Gilmer Valdes

After completing my fellowship and clinical residency in Therapeutic Medical Physics at UPENN, my early training focused on developing machine learning algorithms and advanced modeling to enhance quality assurance, predict patient outcomes, and tailor treatments based on the type and quality of available data, particularly for prostate and lung cancer patients. More recently, I have been involved in advancing methods to address the limitations of current ML algorithms in medicine, including MediBoost, The Additive Tree, Expert Augmented Machine Learning, The Conditional Super Learner, Representational Gradient Boosting, and The Linear Additive Tree or Lockout. These projects have honed my expertise in customizing advanced ML techniques for medical applications. In my current role as Vice Chair of Machine Learning and Director of Clinical AI at Moffitt Cancer Center. I oversee the integration of cutting-edge AI technologies into clinical practice, driving innovations that enhance patient care and outcomes. I lead a team dedicated to bridging the gap between machine learning research and practical clinical solutions. I have received multiple awards including several federal grants and I am the PI of two awarded patents.