Ian Crandell, Ph.D. is a Research Scientist in the Center for Biostatistics and Health Data Science. His research interests include the computational aspects of statistical inference, including high performance computing and parallelization, reproducibility and collaborative coding practice, and data visualization. In his thesis he applied semi-parametric Bayesian models to the problem of error detection and correction in correlated sensor systems. Since then, he has worked on predictive modeling for social problems at the Biocomplexity Institute, analysis of criminal networks, and the application of text mining tools to the classification of toxic protein sequences. He has extensive experience in collaboration from his time working with the Laboratory of Interdisciplinary Statistical Analysis, where he served as a lead collaborator as well as the statistical ambassador to Nigeria.
King R, Entz M, Blair G, Crandell I, Hanlon A, Lin J; The Conduction Velocity-Potassium Relationship in the Heart is Modulated by Sodium and Calcium. Pflügers Archive - European Journal of Physiology (Feb 2021)
McKee K, Crandell I, Hanlon A; County-Level Social Distancing and Policy Impact in the United States: Dynamical Systems Model. JMIR Public Health Surveillance (Dec 2020)
Crandell I, Korkmaz G; Link Prediction in the Criminal Network of Albuquerque. IEEE Conference on Advances in Social Networks Analysis and Mining (2018)
Pires B, Crandell I, Arnsbarger M, Lancaster V, Schroeder A, Shipp S, Kang W, Robinson P, Keller S; Predicting Postsecondary Trajectories in Virginia High Schools using Publicly Available Data. Statistical Journal of the International Association for Official Statistics (2018)
J. Wenskovitch, I. Crandell, N. Ramakrishnan, L. House, S. Leman and C. North; Towards a System- atic Combination of Dimension Reduction and Clustering in Visual Analytics. IEEE Transactions on Visualization and Computer Graphics (Jan. 2018)
Crandell I, Millican J, Leman S, Alexander N, Devenport W, Vasta R, Gramacy RB, Binois M; Anomaly Detection in Large-Scale Wind Tunnel Tests Using Gaussian Processes AIAA Aviation 2017 Conference
Self JZ, Dowling M, Wenskovitch J, Crandell I, House L, Leman S, North C; Observation-Level and Parametric Interaction for High-Dimensional Data Analy-sis. ACM Trans. Interact. Intell. Syst. 9, 4, Article 39 (March 2016)
Hoegh A, Carzolio M, Crandell I, Hu X, Roberts L, Song Y, Leman SC (2015); Nearest-neighbor matchup effects: accounting for team matchups for predicting March Madness. Journal of Quantitative Analysis in Sports, 11(1), 29-37.
Awe OO, Crandell I, Adepoju AA, and Leman SC; A Time Varying Parameter State-Space Model for Analyzing Money Supply Economic Growth Nexus. Journal of Statistical and Econometric Methods (2015), 4(1), pp.73-95.
Hoegh A, Crandell I, Klopfer S, Fies M; Model selection with missing covariates for policy considerations in fox enclosures. Journal of Applied Statistics (2016), 1-14.
Crandell I, Millican A, Leman S, Alexander N, Devenport W, Vasta R, Gramacy RB, Binois M; Anomaly Detection in Large-Scale Wind Tunnel Tests Using Gaussian Processes (2017) AIAA Aviation Conference
Self JZ, Dowling M, Wenskovitch J, Crandell I, House L, Leman S, North C; Observation-Level and Parametric Interaction for High-Dimensional Data Analysis. ACM Trans. Interact. Intell. Syst. 9, 4, Article 39 (March 2017)
Crandell I, Korkmaz G; Link Prediction in the Criminal Network of Albuquerque (2018) IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Crandell I; Cultural Values, Statistical Displays, Amstat News Master’s Notebook, May 2015
Pires B, Crandell I, