Hi ! Welcome to my personal webpage
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.
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
Exploring the biophysical mechanisms underlying PSA dynamics after external radiotherapy to define new biomarkers for the early identification of relapse
This work aims at finding optimal combinations of cytotoxic and antiangiogenic therapies to treat advanced prostatic tumors by combining mathematical analysis and computer simulations
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
Constructing mathematical models to understand and predict the dynamics of COVID-19 infectious spread based on longitudinal epidemiological data series
A.S. Kazerouni, A.N. Dula, A.M. Jarrett, G. Lorenzo, J.A. Weis, J.A. Bankson, E.Y. Chekmenev, F. Pineda, G.S. Karczmar, T.E. Yankeelov
In: K. Pinker, R. Mann, S. Partridge, Breast MRI: State of the Art and Future Directions, Elsevier, pp. 503-531
S. Urcun, G. Lorenzo, D. Baroli, P.Y. Rohan, G. Sciumè, W. Skalli, V. Lubrano, S.P.A. Bordas
In: S.P.A. Bordas, D.S. Balint, Advances in Applied Mechanics, in press
C. Wu, G. Lorenzo, D.A. Hormuth II, E.A.B.F. Lima, K.P. Slavkova, J.C. DiCarlo, J. Virotsko, C.M. Phillips, D. Patt, C. Chung, T.E. Yankeelov
Biophysics Review, vol. 3(2), 2022, p. 021304
E.Y. Yang, G.R. Howard, A. Brock, T.E. Yankeelov, G. Lorenzo
Frontiers in Molecular Biosciences, vol. 9, 2022, p. 972146
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
Guillermo Lorenzo, PhD
Marie Skłodowska-Curie global fellow