UCSF and UC Berkeley's Computational Precision Health
Dr. Ahmed Alaa is an Assistant Professor of Computational Precision Health at the University of California, Berkeley and the University of California, San Francisco. His research focuses on developing machine learning (ML) methods to solve real-world problems in precision medicine. The overarching goal of his research is to develop ML models that can identify the best treatment for each individual patient based on their clinical features and characteristics. To this end, his lab focuses on developing multimodal deep learning models to learn representations of patient data across different...
Epidemiology & Biostatistics - UCSF School of Medicine
Efstathios (Stathis) D. Gennatas, MBBS AICSM PhD, is an Assistant Professor in the Department of Epidemiology and Biostatistics at UCSF. His work includes applied data science in basic research, clinical predictive modeling using electronic health records to improve patient outcomes, and the development of machine learning methods and software for biomedical data analysis. His overarching goal is to help make precision medicine a reality in a safe, fair, and efficient way. He is an affiliate of the Institute for Global Health Sciences, the Bakar Computational Health Sciences...
University of California San Francisco, Department of Anesthesia and Perioperative Care
Dr Pirracchio is a M.D., MPH, Ph.D., hailing from Paris. He obtained his M.D. in 2003, with a specialization in Anesthesiology and Critical Care Medicine. In 2008, he obtained an MPH and completed his doctoral studies in Biostatistics in Paris, France in 2012. In 2012-2013, he spent a year as a postdoctoral fellow in Biostatistics in the School of Public Health at the University of California, Berkeley. Back in Paris, he was the clinical director of the surgical and trauma ICU at European Hospital Georges Pompidou (2013-2015) and a researcher in Biostatistics with the INSERM U-1153...
Vice Chair of Machine Learning/Director of Clinical AI
Moffitt Cancer Center
Beginning with my foundational training in medical physics and evolving through a specialized residency in Therapeutic Medical Physics, my trajectory has been one of merging clinical insights with innovative data-driven research. Early endeavors in harnessing machine learning for patient outcomes took a pivotal turn during my K08 award, where I was privileged to work closely with Dr. Jerome Friedman, one of the pioneering fathers of Statistical Learning, at Stanford. His teachings catalyzed my drive for innovations such as the Additive Tree. Further refining my craft, my...