Sierra Merkes, Ph.D., is an Assistant Collegiate Professor in the Department of Statistics at Virginia Tech. She earned her doctorate from the Jean Dickinson Gibbons Graduate Program in Statistics at Virginia Tech, where her research focused on developing a mixture model methodology for robust anomaly detection in large-scale sensor systems. Her research interests also include sports analytics and modernizing statistical curricula.
S.Merkes, (2022); “Robust Bayesian Anomaly Detection Methods for Large Scale Sensor Systems”, Dis- sertation, Virginia Tech, 2022.
A. Defreitas, W. N. Alexander, W. J. Devenport, S. Merkes, S. Leman, E. Smith, and A. Borgoltz, (2022); “Anomaly detection in wind tunnel experiments by principal component analysis.”, AIAA Journal, 60(4), 2297-2307.
S. Merkes, S. Leman, E. Smith, A. Defreitas, W. N. Alexander, and W. J. Devenport, (2021); “A Bayesian Mixture Model Approach to Anomaly Detection with Application to Wind Tunnel Experiments”, AIAA Scitech 2021 Forum, https://doi.org/10.2514/6.2021-1268.
A. Defreitas, W. N. Alexander, W. J. Devenport, S. Merkes, S. Leman, E.Smith, and A. Borgoltz, (2020); “Improved Anomaly Detection in Experimental Wind Tunnel Data using PCA”, AIAA Scitech 2020 Forum, https://doi.org/10.2514/6.2020-1198.
S. Merkes, A. Defreitas, E. Smith, W. N. Alexander, W. J. Devenport, and S. Leman, (2019); “Robust Anomaly Detection for Large Scale Multi-Type Sensor Systems”, AIAA Scitech 2019 Forum, https://doi.org/10.2514/6.2019-2265.