activity
20162026
most citedInterpretable and Explainable Machine Learning for Materials Science and Chemistry

420 citations · 434 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CR2026

Media Integrity and Authentication: Status, Directions, and Futures

Jessica Young, Sam Vaughan, Andrew Jenks +9

We provide background on emerging challenges and future directions with media integrity and authentication methods, focusing on distinguishing AI-generated media from authentic con…

cs.CY2025★ 1 cited

Measuring AI Diffusion: A Population-Normalized Metric for Tracking Global AI Usage

Amit Misra, Jane Wang, Scott McCullers +2

Measuring global AI diffusion remains challenging due to a lack of population-normalized, cross-country usage data. We introduce AI User Share, a novel indicator that estimates the…

cs.CL2025

AI Diffusion in Low Resource Language Countries

Amit Misra, Syed Waqas Zamir, Wassim Hamidouche +2

Artificial intelligence (AI) is diffusing globally at unprecedented speed, but adoption remains uneven. Frontier Large Language Models (LLMs) are known to perform poorly on low-res…

cs.LG2025

AI-Driven Detection and Analysis of Handwriting on Seized Ivory: A Tool to Uncover Criminal Networks in the Illicit Wildlife Trade

Will Fein, Ryan J. Horwitz, John E. Brown +5

The transnational ivory trade continues to drive the decline of elephant populations across Africa, and trafficking networks remain difficult to disrupt. Tusks seized by law enforc…

cs.CV2022★ 2 cited

BankNote-Net: Open dataset for assistive universal currency recognition

Felipe Oviedo, Srinivas Vinnakota, Eugene Seleznev +3

Millions of people around the world have low or no vision. Assistive software applications have been developed for a variety of day-to-day tasks, including optical character recogn…

cond-mat.mtrl-sci2021★ 420 cited

Interpretable and Explainable Machine Learning for Materials Science and Chemistry

Felipe Oviedo, Juan Lavista Ferres, Tonio Buonassisi +1

While the uptake of data-driven approaches for materials science and chemistry is at an exciting, early stage, to realise the true potential of machine learning models for successf…