Colorado State University

This summer 14 Statistics and Data Science undergraduates gained valuable experience by participating in research projects.  These paid positions are sponsored by the College of Natural Sciences, the Statistics department and the Data Science program.  Students were supervised by faculty members Yawen Guan, Ann Hess, Dongzhou Huang, Kayleigh Keller, Aaron Nielsen, Ben Prytherch, Ben Shaby, Tianying Wang, Yunpeng Zhao and Tianjian Zhou.  Application areas included fire conditions in the Western US, football analytics, statistical learning in cancer genomics, quantum computing and homeless outreach in Fort Collins.  Students presented their work to the department on June 28.  This is the third year the department has offered this popular program.  Eight participating students from summer 2023 presented at Celebrate Undergraduate Research and Creativity (CURC) on April 18.

June 2024: Piotr Kokoszka has been awarded a 36 month Statistics program grant for the project “Monitoring time series in structured function spaces.” The project aims to develop new mathematical theory and statistical tools that will enable monitoring for changes in complex systems, for example in spatio-temporal patterns of infectious diseases, financial asset portfolios, or global trade networks. It will develop statistical methods for  making decisions about an emergence of an atypical pattern in a complex system in real time and with statistical guarantees of a correct decision. The project will also offer multiple interdisciplinary training opportunities for the next generation of statisticians and data scientists, and will include collaborations with economists in the United Kingdom.

Andee Kaplan (PI) received NSF grant “CAREER: Flexible Record Linkage through Realistic Modeling of Dependent, Missing, and Updating Data.” Record linkage is the process of resolving duplicates in partially overlapping sets of records from noisy data sources without a unique identifier. This project will develop flexible Bayesian record linkage models to enable more accurate linking of data sets through development of flexible priors to incorporate dependence and account for realistic missingness. Additionally, this approach will allow for dynamic data via an updating querying framework for record linkage achieved through a novel filtering approach. This novel methodology will be applied to publicly available and Census Bureau data from the 19th century with identifying information about freedom seekers, captive individuals in the coastwise slave trade, and Black Americans in the Antebellum South. Official records of these individuals are fractured into many sources, each only giving a small glimpse in time. Applying the developed flexible Bayesian record linkage model, this research will reconstruct a fuller account of their lives through linking multiple noisy data sources. Graduate students will be mentored and software will be made publicly available.

The Department of Statistics will host the 2024 WNAR/IMS/Graybill Annual Meeting Sunday, June 9 through Wednesday, June 12. More information can be found here.

Professors Piotr Kokoszka (Statistics) and Olve Peersen (Biochemistry and Molecular Biology) have been recognized by the College of Natural Sciences as 2024 Professor Laureates.
The Professor Laureate Award is the college’s highest academic honor, bestowed upon dedicated faculty with outstanding contributions to the missions of research, teaching, mentoring and outreach. The designation is intended to honor recipients and to provide the college with exceptional role models. The title is held for three years and includes an honorarium and two years of research funding.

September 18-20, 2023, our department will host a workshop on complex environmental data. Its aim is to provide researchers in  Bayesian, environmental and ecological statistics with a venue to share cutting-edge advances and spark deeper knowledge gains. The workshop will facilitate  the transfer of knowledge between research sub-communities, fostering collaboration, and encourage advances from the wider community of researchers in Bayesian analysis of complex datasets to percolate into the environmental domain. Details are available at https://statistics.colostate.edu/envibayes-workshop/