QIYANG HAN

Department of Statistics
Rutgers, the State University of New Jersey
110 Frelinghuysen Road
Piscataway, NJ 08854, United States

Office: 408 Hill Center
Email:

About me

I am an Associate Professor of Statistics at Rutgers University. I completed my Ph.D. in 2018 from University of Washington under the supervision of Professor Jon A. Wellner. My research lies at the theoretical interface of statistics, algorithms and probability. Some major research themes include (i) empirical process theory, (ii) constrained nonparametric inference, (iii) mean-field high dimensional statistics, and more recently (iv) large random iterative algorithms.

Papers

  (* = non alpha-order, multiple * = equal contribution)

• Q. Han and X. Xu. Gradient descent inference in empirical risk minimization.

• Q. Han, K. Khamaru and C.-H. Zhang. UCB algorithms for multi-arm bandits: precise regret and adaptive inference.

• Q. Han. Entrywise dynamics and universality for general first order methods.

• Z. Bao, Q. Han and X. Xu. A leave-one-out approach to approximate message passing.

• Q. Han and X. Xu. The distribution of Ridgeless least squares interpolators.

• Q. Han and H. Ren. Gaussian random projections of convex cones: approximate kinematic formulae and applications.

• Q. Han. Exact bounds for some quadratic empirical processes with applications.

• Q. Han and Y. Shen. Universality of regularized regression estimators in high dimensions. Ann. Statist., 51, 1799-1823.

• Q. Han. Noisy linear inverse problems under convex constraints: exact risk asymptotics in high dimensions. Ann. Statist., 51, 1611-1638.

• Q. Han and Y. Shen. Generalized kernel distance covariance in high dimensions: Non-null CLTs and power universality. Inf. Inference, to appear.

• *O. Y. Feng, Y. Chen, Q. Han, R. J. Carroll and R. J. Samworth. Nonparametric, tuning-free estimation of S-shaped functions. J. Roy. Statist. Soc., Ser. B, 84, 1324-1352.

• Q. Han, T. Jiang and Y. Shen. Contiguity under high dimensional Gaussianity with applications to covariance testing. Ann. Appl. Probab., 33, 4272-4321.

• Q. Han, B. Sen and Y. Shen. High dimensional asymptotics of likelihood ratio tests in the Gaussian sequence model under convex constraints. Ann. Statist., 50, 376-406.

• H. Deng, Q. Han and B. Sen. Inference for local parameters in convexity constrained models. J. Amer. Statist. Assoc., 118, 2721-2735.

• *Y. Shen, Q. Han and F. Han. On a phase transition in general order spline regression. IEEE Trans. Inform. Theory, 68, 4043-4069.

• H. Deng, Q. Han and C.-H. Zhang. Confidence intervals for multiple isotonic regression and other monotone models. Ann. Statist., 49, 2021-2052.

• Q. Han and K. Kato. Berry-Esseen bounds for Chernoff-type non-standard asymptotics in isotonic regression. Ann. Appl. Probab., 32, 1459-1498.

• Q. Han. Set structured global empirical risk minimizers are rate optimal in general dimensions. Ann. Statist., 49, 2642-2671.

• Q. Han. Multiplier U-processes: sharp bounds and applications. Bernoulli, 28, 87-124.

• Q. Han and J. A. Wellner. Complex sampling designs: uniform limit theorems and applications. Ann. Statist., 49, 459-485.

• Q. Han and C.-H. Zhang. Limit distribution theory for block estimators in multiple isotonic regression. Ann. Statist., 48, 3251-3282.

• Q. Han and J. A. Wellner. Robustness of shape-restricted regression estimators: an envelope perspective.

• *Q. Han, *T. Wang, S. Chatterjee and R. J. Samworth. Isotonic regression in general dimensions. Ann. Statist., 47, 2440-2471.

• Q. Han and J. A. Wellner. Convergence rates of least squares regression estimators with heavy-tailed errors. Ann. Statist., 47, 2286-2319.

• Q. Han. Oracle posterior contraction rates under hierarchical priors. Electron. J. Statist., 15, 1085-1153.

• Q. Han and J. A. Wellner. Multivariate convex regression: global risk bounds and adaptation.

• Q. Han and J. A. Wellner. Approximation and estimation of s-concave densities via Rényi divergences. Ann. Statist., 44, 1332-1359.

• *A. Jalali, Q. Han, I. Dumitriu and M. Fazel. Exploiting tradeoffs for exact recovery in heterogeneous stochastic block models. NeurIPS 2016, 29.