Dr. Jason T. George’s research efforts focus on computational and stochastic cancer modeling. |
Image: Texas A&M Engineering
The Department of Biomedical Engineering at Texas A&M University recently welcomed Dr. Jason T. George ’12 to its faculty and celebrated a statewide award to support his cancer research.
George, a new assistant professor in biomedical engineering, has a joint appointment with the School of Engineering Medicine in Houston. He has research space in the Institute for Biosciences and Technology at the Texas Medical Center, which will accelerate clinical and translational research collaborations.
“This is a unique and exciting opportunity for me and my group to apply our engineering-based approach to solve challenging problems in medicine and computational cancer biology,” George said.
His group applies a hybrid theoretical and data-driven approach to understanding cancer evolution and treatment failure. His data-driven research efforts focus on cancer classification and prognosis, while current mathematical modeling projects focus on the role of the adaptive immune system in treating cancer and, based on predictions, which interactions or features may be particularly relevant to the disease.
“For a leukemia patient in need of a new immune system, our predictions could one day be used by a clinician to optimally select the donor with the best chance of eliminating the patient’s cancer,” George said.
George was awarded the Recruitment of First-Time, Tenure-Track Faculty Members Award from the Cancer Prevention and Research Institute of Texas. George said the award would help fund his group’s research to study cancer evasion at a fundamental level.
“With this award, our group will develop and apply theoretical and computational models with the aim of understanding why current treatments fail in certain patients, in addition to predicting improved therapeutic strategies,” George said.
As a new faculty member, George said one place he will focus on is recruiting creative and motivated researchers at all levels who are passionate about solving challenging problems at the interface of engineering and medicine. Research topics include:
George, a new assistant professor in biomedical engineering, has a joint appointment with the School of Engineering Medicine in Houston. He has research space in the Institute for Biosciences and Technology at the Texas Medical Center, which will accelerate clinical and translational research collaborations.
“This is a unique and exciting opportunity for me and my group to apply our engineering-based approach to solve challenging problems in medicine and computational cancer biology,” George said.
His group applies a hybrid theoretical and data-driven approach to understanding cancer evolution and treatment failure. His data-driven research efforts focus on cancer classification and prognosis, while current mathematical modeling projects focus on the role of the adaptive immune system in treating cancer and, based on predictions, which interactions or features may be particularly relevant to the disease.
“For a leukemia patient in need of a new immune system, our predictions could one day be used by a clinician to optimally select the donor with the best chance of eliminating the patient’s cancer,” George said.
George was awarded the Recruitment of First-Time, Tenure-Track Faculty Members Award from the Cancer Prevention and Research Institute of Texas. George said the award would help fund his group’s research to study cancer evasion at a fundamental level.
“With this award, our group will develop and apply theoretical and computational models with the aim of understanding why current treatments fail in certain patients, in addition to predicting improved therapeutic strategies,” George said.
As a new faculty member, George said one place he will focus on is recruiting creative and motivated researchers at all levels who are passionate about solving challenging problems at the interface of engineering and medicine. Research topics include:
- Computational cancer modeling
- Immunology and cancer immunotherapy
- Probabilistic modeling of biological systems
- Systems biology and stochastic modeling of phenotypic transitions in oncology