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  Ramón y Cajal Fellowship from the Spanish Ministry of Science, Innovation, and Universities, I am currently working at the Group of Numerical Methods in Engineering in the Department of Mathematics of the University of A Coruña in Spain. Additionally, I am  a research affiliate  at the Center for Computational Oncology at the Oden Institute in The University of Texas at Austin.

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.

Research




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

Publications


A Pilot Study on Patient-specific Computational Forecasting of Prostate Cancer Growth during Active Surveillance Using an Imaging-informed Biomechanistic Model


Guillermo Lorenzo, Jon S Heiselman, Michael A Liss, Michael I Miga, Hector Gomez, Thomas E Yankeelov, Alessandro Reali, Thomas JR Hughes

Cancer Research Communications, vol. 4, 2024, pp. 617--633


A mathematical model for predicting the spatiotemporal response of breast cancer cells treated with doxorubicin


Hugo JM Miniere, Ernesto ABF Lima, Guillermo Lorenzo, David A Hormuth, Sophia Ty, Amy Brock, Thomas E Yankeelov

Cancer Biology & Therapy, vol. 25, 2024, p. 2321769


Designing clinical trials for patients who are not average


Thomas E Yankeelov, David A Hormuth, Ernesto ABF Lima, Guillermo Lorenzo, Chengyue Wu, Lois C Okereke, Gaiane M Rauch, Aradhana M Venkatesan, Caroline Chung

Iscience, vol. 27, 2024, p. 108589


Predictive digital twin for optimizing patient-specific radiotherapy regimens under uncertainty in high-grade gliomas


Anirban Chaudhuri, Graham Pash, David A Hormuth, Guillermo Lorenzo, Michael Kapteyn, Chengyue Wu, Ernesto ABF Lima, Thomas E Yankeelov, Karen Willcox

Frontiers in Artificial Intelligence, vol. 6, 2023, p. 1222612


Dynamic parameterization of a modified SEIRD model to analyze and forecast the dynamics of COVID-19 outbreaks in the United States


Orhun O Davarci, Emily Y Yang, Alexander Viguerie, Thomas E Yankeelov, Guillermo Lorenzo

Engineering with Computers, 2023, pp. 1--25


View all

Pages


Academic biography

A brief summary of my education and research experience


Achievements

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


Teaching

List of my experience in teaching university courses and supervising students

Contact


Guillermo Lorenzo, PhD

Ramón y Cajal Research Fellow



Group of Numerical Methods in Engineering, Department of Mathematics

University of A Coruña

Grupo de Métodos Numéricos en Enxeñaría
ETSE de Enxeñaría de Camiños, Canais e Portos
Campus de Elviña s/n
15008 A Coruña
Spain


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