Search Results for author: Ashwin Agrawal

Found 7 papers, 3 papers with code

LeanAI: A method for AEC practitioners to effectively plan AI implementations

no code implementations29 Jun 2023 Ashwin Agrawal, Vishal Singh, Martin Fischer

This lack of understanding results in the disconnect between AI planning and implementation because the planning is based on a vision of what AI should solve without considering if it can or will solve it.

Digital Twin: Where do humans fit in?

no code implementations8 Jan 2023 Ashwin Agrawal, Robert Thiel, Pooja Jain, Vishal Singh, Martin Fischer

This piecemeal implementation of DTs often leaves practitioners wondering what roles (or functions) to allocate to DTs in a work system, and how might it impact humans.

Vitruvio: 3D Building Meshes via Single Perspective Sketches

1 code implementation24 Oct 2022 Alberto Tono, Heyaojing Huang, Ashwin Agrawal, Martin Fischer

If previous state-of-the-art (SOTA) data-driven methods for single view reconstruction (SVR) showed outstanding results in the reconstruction process from a single image or sketch, they lacked specific applications, analysis, and experiments in the AEC.

A new perspective on Digital Twins: Imparting intelligence and agency to entities

no code implementations11 Oct 2022 Ashwin Agrawal, Vishal Singh, Martin Fischer

Despite the Digital Twin (DT) concept being in the industry for a long time, it remains ambiguous, unable to differentiate itself from information models, general computing, and simulation technologies.

Digital Twin: From Concept to Practice

no code implementations14 Jan 2022 Ashwin Agrawal, Martin Fischer, Vishal Singh

Recent technological developments and advances in Artificial Intelligence (AI) have enabled sophisticated capabilities to be a part of Digital Twin (DT), virtually making it possible to introduce automation into all aspects of work processes.

RadGraph: Extracting Clinical Entities and Relations from Radiology Reports

1 code implementation28 Jun 2021 Saahil Jain, Ashwin Agrawal, Adriel Saporta, Steven QH Truong, Du Nguyen Duong, Tan Bui, Pierre Chambon, Yuhao Zhang, Matthew P. Lungren, Andrew Y. Ng, Curtis P. Langlotz, Pranav Rajpurkar

We release a development dataset, which contains board-certified radiologist annotations for 500 radiology reports from the MIMIC-CXR dataset (14, 579 entities and 10, 889 relations), and a test dataset, which contains two independent sets of board-certified radiologist annotations for 100 radiology reports split equally across the MIMIC-CXR and CheXpert datasets.

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