index

content/authors/alexfrangi/_index.md

@@ -34,4 +34,22 @@ I am a Professor of Computational Medicine in the Department of Computer Science
 
 I am on a mission to revolutionise the development of medical products and the decision-making process surrounding the R&D and regulation of medical devices, with a specific focus on cardiovascular, orthopaedic, and medical imaging devices. I am driven to enhance the scientific evidence used in device design and regulation, envisioning a future where our work significantly improves patient outcomes and healthcare efficiency. I also lead the InSilicoUK Network.
 
-To achieve these ambitious goals, I believe in the power of interdisciplinary collaboration. Our landmark papers, listed below, are a testament to this. They reflect my interests in statistical models, deep representation/manifold learning, synthetic data and digital twins/chimeras, uncertainty quantification and design optimisation using deep learning, and the incorporation of knowledge priors (eg physics, physiology, etc) into learning systems. I look forward to the opportunity to work together on these exciting projects.  
+To achieve these ambitious goals, I believe in the power of interdisciplinary collaboration. Our landmark papers, listed below, are a testament to this. They reflect my interests in statistical models, deep representation/manifold learning, synthetic data and digital twins/chimeras, uncertainty quantification and design optimisation using deep learning, and the incorporation of knowledge priors (eg physics, physiology, etc) into learning systems. I look forward to the opportunity to work together on these exciting projects.  
+
+**Selected Publications**
+
+- Bonazzola R, Ferrante E, Ravikumar N, Xia Y, Keavney B, Plein S, Syeda-Mahmood T, Frangi AF. Unsupervised ensemble-based phenotyping enhances discoverability of genes related to left-ventricular morphology. Nat Mach Intell. 2024;6(3):291-306.
+
+- MacRaild M, Sarrami-Foroushani A, Lassila T, Frangi AF. Accelerated simulation methodologies for computational vascular flow modelling. J R Soc Interface. 2024 Feb;21(211):20230565.
+
+- Xia Y, Ravikumar N, Lassila T, Frangi AF. Virtual high-resolution MR angiography from non-angiographic multi-contrast MRIs: synthetic vascular model populations for in-silico trials. Med Image Anal. 2023 Jul;87:102814.
+
+- Diaz-Pinto A, Ravikumar N, Attar R, Suinesiaputra A, Zhao Y, Levelt E, Dall'Armellina E, Lorenzi M, Chen Q, Keenan TD, Agrón E, Chew EY, Lu Z, Gale CP, Gale RP, Plein S, Frangi AF. Predicting myocardial infarction through retinal scans and minimal personal information. Nat Mach Intell. 2022;4:55-61. doi: 10.1038/s42256-021-00427-7.
+
+
+- Zhang J, Zhao Y, Shone F, Li Z, Frangi AF, Xie SQ, Zhang ZQ. Physics-informed Deep Learning for Musculoskeletal Modelling: Predicting Muscle Forces and Joint Kinematics from Surface EMG. IEEE Trans Neural Syst Rehabil Eng. 2022 (in press). doi: 10.1109/TNSRE.2022.3226860.
+
+- Xia Y, Chen X, Ravikumar N, Kelly C, Attar R, Aung N, Neubauer S, Petersen SE, Frangi AF. Automatic 3D+t four-chamber CMR quantification of the UK biobank: integrating imaging and non-imaging data priors at scale. Med Image Anal. 2022 Aug;80:102498.
+
+- Sarrami-Foroushani A, Lassila T, MacRaild M, Asquith J, Roes KCB, Byrne JV, Frangi AF. In-silico trial of intracranial flow diverters replicates and expands insights from conventional clinical trials. Nat Commun. 2021 Jun 23;12(1):3861.
+