A team of statisticians from Colorado State University that has spent the pandemic helping the State of Colorado forecast spikes in COVID-19 cases says the recent surge in cases in Colorado is mysterious. While some cases can easily be tracked back, the overall surge that Colorado has seen in recent weeks hasn’t been linked to one underlying factor.

“I don’t know if we will ever know what is causing this spike,” said Dr. Bailey Fosdick, Associate Professor in CSU’s Department of Statistics.

Read full story here.

Channing Parker served as a Statistical Consultant on a project at University of Pittsburgh in 2019, which resulted in a publication in early 2021. The paper, titled “Eye trauma in falls presenting to the emergency department from 2006 through 2015” by Bushra Usmani, Asad Latif, Mustafa Iftikhar, Yasir J Sepah, Channing Parker, Justin A Fliss, Kunal K Dansingani, and Syed Mahmood Ali Shah, was published in the British Journal of Ophthalmology on February 1st of 2021. The purpose of this project was to characterize the epidemiology of eye trauma in the event of falls presenting to the emergency departments (ED) in the USA.

Prof. Wen Zhou has been awarded a new NIH R01grant (1R01GM144961, 2021-2024) as the PI to study the higher order dependence in high dimensional data and its applications in learning protein functionality and structure. Collaborating with Prof. Zhao Ren from Statistics at University of Pittsburgh, Prof. Robert Jernigan from Biochemistry, Biophysics and Molecular Biology and Dr. Kejue Jia from Bioinformatics and Computational Biology at Iowa State University, in this project, Wen will tackle the important problem of quantifying and drawing inference on the higher order dependence, and biologically, how to integrate protein structure and sequence information in complex systems. Some of the most important characteristics of protein sequencing and structural data are the strong correlations buried within them, with the pairwise correlations in the sequence data already being routinely used to predict structural contacts. Here, the team will develop novel ways to use huge data sets to extract higher-order dependences, which are now possible with the availability of the large volumes of sequence data from genomics; and in addition, in the molecular structures such higher-order dependences are directly observable in the protein structures where groups of amino acids interact directly. Importantly, these higher-order dependences reflect the dense physical environment in the cell that requires for proper statistical characterization. A new model free information-theoretic measure is introduced to quantify the higher-order dependences, which serves as the central method in this project. By identifying the major challenges in drawing statistical inference based on this measure, the team will develop, evaluate, and improve a new statistical inference and computational framework for analyses of higher-order dependences with discrete data of a general type, motivated by the protein multiple sequence data. The new computationally efficient framework makes it possible to discover reliable higher-order dependences with the ability of quantifying uncertainty. The preliminary data here combine the information from sequences and structures to yield unexpected results that immediately relate to the dynamics of the protein structures. The outcome is an entirely new approach to handle the large volumes of protein sequence data and other omics data now available and the enormous volumes about to arrive on the doorsteps of omics analysts.

Channing Parker recently joined the Department of Statistics as an instructor. They graduated from the University of Pittsburgh in 2019 with a Master’s degree in Statistics, after receiving their Bachelor’s in Mathematics and Statistics from James Madison University in 2017. Before joining the Statistics Department at CSU, they held a Visiting Instructor position at the University of Pittsburgh from 2019 to 2021. In their spare time, Channing enjoys various forms of art, including photography, macrame, and flower preservation, along with gardening, thrift shopping, and cuddling their dog, Finn. Welcome to the Department of Statistics, Channing!

Jay Breidt has been selected to serve as Chair of the Census Scientific Advisory Committee (CSAC) for the US Census Bureau, beginning in August 2021. CSAC is organized under the Federal Advisory Committee Act and consists of experts from demographics, economics, geography, psychology, statistics, survey methodology, social and behavioral sciences, information technology and computing, marketing and other fields. The Committee advises the Director of the Bureau on the full range of Census Bureau programs and activities including communications, decennial census of population, demographic analysis, economic surveys and censuses, field operations, geographic analysis, information technology, and statistics.

