Author: Chen, Kun

Chen won the 2024 Innovative Scholarship Award from CLAS

Kun Chen has been selected as the winner of the 2024 Innovative Scholarship Award from the College of Liberal Arts and Sciences (CLAS) at UConn. 

Innovative Scholarship Award is awarded in recognition of outstanding achievements in interdisciplinary research that engage novel intersections to address major challenges to knowledge, well-being, and our world.  CLAS Annual Awards recognize the extraordinary contributions of faculty and staff and are the highest honor given by the College. The awards are designed to highlight activities that advance the strategic goals and mission of CLAS. 

Chen is very honored to receive the award and grateful for the recognition from his peers.

PharmaDS Conference successfully held at UConn

Thrilled to share that the inaugural Pharmaceutical Data Science Conference ( at UConn (03/18 – 03/19) was a great success! A journey that took three years from its initial conception to fruition, this conference was spearheaded by elite members from pharmaceutical companies’ data science teams. I’m immensely proud to have collaborated with these incredible partners to bring this vision to life.

This unique conference bridged the entire data science lifecycle within the pharmaceutical sector, touching upon many critical application issues. Both UConn students and I have gained immensely from this experience.

The timing couldn’t have been more perfect, aligning with the vibrant 800-person fireside chat on “Empowering Statistics in the Era of AI” just days prior. The discussions there resonated deeply with the essence of our conference, highlighting the crucial cross-talk and problem-driven training that current statistics education might overlook.

It’s a reminder of the transformative power of data science and the importance of fostering an environment where vital conversations across academia, industry, and government agencies can take place. Looking forward to many more milestones like this!


Xiaohui Yin won ASA Student Paper Award

PhD student Xiaohui Yin won the 2024 ASA Mental Health Statistics Section Student Paper Award based on our work “Salvaging Forbidden Treasure in Medical Data: Utilizing Single-Record Data to Improve Suicide Risk Prediction via Integrative Learning.”

Congratulations, Xiaohui!


Join us at NERDS 2023 (10/12-10/13)

Join us for the 6th Annual NERDS Workshop, a unique gathering dedicated to addressing the statistical challenges in rare disease drug development. NERDS workshop provides a common platform for knowledge exchange, idea sharing, and networking among statisticians, regulators, and industry experts in the rare disease space.


Contact: Co-Chairs Kun Chen ( and Richard Zhang (

Project on utilizing ML for studying soil health funded by USDA

Our project “Elucidating the impact of nanoagrichemicals on paddy soil health and rice production through combined greenhouse studies and machine learning” is funded by USDA. The project is a collaboration with Dr. Samuel Ma (PI) and Dr. Fugen Dou (Co-PI) at TAMU. Chen (Co-PI) will guide the experimental design and data collection and utilize innovative statistical and machine-learning methods in the proposed research.

Book on reduced-rank regression is published by Springer

Chen’s co-authored book on reduced-rank regression has been published by Springer.

Reinsel, G. C., Velu, R. P., and Chen, K. (2022) Multivariate Reduced-Rank Regression: Theory, Methods and Applications, 2nd Edition. Springer.

The book’s first edition has profoundly influenced Chen’s research since his graduate study at the University of Iowa under the supervision of Professor Kung-Sik Chan. Chen is honored and humbled to be a co-author in this new edition. 


This book provides an account of multivariate reduced-rank regression, a tool of multivariate analysis that enjoys a broad array of applications. In addition to a historical review of the topic, its connection to other widely used statistical methods, such as multivariate analysis of variance (MANOVA), discriminant analysis, principal components, canonical correlation analysis, and errors-in-variables models, is also discussed.

This new edition incorporates Big Data methodology and its applications, as well as high-dimensional reduced-rank regression, generalized reduced-rank regression with complex data, and sparse and low-rank regression methods. Each chapter contains developments of basic theoretical results, as well as details on computational procedures, illustrated with numerical examples drawn from disciplines such as biochemistry, genetics, marketing, and finance.

This book is designed for advanced students, practitioners, and researchers, who may deal with moderate and high-dimensional multivariate data. Because regression is one of the most popular statistical methods, the multivariate regression analysis tools described should provide a natural way of looking at large (both cross-sectional and chronological) data sets. This book can be assigned in seminar-type courses taken by advanced graduate students in statistics, machine learning, econometrics, business, and engineering.