Data Science Fridays: Dr. Lingge Li
February 14, 2:00pm - 3:00pm
Mānoa Campus, Keller Hall 103
Improving Statistical Inference with Flexible Approximations
Speaker: Dr. Lingge Li, University of California, Irvine
In the statistics and machine learning communities, there exists a perceived dichotomy between parameter inference and out-of-sample prediction. Statistical inference is often done with models that are carefully specified a priori while out-of-sample prediction is often done with “black-box” models that have greater flexibility. With technological advancements, scientists can now collect overwhelming amounts of data in various formats and their objective is to make sense of the data. To this end, we propose the synergy of statistical inference and prediction workhorses that are neural networks and Gaussian processes.
Lingge Li is a recent statistics PhD graduate from University of California Irvine. His research topics include computational statistics as well as machine learning applications in high energy physics and neuroscience.
Hawai'i Data Science Institute, Mānoa Campus