Hi ! Welcome to my personal webpage

I am a researcher in computational oncology.

The focus of my investigations is to build personalized mathematical models of the biophysical mechanisms underlying cancer growth and therapeutic response, with which I can make patient-specific forecasts of tumor prognosis using computer simulations. I believe that this predictive approach can dramatically contribute to advance clinical practice in oncology by delivering personalized solutions aiming at optimizing clinical outcomes for each patient.

I also work in modeling the mechanisms of development, treatment, and spread of other pathologies. Additionally, I am interested in constructing robust computational methods to efficiently and accurately solve my models in clinically-relevant times.

Thanks to a Marie Skłodowska-Curie global fellowship, I am currently working at the Center for Computational Oncology at the Oden Institute in The University of Texas at Austin, but I am also a postdoctoral fellow at Computational Mechanics & Advanced Materials Group at the University of Pavia in Italy.

Check out my research below and feel free to contact me if you are interested in discussing my work or any of the topics in this webpage. I am always open to new collaborations, delivering academic and popular science talks, or having a drink to talk science and else.


Patient-specific, imaging-based forecasting of prostate cancer growth

This research aims at integrating standard clinical and imaging data from individual patients into mathematical models to enable the prediction of tumor growth using computer simulations

Personalized prediction of PSA dynamics after external radiotherapy of prostate cancer

Exploring the biophysical mechanisms underlying PSA dynamics after external radiotherapy to define new biomarkers for the early identification of relapse

Optimal control of therapeutic regimens for advanced prostate cancer

This work aims at finding optimal combinations of cytotoxic and antiangiogenic therapies to treat advanced prostatic tumors by combining mathematical analysis and computer simulations

Integrating multiscale data and mechanistic models to predict breast cancer response to neoadjuvant therapies

Personalized prediction of breast cancer response to neoadjuvant therapies by using biophysical models parameterized with patient-specific imaging data and constrained by comprehensive pharmacodynamic experimental data

Data-driven mechanistic models to forecast COVID-19 outbreaks

Constructing mathematical models to understand and predict the dynamics of COVID-19 infectious spread based on longitudinal epidemiological data series


Quantitative in vivo imaging to enable tumor forecasting and treatment optimization

Guillermo Lorenzo, David A Hormuth II, Angela M Jarrett, Ernesto ABF Lima, Shashank Subramanian, George Biros, J Tinsley Oden, Thomas JR Hughes, Thomas E Yankeelov

In: Igor Balaz, Andrew Adamatzky, Cancer, Complexity, Computation, Springer, 2022, pp. 55-97

Data-Driven Simulation of Fisher-Kolmogorov Tumor Growth Models Using Dynamic Mode Decomposition

Alex Viguerie, Malú Grave, Gabriel F. Barros, Guillermo Lorenzo, Alessandro Reali, Alvaro Coutinho

Journal of Biomechanical Engineering, 2022, in press

Towards patient-specific optimization of neoadjuvant treatment protocols for breast cancer based on image-guided fluid dynamics

Chengyue Wu, David Hormuth, Guillermo Lorenzo, Angela Jarrett, Federico Pineda, Frederick M. Howard, Greg Karczmar, Thomas E. Yankeelov

IEEE Transactions on Biomedical Engineering, 2022, in press

Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology

Chengyue Wu, Guillermo Lorenzo, David A. Hormuth, Ernesto A. B. F. Lima, Kalina P. Slavkova, Julie C. DiCarlo, John Virostko, Caleb M. Phillips, Debra Patt, Caroline Chung, Thomas E. Yankeelov

Biophysics Reviews, vol. 3, 2022, p. 021304

Simulating the spread of COVID-19 via a spatially-resolved susceptible–exposed–infected–recovered–deceased (SEIRD) model with heterogeneous diffusion

Alex Viguerie, Guillermo Lorenzo, Ferdinando Auricchio, Davide Baroli, Thomas J.R. Hughes, Alessia Patton, Alessandro Reali, Thomas E. Yankeelov, Alessandro Veneziani

Applied Mathematics Letters, vol. 111, 2021, p. 106617

View all


Academic biography

A brief summary of my education and research experience


Main honors, awards, and competitive grants that I have received during my career


List of my experience in teaching university courses and supervising students


Guillermo Lorenzo, PhD

Marie Skłodowska-Curie global fellow

[email protected]

Oden Institute

The University of Texas at Austin

201 E. 24th Street, POB 2.128
1 University Station (C0200)
Austin, Texas 78712-1229