Arun Kumar Kuchibhotla

Arun Kumar Kuchibhotla is an Assistant Professor in the Department of Statistics & Data Science at Carnegie Mellon University (CMU), within the Dietrich College of Humanities and Social Sciences. His research focuses on foundational aspects of statistical inference, machine learning, and high-dimensional data analysis.

• Education: While specific details are not listed here, his academic trajectory aligns with roles in top-tier institutions.

His research interests span Causal Inference, Statistical Machine Learning, High-Dimensional Statistics, and Nonparametric Methods. He explores theoretical guarantees for algorithms, fair machine learning applications, and algorithmic forecasting of extreme events. Notable areas include:

• Development of robust statistical methods for online algorithms and M-estimation.
• Innovations in conformal prediction, confidence regions, and self-normalization techniques.
• Advancements in fair risk assessments in criminal justice through optimal transport and conformal prediction.

Recent work emphasizes dimension-agnostic adaptive inference and censored time-to-event analysis, with applications to climate science and policy. His publications in 2025 highlight breakthroughs in concentration inequalities, extreme heat wave forecasting, and doubly robust calibration methods.

Advising & Grants: No specific advisees or grants are listed here, but his research aligns with CMU’s emphasis on interdisciplinary projects. He collaborates with groups like the CSAFE and DELPHI labs, focusing on applied statistics and societal impact.

Labs/Teams: Active contributor to CMU’s CSAFE Lab Group (statistical forensics) and DELPHI Lab Group (epidemiological forecasting).