Biography
Veer is pursuing an Engineering DPhil with the Computational Health Informatics (CHI) Lab, where he is supervised by Professor David Clifton.
Veer is passionate about AI-driven innovation in healthcare. He previously completed his BS in Computer Science at Yale University, where he worked with the Cardiovascular Data Science Lab to develop AI-enabled technologies to make the detection of cardiovascular disease more accessible in resource-limited settings across the world. He was awarded a Rhodes Scholarship.
Awards and Prizes
- Rhodes Scholarship
- American Heart Association (AHA) Elizabeth Barrett-Connor Research Award in Epidemiology and Prevention for Investigators in Training, Winner
- Quality of Care Outcomes and Research (QCOR) Early Career Investigator Award, Finalist
Research Interests
Veer's interests include utilizing AI for the analysis of multiple modalities of clinical data from the EHR.
Research Groups
Related Academics
Publications
An Ensemble Deep Learning Algorithm for Structural Heart Disease Screening Using Electrocardiographic Images: PRESENT SHD.
Dhingra LS, Aminorroaya A, Sangha V, Pedroso AF, Shankar SV et al. (2024)
BibTeX
@misc{anensembledeepl-2024/11,
title={An Ensemble Deep Learning Algorithm for Structural Heart Disease Screening Using Electrocardiographic Images: PRESENT SHD.},
author={Dhingra LS, Aminorroaya A, Sangha V, Pedroso AF, Shankar SV et al.},
year = "2024"
}
Artificial Intelligence Applications for Electrocardiography to Define New Digital Biomarkers of Cardiovascular Risk.
Sangha V & Khera R (2024), Circulation. Cardiovascular quality and outcomes, e011483
BibTeX
@article{artificialintel-2024/11,
title={Artificial Intelligence Applications for Electrocardiography to Define New Digital Biomarkers of Cardiovascular Risk.},
author={Sangha V & Khera R},
journal={Circulation. Cardiovascular quality and outcomes},
pages={e011483},
year = "2024"
}
Artificial Intelligence-Enhanced Risk Stratification of Cancer Therapeutics-Related Cardiac Dysfunction Using Electrocardiographic Images.
Oikonomou EK, Sangha V, Dhingra LS, Aminorroaya A, Coppi A et al. (2024), Circulation. Cardiovascular quality and outcomes
BibTeX
@article{artificialintel-2024/9,
title={Artificial Intelligence-Enhanced Risk Stratification of Cancer Therapeutics-Related Cardiac Dysfunction Using Electrocardiographic Images.},
author={Oikonomou EK, Sangha V, Dhingra LS, Aminorroaya A, Coppi A et al.},
journal={Circulation. Cardiovascular quality and outcomes},
year = "2024"
}
Biometric contrastive learning for data-efficient deep learning from electrocardiographic images.
Sangha V, Khunte A, Holste G, Mortazavi BJ, Wang Z et al. (2024), Journal of the American Medical Informatics Association : JAMIA, 31(4), 855-865
BibTeX
@article{biometriccontra-2024/4,
title={Biometric contrastive learning for data-efficient deep learning from electrocardiographic images.},
author={Sangha V, Khunte A, Holste G, Mortazavi BJ, Wang Z et al.},
journal={Journal of the American Medical Informatics Association : JAMIA},
volume={31},
pages={855-865},
year = "2024"
}
A Multicenter Evaluation of the Impact of Therapies on Deep Learning-based Electrocardiographic Hypertrophic Cardiomyopathy Markers.
Dhingra LS, Sangha V, Aminorroaya A, Bryde R, Gaballa A et al. (2024)
BibTeX
@misc{amulticentereva-2024/3,
title={A Multicenter Evaluation of the Impact of Therapies on Deep Learning-based Electrocardiographic Hypertrophic Cardiomyopathy Markers.},
author={Dhingra LS, Sangha V, Aminorroaya A, Bryde R, Gaballa A et al.},
year = "2024"
}
Augmenting reality in echocardiography.
Sangha V (2024), Heart (British Cardiac Society), 110(6), 387-388
BibTeX
@article{augmentingreali-2024/2,
title={Augmenting reality in echocardiography.},
author={Sangha V},
journal={Heart (British Cardiac Society)},
volume={110},
pages={387-388},
year = "2024"
}
Biometric contrastive learning for data-efficient deep learning from electrocardiographic images
Sangha V, Khunte A, Holste G, Mortazavi BJ, Wang Z et al. (2024), medRxiv, 31(4), 855-865
BibTeX
@inproceedings{biometriccontra-2024/1,
title={Biometric contrastive learning for data-efficient deep learning from electrocardiographic images},
author={Sangha V, Khunte A, Holste G, Mortazavi BJ, Wang Z et al.},
pages={855-865},
year = "2024"
}
Automated Diagnostic Reports from Images of Electrocardiograms at the Point-of-Care
Khunte A, Sangha V, Oikonomou E, Dhingra LS, Aminorroaya A et al. (2024)
BibTeX
@misc{automateddiagno-2024/,
title={Automated Diagnostic Reports from Images of Electrocardiograms at the Point-of-Care},
author={Khunte A, Sangha V, Oikonomou E, Dhingra LS, Aminorroaya A et al.},
year = "2024"
}
Scalable Risk Stratification for Heart Failure Using Artificial Intelligence applied to 12-lead Electrocardiographic Images: A Multinational Study
Dhingra L, Aminorroaya A, Sangha V, Camargos AP, Asselbergs F et al. (2024)
BibTeX
@misc{scalableriskstr-2024/,
title={Scalable Risk Stratification for Heart Failure Using Artificial Intelligence applied to 12-lead Electrocardiographic Images: A Multinational Study},
author={Dhingra L, Aminorroaya A, Sangha V, Camargos AP, Asselbergs F et al.},
year = "2024"
}
Artificial Intelligence Applied to Electrocardiographic Images for Scalable Screening of Transthyretin Amyloid Cardiomyopathy
Sangha V, Oikonomou E & Khera R (2024)
BibTeX
@misc{artificialintel-2024/,
title={Artificial Intelligence Applied to Electrocardiographic Images for Scalable Screening of Transthyretin Amyloid Cardiomyopathy},
author={Sangha V, Oikonomou E & Khera R},
year = "2024"
}
