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...
School of Mathematics, University of Edinburgh, UK
Sjoerd Beentjes is a tenured Assistant Professor in Biostatistics at the School of Mathematics, University of Edinburgh, UK. His research interests primarily lie in causal inference and high-dimensional statistics, and developing rigorous statistical inference procedures with application domains such as genomics, molecular biology, and health informatics, in particular in collaboration with regulatory bodies. He completed a PhD in pure mathematics, and his other research interests include leveraging ideas from pure mathematics in causal inference and statistics. Recent work has been (i)...
Epidemiology & Biostatistics - UCSF School of Medicine
Clinical Informatics and Digital Transformation - UCSF School of Medicine
Efstathios (Stathis) D. Gennatas, MBBS AICSM PhD, is an Assistant Professor in the Department of Epidemiology and Biostatistics and affiliated with the Division of Clinical Informatics and Digital Transformation, Department of Medicine at UCSF. His work focuses on 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...
Ava Khamseh is a tenured Assistant Professor in Biomedical AI at the University of Edinburgh, UK. Her cross-disciplinary research focuses on developing and applying causal machine learning in genomics and health informatics. She is also the Deputy Director of the Centre for Doctoral Training in AI for Biomedical Innovation at the School of Informatics. Recent work has been in (i) method development in high dimensional statistics for quantifying cell types and states in healthy and disease tissue using single cell RNA-sequencing (Stator), (ii) Targeted Learning estimators for application to...
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...
He earned his PhD in Biostatistics from Johns Hopkins Bloomberg School of Public Health. Dr. Wang's research involves method development with targeted maximum likelihood estimation in longitudinal mediation, computerized and higher order efficient estimation, electronic health record data analysis, as well as reproducibility and clinical trials with brain functional connectivity.
