04/2020
Off-the-shelf deep learning is not enough: parsimony, Bayes and causality
Deep neural networks ("deep learning") have emerged as a technology of c...
research
∙
08/01/2019
Structure retrieval from 4D-STEM: statistical analysis of potential pitfalls in high-dimensional data
Four-dimensional scanning transmission electron microscopy (4D-STEM) is ...
research
∙
05/13/2018
