My research interests are
selective inference, high-dimensional statistics, machine learning theory, and reinforcement learning.
My Google scholar page can be
found here.
Selective Inference
Broadly construed, selective inference means searching for interesting patterns in data, usually with inferential guarantees that account for the search process. It encompasses:
1. Multiple testing: testing many hypotheses at once (and paying disproportionate attention to rejections)
2. Post-selection inference: examining the data to decide what question to ask, or what model to use, then carrying out one or more appropriate inferences
3. Adaptive / interactive inference: sequentially asking one question after another of the same data set, where each question is informed by the answers to preceding questions
4. Cheating: cherry-picking, double dipping, data snooping, data dredging, p-hacking, HARKing, and other low-down dirty rotten tricks; basically any of the above, but done wrong!
High-dimensional Statistics
In statistical theory, the field of high-dimensional statistics studies data whose dimension is larger than typically considered in classical multivariate analysis. The area arose owing to the emergence of many modern data sets in which the dimension of the data vectors may be comparable to, or even larger than, the sample size, so that justification for the use of traditional techniques, often based on asymptotic arguments with the dimension held fixed as the sample size increased, was lacking.
Machine Learning Theory
Statistical learning theory, a framework of statistical machine learning, is based on the knowledge of accidents and functional analysis. The theoretical basis of Support Vector Machine comes from statistical learning theory.
Reinforcement Learning
Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.
