Area of research
Statistics and Probability
Research interest
Research interests include Statistical Methods and Inference, Statistical Methods and Bayesian Inference, Advanced Statistical Methods and Models, and Advanced Causal Inference Techniques.
A nonparametric doubly robust test for a continuous treatment effect
Role of Variable Renewable Energy Penetration on Electricity Price and its Volatility across Independent System Operators in the United States
ANALYSIS OF GLOBAL AND LOCAL OPTIMA OF REGULARIZED QUANTILE REGRESSION IN HIGH DIMENSIONS: A SUBGRADIENT APPROACH
High-Dimensional Quantile Regression: Convolution Smoothing and Concave Regularization
Fairness-Oriented Learning for Optimal Individualized Treatment Rules
A Tuning-free Robust and Efficient Approach to High-dimensional Regression
Double-slicing assisted sufficient dimension reduction for high-dimensional censored data
Loss of Asxl2 leads to myeloid malignancies in mice
Salicylate, diflunisal and their metabolites inhibit CBP/p300 and exhibit anticancer activity
Partially linear additive quantile regression in ultra-high dimension
Integrative genetic analysis of mouse and human AML identifies cooperating disease alleles
Distributional Reinforcement Learning for Risk-Sensitive Sequential Decision Making: New Theory and Methods
Collaborative Research: Predictive Risk Investigation SysteM (PRISM) for Multi-layer Dynamic Interconnection Analysis
FRG: Collaborative Research: Quantile-Based Modeling for Large-Scale Heterogeneous Data
Collaborative Research: Predictive Risk Investigation SysteM (PRISM) for Multi-layer Dynamic Interconnection Analysis
I-Corps: Use of eHealth to Personalize Exergame Prescriptions
NeTS: Student Travel Support for the 2017 SIGCOMM Conference
CRI-New: Collaborative: Building the Core NDN Infrastructure
Collaborative Research: High-Dimensional Projection Tests and Related Topics
FIA-NP: Collaborative Research: Named Data Networking Next Phase (NDN-NP)
New Developments on Quantile Regression Analysis of Censored Data: Theory, Methodology and Computation
FIA: Collaborative Research: Named Data Networking (NDN)
Semiparametric Inference for High-dimensional Correlated or Heterogeneous Cross-sectional Data with Discrete Response
NeTS-FIND: Collaborative Research: Enabling Future Internet innovations through Transit wire (eFIT)
Semiparametric and Nonparametric Methods of Model Selection and Model Checking for Correlated Data
CRI: Collaborative Research: Building the Next-Generation Global Routing Monitoring System
Rational Points on Algebraic Varieties