Research

And what, Socrates, is the food of the soul? Surely, I said, knowledge is the food of the soul.

—Plato

Paper

$\dagger$ represents student author at the paper writing time.

Wu, K. Sundararajan,R., Haddad, M., Piancastelli, L., Barreto-Souza and W., Mixed Time Series Quasi-Likelihood Models for Uncovering Covid-19 Viral Load and Mortality Dynamics. (Submitted to Journal of the Royal Statistical Society Series C) Paper Link.
Wu, K. and Politis, D.N., Calibration Prediction Interval for Non-parametric Regression and Neural Networks. (Submitted to Electronic Journal of Statistics) Paper Link.
Wu, K. and Politis, D.N., Deep Limit Model-free Prediction in Regression. (Submitted to ACM/IMS Journal of Data Science) Paper Link.
Ryan, O., Wu, K. and Jacobson, N.C., Exploratory Continuous-Time Modeling (expct): Extracting Dynamic Features from Irregularly Spaced Time Series. (Submitted to Multivariate Behavioural Research).
Wu, K., McFadden, J.R.$^{\dagger}$ and Jacobson, N.C., Determining Timing Effects of Microrandomized Trials Using Intensive Longitudinal Data and The Differential Time-varying Effect Model. (Under working) Paper Link.

Wu, K., Karmakar, S. and Gupta, R., GARCHX-NoVaS: A Model-free Approach to Incorporate Exogenous Variables. Journal of Forecasting, 2025. Paper Link.
Wu, K. and Politis, D.N., Scalable Subsampling Inference of Deep Neural Networks. ACM/IMS Journal of Data Science 2025, 2(1), 1-29. Paper Link.
Wu, K.and Politis, D.N., Bootstrap Prediction Inference of Nonlinear Autoregressive Models, Journal of Time Series Analysis 2024, 45, 800-822. Paper Link.
Wu, K., Gupta, R., Pierdzioch, C. and Karmakar, S., Climate Risks and Stock Market Volatility over A Century in An Emerging Market Economy: The Case of South Africa. Climate 2024, 12(5), 68. Paper Link.
Politis, D.N. and Wu, K., Non-parametric Forward Bootstrap on Predicting Non-linear Time Series: Consistency, Pertinence and Debiasing, Stats 2023, 6(3), 839-867. Paper Link.
Wu, K. and Karmakar, S., A Model-free Approach to Do Long-term Volatility Forecasting and Its Variants, Financial Innovation 2023, 9(59). Paper Link.
Wu, K. and Karmakar, S., Model-Free Time-aggregated Predictions for Econometric Datasets, Forecasting 2021, 3(4), 920-933. Paper Link.

IMS International Conference on Statistics and Data Science Conference, Seville, Spain, 2025
Deep Limit Model Free Prediction in Regression, Talk
NBER-NSF Time Series Conference, Rutgers University, U.S.A., 2025
Types of Distribution-free Methods for Forecasting Financial Volatility, Poster
NBER-NSF Time Series Conference, Rutgers University, U.S.A., 2025
Bootstrap Prediction Inference of Non-linear Autoregressive Models, Co-authored Talk
Statistical Frontiers in LLMs and Foundation Models—NeurIPS, Vancouver, Canada, 2024
Deep Limit Model-free Prediction, Poster
Statistical Frontiers in LLMs and Foundation Models—NeurIPS, Vancouver, Canada, 2024
Subsampling on Deep Neural Networks, Poster
Computational and Methodological Statistics, virtual, 2022
Extracting Dynamic Features from Irregularly Spaced Time Series, Co-authored Talk