Innovation in clinical trial design has become paramount to exploring ways to accelerate clinical development for CNS disorders and Immunology. Approaches that use existing datasets to reduce the size of clinical trials have come to the forefront, but the use of external control arms is limited due the inability to control bias. In this webinar, Unlearn.AI will highlight a novel technology that leverages existing data that can be applied to randomized controlled trials (RCTs) to enable smaller, more efficient clinical trials without introducing bias. RCTs that incorporate Digital Twins, or predicted placebo outcomes, can be run faster while maintaining power, reliability, and safety.
Charles is a scientist with interests at the intersection of physics, machine learning, and computational biology. Previously, Charles worked as a machine learning engineer at Leap Motion and a computational biologist at Pfizer. He was a Philippe Meyer Fellow in theoretical physics at École Normale Supérieure in Paris, France, and a postdoctoral scientist in biophysics at Boston University. Charles holds a Ph.D. in biophysics from Harvard University and a B.S. in biophysics from the University of Michigan.
Dave is a biostatistician with expertise in prognostic models, clinical trials, observational studies, and diagnostic devices. Most recently, Dave was Head of Biostatistics & Epidemiology for Verily Life Sciences. Other past roles include Lead Program Biostatistician at Genomic Health and Sr Dir of Medical Affairs Statistical Analysis at ICON Clinical Research. Dave received his bachelors and masters in Statistics from Carnegie Mellon. He has co-authored over 100 peer-reviewed papers in medical journals.
Arsalan Arif is a news media entrepreneur who set out in 2015 to build his vision of an independent biotech news company at Endpoints News.