Cost-efficient Bayesian inference with expensive scientific experiments

Oregon Convention Center 

Data science is at a defining crossroad for modern scientific discovery. On one hand, with remarkable breakthroughs in scientific modeling and experimental technology, reliable data can now be generated for complex systems that were previously unobtainable. On the other hand, the generation of such high-fidelity data requires costly experiments, which greatly limits the amount of available data. This presents a critical bottleneck for modern scientific discovery. My research aims to bridge this gap by developing Bayesian methods (supported by theory & algorithms) that embed scientific knowledge as prior information. This fusing of "data" and "science" within a Bayesian framework allows for principled integration of scientific prior knowledge, thus enabling more accurate and precise scientific findings given a limited experimental cost budget. In this talk, I will present a suite of recent Bayesian methods developed by our group that tackle this integration, motivated by ongoing collaborations in high-energy physics, aerospace engineering and bioengineering.