Dr Gonzalo Daniel Maso Talou
Eng, MSc, PhD in Computational Modelling
Gonzalo Daniel Maso Talou is a post-doctoral research fellow in Auckland Bioengineering Institute and is currently part of the Virtual Brain Group, the Laboratory for Animate Technologies and the Biomechanics for Breast Health Group. He completed his undergraduate studies in Software Engineering at the National University of the Central Buenos Aires in 2010 and his MSc in Scientific Computing and PhD in Computational Modelling at the National Laboratory for Scientific Computing in 2013 and 2017, respectively. In 2015, he received the Prêmio Bolsa Nota 10 scholarship from FAPERJ foundation in recognition to the highest GPA and academic production during the PhD program.
In 2019, he started the Advanced AI-driven Image Processing group with Dr Soroush Safaei as part of the Virtual Brain group activities, focusing on the automatic generation of subject-specific neurovascular models and the analysis of neurofluid dynamics.
His current research covers topics of computational modelling, AI-physics models and image processing for different biological systems. Cardiovascular and neurovascular models are involved in the application and main research outputs of his publications.
- Luís Fernando Mendes Cury (2019-2021): A parallel algorithm for the construction of vast vascular networks. (Jointly with Pablo Blanco)
- Cameron Apeldoorn (2020-2021): Latent vasculature in MRI sequences: A generative adversarial approach for recovering vasculature from standard MRI sequences. (Jointly with Soroush Safaei and Mark Sagar)
- Robin Laven (2020-2021): Markerless tracking of highly deformable objects for applications in breast cancer diagnosis and treatment. (Jointly with Thiranja Prasad Babarenda Gamage, Huidong Bai, Martyn Nash and Poul Nielsen)
- Kejia Khoo (2020-present): Personalised anatomical modelling of the female torso. (Jointly with Thiranja Prasad Babarenda Gamage, Poul Nielsen and Martyn Nash)
- Logan Vugler (2021-present): Using deep learning to non-invasively measure haemodynamic parameters in a clinically useful timeframe. (Jointly with Alan Wang, Patrick Schweder, Hugh McHugh)
- Katze Zambo (2021-present): Machine learning techniques for cerebrovascular 4D MRI flow analysis. (Jointly with Vinod Suresh, Catherine Morgan and Soroush Safaei)
- Leo Dang (2021-present): A functional brain atlas for ICP biomarkers discovery. (Jointly with Soroush Safaei and Samantha Holdsworth)
- Zhaohan Xiong (2019-present): Is Artificial Intelligence Intelligent Enough to See Scars in the Heart from Non-Contrast MRIs? (Jointly with Jichao Zhao and Martin Stiles)
- Alireza Farrokhi Nia (2019-present): Using advanced machine learning techniques to recognize emotional state from psycho-physiological data. (Jointly with Mark Billinghurst)
- Jiantao Shen (2020-present): Computational hemodynamic assessment in the brain using MRI and its association with Alzheimer’s disease. (Jointly with Soroush Safaei and Peter Hunter)
- Robyn May (2020-present): In the search of vascular culprits: a computational model of the cardiovascular system for newborns and their developmental prognosis. (Jointly with Soroush Safaei and Frank Bloomfield)
- Harshil Magan (2020-present): Physics-aware integrated networks and their application to brain hemodynamics. (Jointly with Soroush Safaei and Peter Hunter)
- Gurleen Singh (2020-present): Neuromorphic systems for modelling and understanding of multi-modal brain imaging data. (Jointly with Mahyar Osanlouy and Peter Hunter)
- Alice Little (2021-present): Magnetic Resonance Imaging (MRI) of brain motion as a non-invasive diagnostic and assessment tool for abnormal intracranial pressure. (Jointly with Samantha Holdsworth, Sarah-Jane Guild and Miriam Scadeng)
- Cameron Apeldoorn (2021-present): Sprouting life or rooting hypertension? Understanding vascular remodelling contributions to hypertension via computational models. (Jointly with Julian Paton, Soroush Safaei)
- John Pan (2021-present): Automated personalised anatomical modelling of the torso. (Jointly with Thiranja Prasad Babarenda Gamage, Martyn Nash, Poul Nielsen)
2020: Marsden Standard Fund Award, Royal Society of NZ, New Zealand.
2020: Best Postdoctoral Poster, ABI Research Forum, University of Auckland, New Zealand.
2020: Excellence in Research Translational Award 2019, ABI Research Forum, University of Auckland, New Zealand.
2019: Publication of the year award, Australian and New Zealand Society of Biomechanics, Australia/New Zealand.
2015: Prêmio Bolsa Nota 10 scholarship, FAPERJ, Brazil.
Areas of expertise
- Computational modelling
- Artificial Intelligence
- Early Career Research Committee Member (2021-present).
- Physiological Society of New Zealand Member (2021-present).
Selected publications and creative works (Research Outputs)
- Maso Talou, G. D., Babarenda Gamage, T. P., Sagar, M., & Nash, M. P. (2020). Deep Learning Over Reduced Intrinsic Domains for Efficient Mechanics of the Left Ventricle. Frontiers in Physics, 810.3389/fphy.2020.00030
Other University of Auckland co-authors: Thiranja Babarenda Gamage, Martyn Nash
- Babarenda Gamage, T. P., Malcolm, D. T. K., Maso Talou, G., Mîra A, Doyle, A., Nielsen, P. M. F., & Nash, M. P. (2019). An automated computational biomechanics workflow for improving breast cancer diagnosis and treatment. Interface focus, 9 (4)10.1098/rsfs.2019.0034
Other University of Auckland co-authors: Martyn Nash, Thiranja Babarenda Gamage, Anthony Doyle, Poul Nielsen
- Blanco, P. J., Bulant, C. A., Müller LO, Talou, G. D. M., Bezerra, C. G., Lemos, P. A., & Feijóo RA (2018). Comparison of 1D and 3D Models for the Estimation of Fractional Flow Reserve. Scientific reports, 8 (1)10.1038/s41598-018-35344-0
- Maso Talou, G. D., Blanco, P. J., Ares, G. D., Guedes Bezerra, C., Lemos, P. A., & Feijóo RA (2018). Mechanical Characterization of the Vessel Wall by Data Assimilation of Intravascular Ultrasound Studies. Frontiers in physiology, 910.3389/fphys.2018.00292
- Maso Talou, G. D., Blanco, P. J., Larrabide, I., Guedes Bezerra, C., Lemos, P. A., & Feijoo, R. A. (2017). Registration methods for IVUS: Transversal and longitudinal transducer motion compensation. IEEE Transactions on Biomedical Engineering, 64 (4), 890-903. 10.1109/TBME.2016.2581583
- Bulant, C. A., Blanco, P. J., Maso Talou, G. D., Guedes Bezerra, C., Lemos, P. A., & Feijóo RA (2017). A head-to-head comparison between CT- and IVUS-derived coronary blood flow models. Journal of Biomechanics, 51, 65-76. 10.1016/j.jbiomech.2016.11.070