Piotr Kokoszka (PI) and Haonan Wang and Indrakshi Ray received NSF grant “Threat Detection Based on Simultaneous Monitoring of Complex Signals from Multiple Sources” funded by the Division of Mathematical Sciences program “Algorithms for Threat Detection”. Motivated by threats to the operation of fleets of heavy vehicles, the research will develop statistical methodology and theory that will create foundations for a broad class of algorithms for real time detection of malicious attacks on various large scale operations. Statistical methodology will be based on probability in metric spaces and hidden Markov chains. Several PhD students will gain expertise at the intersection of advanced statistics, computer science and engineering.

Julia Sharp continues to be a leader in the American Statistical Association (ASA). Julia served on the ASA Board of Directors from 2017 until 2019. While on the Board of Directors, Julia led a Diversity and Inclusion Task Group. This Task Group initiated a diversity and inclusion resource repository and discussions with other entities to develop a diversity and inclusion consortium. Julia led the proposal for the Justice, Equity, Diversity, and Inclusion (JEDI) Outreach Group, which was approved by the ASA Board and is concluding its successful first year of existence. Julia is also serving as Chair of the ASA Council of Chapters Governing Board. Her initiatives have included modernizing chapter communication and obtaining historical chapter health information to help with leadership transitions and communication. Julia also serves on the Conference for Statistical Practice Steering Committee.

Emily Luetschwager, a 2021 CSU graduate and statistics major, was one of 10 posters recognized in the ASA’s Statistical Significance Poster Competition. The competition consists of submitting a one-page illustration of the value of statistics to society with the objective of communicating to a layperson how the statistical solution to a problem would help inform decision making in specific sectors. Emly’s poster, titled “Statistics and the Environment: Detecting Natural Gas Leaks”, outlined how statistical methods help detect and find natural gas leaks with mobile sensors. Emily’s poster was awarded Honorable Mention. Congratulations, Emily!

Glenn Swanson, a 2021 MAS graduate student won first place in the Harmonized Pediatric Traumatic Brain Injury Hackathon put on by the University of Colorado School of Medicine. To build her models, Glenn kept the mentality of “keep it parsimonious and interpretable” – something that has stuck with her since taking STAA 551 – for both her linear model and logistic regression equations. In the end her models were selected for their “performance, simplicity, and transferability in the healthcare setting”. Congratulations Glenn!

Ryan Volkert and Dawson Eliason both followed a unique opportunity to be among the first class to graduate from Colorado State University’s Data Science major in the College of Natural Sciences, an achievement that uniquely positions them for a range of possibilities in the professional world. Read the full article here.

Emily Luetschwager earned 3rd place at the Celebrate Undergraduate Research and Creativity Showcase. Congratulations Emily!

Graduate student Daniel Mork has been appointed to the Council for Emerging and New Statisticians (CENS) for the Eastern North American Region (ENAR) of the International Biometrics Society. CENS works with the professional society to better serve early career members. The Council is responsible for developing networking; career development; and diversity, equity, and inclusion events. Dan will serve a two-year term of CENS. Congratulations Dan!

See the full list of award winners here.

Julia Sharp was named the Outstanding Mentor Award winner by the American Statistical Association’s Section on Statistical Consulting. See website here. Congratulations Julia!

Assistant Professor Ander Wilson has received a new grant to study the effects of maternal air pollution exposure on birth outcomes. Dr. Wilson is part of a team with researchers at Columbia University, Harvard University, and the Danish Cancer Society that received the award from the National Institute for Environmental Health Sciences. The CSU team will develop new statistical and interpretable machine learning methods as part of the collaboration. These new methods will allow researchers to understand who is most susceptible to air pollution and shed light on the biological mechanisms by which air pollution effects fetal development.

