The National Institute of Statistical Sciences (NISS) is pleased to highlight the 2026 NISS New Researcher Award recipients, who will present invited talks at the Spring Research Conference (SRC), hosted by Clemson University in Clemson, South Carolina, from May 26–28, 2026. The award recognizes innovative early-career researchers whose work demonstrates strong potential for advancing statistical science and its applications.
This year’s awardees—Chi‑Kuang Yeh (Georgia State University), Jie Min (University of South Florida), Chun‑Yi Chang (Michigan State University), and Difan Song (Georgia Institute of Technology)—will present research spanning optimal experimental design, computational statistics, machine learning, and computer experiments. Their invited presentations reflect the breadth and depth of contemporary statistical research and its growing impact across science, engineering, and data-driven decision-making.
Advancing Optimal Design and Public Health Screening
On May 26, Chi‑Kuang Yeh, Assistant Professor of Mathematics and Statistics at Georgia State University, will speak in the invited session on Reliability. His talk, “Optimal Group Testing Design under Cost Constraints with Application to Chlamydia Screening,” introduces a robust optimal design framework for group testing. By incorporating maximin criteria, Yeh’s approach improves parameter estimation under both cost constraints and model uncertainty, with direct relevance to public health screening programs where resources are limited and efficient testing strategies are critical.
Yeh’s research lies at the intersection of functional data analysis, statistical machine learning, and optimal experimental design, with applications motivated by challenges in biomedicine and neuroscience.
Innovations in Bayesian Computation for Complex Systems
On May 27, Jie Min, Assistant Professor in the Department of Mathematics and Statistics at the University of South Florida, will present in the invited session Innovations for Challenges in Engineering. Her talk, “Block No‑U‑Turn Samplers for Spatially Correlated Accelerated Failure Time Models,” introduces a more efficient Markov chain Monte Carlo (MCMC) framework for spatial survival analysis.
By exploiting block structures within No‑U‑Turn Samplers (NUTS), Min’s methodology significantly reduces computational burden while maintaining high-quality posterior sampling. Her work supports reliability analysis and modeling in complex engineering systems, including autonomous vehicles, high-performance computing systems, and AI-driven technologies.
Scalable Machine Learning with Deep Gaussian Processes
Also on May 27, Chun‑Yi Chang, a fourth‑year PhD student in Statistics and Probability at Michigan State University, will present “Scalable Deep Gaussian Processes via Stochastic Gradient Descent” in one of the Digital Twin invited sessions. Chang’s presentation addresses long-standing scalability challenges in Deep Gaussian Processes (DGPs), which are powerful but computationally demanding models for complex data.
Her work introduces a mini-batch stochastic gradient descent framework that enables efficient latent representation learning, making DGPs practical for large-scale datasets while maintaining strong predictive performance. Chang’s research contributes to surrogate modeling, uncertainty quantification, and machine learning for computer experiments.
Active Learning and Efficient Computer Experiments
On May 28, Difan Song, a fifth-year PhD student in Industrial Engineering (Statistics specialization) at Georgia Tech and incoming postdoctoral researcher at Harvard University, will present in the Technometrics invited session. His talk, “Efficient Active Learning Strategies for Computer Experiments,” offers a new perspective on active learning by leveraging screening designs and a novel Gaussian process kernel.
Song’s approach yields substantial gains in both emulation accuracy and optimization efficiency, particularly in settings where data acquisition is expensive. His broader research portfolio includes applications in high-energy physics, calibration of complex simulation models, and experimental design for black-box systems. His work has appeared in leading journals such as the Journal of the American Statistical Association and Technometrics.
Celebrating Emerging Leaders in Statistical Science
The NISS New Researcher Awards underscore the Institute’s ongoing commitment to supporting and elevating emerging leaders in statistics and data science. By recognizing researchers at critical stages of their careers, NISS aims to foster innovation and strengthen connections across academia, industry, and government.
NISS congratulates the 2026 award recipients and looks forward to their contributions at the Spring Research Conference and beyond.