Sierra Merkes

Sierra Merkes serves as a Collegiate Assistant Professor in the Department of Statistics within Virginia Tech's College of Science, a position she has held since 2022 following her doctoral completion at the same institution. Her academic career includes prior service as an Instructor of Record (2020-2022), with teaching responsibilities spanning undergraduate and graduate statistics courses including sports analytics and engineering statistics.

Her educational foundation comprises:

• Ph.D. in Statistics, Virginia Tech (2022), advised by Scotland Leman
• M.S. in Statistics, Virginia Tech (2017)
• B.S. in Mathematics, Radford University (2016)

Research focuses on developing robust statistical methodologies for anomaly detection in sensor networks, particularly within aerospace wind tunnel applications, while maintaining active contributions to statistics education and sports analytics. Her work integrates Bayesian inference with engineering constraints to address real-world data challenges in multi-type sensor systems.

Publication analysis reveals consistent emphasis on anomaly detection techniques using Bayesian models and dimensionality reduction methods, with increasing sophistication in handling large-scale heterogeneous sensor data. The research trajectory demonstrates strong interdisciplinary collaboration between statistics and aerospace engineering, yielding practical solutions for experimental data reliability.

Recognition includes:

• 2021 AIAA Sensor Systems and Information Fusion Best Paper Award
• Jesse C. Arnold Teaching Award (2020-2021)
• Rose Costain Graduate Fellowship for Departmental Leadership (2019-2021)
• Corporate Partner Scholarship (2016)

Professional service encompasses conference organization for Women in Data Science (2025), the Department of Statistics 75th Anniversary (2024), and Design and Analysis of Experiments Conference (2024), reflecting commitment to community building. Current teaching portfolio includes advanced regression analysis, sports analytics, and engineering statistics courses.

Research activities involve collaborative work with aerospace engineering groups on wind tunnel data analysis, though no dedicated laboratory is specified in available materials.