This year the Colorado State University Department of Statistics is celebrating its 50^th anniversary. It is also the 60^th anniversary of its Ph.D. program and the Franklin A. Graybill Statistics and Data Science Laboratory (Stat Lab) – marking six decades of world-class education, research, service, and interdisciplinary support of the CSU community through powerful application of foundational science. Read full article here.

Zach Weller, Assistant Professor in the Statistics Department, helped to publish a paper that estimates methane leakage from local natural gas distribution systems. The EPA has shown interest in potentially using the methodology to update its GHG inventory.

Ander Wilson, Assistant Professor in the Statistics department, was recently recognized as one of the authors of a paper which received the Best Environmental Epidemiology Paper Award. The paper is titled Acute Air Pollution Exposure and the Risk of Violent Behavior in the United States by Jesse D Berman, Jesse Burkhardt,  Jude Bayham, Ellison Carter, and Ander Wilson. See the paper here (scroll down to the BEEP Award). Congratulations Ander!

Statistics PhD Student Daniel Mork won the College of Natural Sciences Outstanding Scholar Award. Dan was one of four awardees from the College of Natural Sciences at the 2020 Graduate Student Showcase. Daniel presented his research that focuses on developing new statistical methods to estimate how maternal exposures to environmental chemicals during pregnancy influence birth outcomes. This is joint work with his thesis advisor Ander Wilson.

Statistic Department’s Ben Prytherch and Professor Matt Ross (Ecosystem Science and Sustainability) received a $20,000 Digital Learning Initiative grant from the Provost’s office to develop three 1-credit, online delivered, freely accessible R courses. The courses are currently being developed by Statistics students Alex Fout, Connor Gibbs, and Kathleen Wendt (PhD student in HDFS). The purpose is to serve students and faculty across the university who are teaching or taking classes that involve data analysis using R. They found that many instructors have been teaching R to their students as a component of their courses. This creates extra work for instructors and a steeper learning curve for students. These courses will allow faculty to direct their students to a high-quality set of training materials that will be accessible at any time, regardless of whether students are enrolled, and will free up time in their own classes to teach subject matter instead of teaching coding.

Colorado State University has a long history of investigating infectious disease outbreaks and is known for research exploring the complex interplay of pathogens, disease vectors, and the ecosystems in which transmissions occur. CSU is focused on the translation of those findings into practical solutions used by farmers, pet owners, and patients. Read more here.

Training students in the art of effective statistical collaboration is essential for their applied statistics education. Challenging situations can arise in collaborations that are necessary to discuss before they occur. Julia Sharp (CSU Associate Professor), Emily Griffith (NCSU Research Associate Professor), and Megan Higgs (Critical Inference LLC and CSU Alumni), with help from Ann Hess (CSU Associate Professor), received support from the American Statistical Association Member Initiative program to create a set of 10 short videos illustrating challenging and salient topics of communication for training in statistical collaboration. Scenarios portrayed in the videos convey realistic statistical collaboration encounters.  The 10 videos and accompanying materials can be used in training future statistical collaborators.  The videos can be found here.

Statistics Instructor and Advisor Ben Prytherch, has started the CSU Statistics Book Club! The club started in January 2019, they read and discuss interesting topics from the world of data analysis and statistics that might not be emphasized in a stats class. Their first book was Deborah Mayo’s “Statistical Inference as Severe Testing”. In fall 2019, the club read Alex Reinhart’s “Statistics Done Wrong: The Woefully Complete Guide”. For spring 2020, they are reading Judea Pearl’s “The Book of Why”. The book club is open to anyone! If you have questions about the group, or if you want to join, send an email to ben.prytherch@colostate.edu. You can also learn more by checking out the CSU Statistics Book Club website, here. Happy reading!

Haonan Wang has been on the forefront of statistical analysis for nearly 20 years. He is well known in the statistics world for a variety of research areas, but his most innovative work is referred to as object-oriented data analysis. Read more…