2025 Statistics Newsletter

Message from the Chair
Department Spotlights
Department Kudos
Alumni Class Notes 

Message from the Chair

Warm greetings to all our alumni and friends from the Department of Statistics at George Washington University!

As I conclude my third and final year as department chair, I would like to express my heartfelt gratitude to our faculty, students, staff, alumni and friends for their unwavering support throughout this journey. It has been a tremendous honor and a rewarding experience to serve a department so deeply committed to excellence in research, teaching and service.

This past year marked a special milestone—our department’s 90th anniversary. We celebrated with a dynamic conference, “The Past, Present, and Future of Statistics in the Era of AI,” which brought together over 100 participants—from junior researchers to established scholars, along with current and former students and faculty and collaborators from around the world.

Our community continues to shine. Faculty, students and alumni have received prestigious awards, new grants and well-deserved recognitions for their diverse range of contributions.

We’ve also deepened our regional partnerships. Over the past two years, we’ve built a strong and growing collaboration with our colleagues at George Mason University’s Department of Statistics. Together, we co-organized alumni receptions at JSM, launched DMV Statistics Day—first hosted by us in 2024, then by GM in 2025—and teamed up on research and institute grant proposals. This collaboration is now expanding to include Georgetown, the University of Maryland and American University.

As I step down, I’m delighted to share that Professor Feifang Hu will serve as the next department chair. I am confident the department is in excellent hands. While we may face uncertainties ahead, I remain optimistic. Our future is bright as long as we stay connected, support one another and continue to build strong partnerships.

Sincerely,

Huixia Judy Wang
Chair, Department of Statistics

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Department Spotlights 

Professor Apanasovich Champions Public Interest Research 

Professor Tatiyana Apanasovich advances climate resilience and health equity through data-driven public interest research. As principal investigator (PI) on a project supported by the Research and Engagement for Action on Climate and Health (REACH) Center—a multi-institutional exploratory center funded by the National Institute for Environmental Health Sciences—she leads a multidisciplinary team at GW. The team works with D.C. agencies to model how flooding impacts health outcomes in socially vulnerable communities.

By integrating environmental, health and equity data, the project informs targeted interventions and supports evidence-based policymaking. Professor Apanasovich is also the lead PI of GW’s Data Science Bootcamp (with co-PI Professor Huixia Judy Wang), one of only 10 projects selected nationwide by the 2024 Public Interest Technology University Network (PIT-UN) Challenge. The program, which was featured in GW Today, hosted 14 students from across the country as part of an interdisciplinary initiative to advance health equity and climate resilience.

In partnership with Trinity Washington University and the University of the District of Columbia, the four-week bootcamp equipped students with ethical, equity-centered data science skills applied to real-world challenges in climate and public health. Students participated in lectures, hands-on training and collaborative research projects. Guided by faculty, graduate mentors and local experts, participants analyzed climate and public health data to explore how environmental factors shape health outcomes in their communities.

Together, these efforts reflect Professor Apanasovich’s commitment to advancing public interest technology and leveraging statistical science to tackle urgent societal challenges. 
 

Professor Liang’s Innovations Lead to Broad Impact

Professor Hua Liang is an expert on semiparametric regression models. He has been working in the area since 1990, and has published a series of related articles in Annals of Statistics, Biometrika, JASA and JRSSB as well as two books. 

In a recently published article in American Statistician, he and his collaborators suggested an efficient computation strategy for efficiently estimating parameters and nonparametric functions in various generalized single-index models by integrating with well-developed algorithms and packages for estimating the generalized additive models. Such an integration makes estimation and inference in these index-type models much easier, expedient, and flexible, bringing considerable convenience. 

In addition, Professor Liang has applied the developed methods in this area to solve various biomedical problems, and these contributions have been published in JAMA, JAIDS and Virology. He, therefore, was awarded two R01 grants. His other research interests include high-dimensional model averaging and variable selection; mixed-effects models; and measurement errors models.

Over the last 10 years, Professor Liang has made significant efforts in goodness-of-fit assessment for various regression models, proposing projection-based tests that achieve dimension reduction and behave as if only a single covariate were present. The nonparametric tests are shown to be root-n consistent and can detect Pitman local alternative hypothetical models.

He has published over 80 statistical articles. Besides methodological research, he enjoys collaborations and has worked with numerous investigators at the GW Medical School, DCCFAR and the Children’s National Medical Center over the past decade. 
 

The Past, Present, and Future of Statistics in the Era of AI

GW Statistics proudly hosted “The Past, Present, and Future of Statistics in the Era of AI” from in May 2025, as part of our 90th anniversary celebration. The conference was supported by National Science Foundation (NSF) grant DMS-2514925 (PI: Professor Tatiyana Apanasovich; Co-PIs: Professors Xiaoke Zhang and Subrata Kundu).

With over 100 participants, the event brought together established researchers, emerging scholars and industry professionals—alongside friends of the department, students, alumni and current and former faculty from around the world. The program featured two short courses, two plenary talks, multiple distinguished alumni lectures, a poster session and lively panel discussions.

The conference highlighted the strong demand and enthusiasm across academia and industry for high-quality gatherings focused on pressing statistical challenges and opportunities in the AI era. Through discussions on emerging trends and the presentation of innovative methodologies, the conference successfully contributed to advancing research at the intersection of statistics and AI.

We are grateful to our organizing committee (chaired by Professor Zhang), student volunteers and generous sponsors.

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Department Kudos

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Alumni Class Notes

• Jingdi Chen, MS ’19, will join the Department of Electrical & Computer Engineering, University of Arizona, as a tenure-track assistant professor in fall 2025.
• Usha Govindarajulu, MS ’99, is an associate professor of Biostatistics at Icahn School of Medicine at Mount Sinai in New York, N.Y.
• Huan Lu, MS’16, was promoted to manager, clinical data and AI processing at Sanofi.
• Rui Miao, PhD ’22, will join the Department of Mathematical Sciences, University of Texas at Dallas, as a tenure-track assistant professor in fall 2025.
• Annabelle Pham, BS ’23, is a program analyst at the United States Census Bureau. She oversees the design of the administrative data frames for the 2030 Decennial Census.
• Blyth Riegel Stenner, MA ’73, worked in educational administration, database design and administration, research and evaluation and psychometrics for over 40 years. He is also an active consultant and grant writer, contracted evaluator.
• Biao Zhang, MS ’14, is in Columbus, Ohio, and working as a quant associate in JP Morgan & Chase Co.

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