Ph.D. Statistics.
News:
I have released my first-authored paper on uncertainty quantification in LLMs: "CITE: Anytime Valid Statistical Inference in LLM Self-Consistency" (joint work with N. Iwase, Y. Ichihara, J. Komiyama and M. Imaizumi).
I have released my new paper "Fixed-Level Calibration of the Cauchy Combination Test" (solo-author).
I have released my new paper "Finite-Sample Inference for Sparsely Permuted Linear Regression" (joint work with Masaaki Imaizumi), revise requested by J. Amer. Stat. Assoc.
Affiliations:
Project Assistant Professor at Komaba Institute for Science, University of Tokyo, Department of Basic Science, Graduate School of Arts and Science (Imaizumi-lab)
Visiting Scientist at RIKEN Center for Advanced Intelligence Project, High-Dimensional Structure Theory Team
Curriculum Vitae:
Before joining the University of Tokyo/RIKEN, I completed my Ph.D. in Statistics at Rutgers University (USA) and I am very fortunate to be advised by Professor Min-ge Xie. I also received M.A. in Economics at Department of Statistics, Graduate School of Economics, University of Tokyo, and B.A. in Economics at Kyoto University.
E-mail: hirofumi-ota (at) g.ecc.u-tokyo.ac.jp
(previous email address "ho105 (at) stat.rutgers.edu" and "ho105 (at) rutgers.edu" are no longer in use).
Recent research themes:
My research interest lies in mathematical foundations of statistical inference, data science and artificial intelligence.
Especially, my research is driven by problems in modern data science that resist existing uncertainty quantification frameworks. I develop new theory and methods that broaden the class of models and data structures for which reliable parameter inference can be achieved.
(i) modern uncertainty quantification
selective inference
irregular inference problems (discrete, non-numerical parameters)
repro samples method, universal inference, conformal inference, likelihood-free inference, etc.
statistical guarantees for reliable reasoning in LLMs
(ii) E-values/E-processes
theory, method and applications to modern ML
sequential inference
(iii) econometrics
quantile models
non-standard asymptotics
(iv) mathematical statistics
high-dimensional/nonparametric statistics
information aggregation
shape-constrained estimation and inference
Honors and Awards:
Best presentation award at the Conference of Japan Statistical Society, 2025
Truman-Koehler Scholarship/Excellence Award (Rutgers University, USA)
Grant-in-Aid for JSPS Fellows (DC1, Japan)
Reviewing services:
Electronic Journal of Statistics, Journal of Computational and Graphical Statistics, Journal of Time Series Analysis, Annals of Institute of Statistical Mathematics, Econometric Reviews, Scandinavian Journal of Statistics, Journal of Business & Economic Statistics.
Other professional experiences:
Office of Statistical Consulting (Department of Statistics, Rutgers University) -- consulting services on statistical theory and methodology for companies, researchers, and graduate students.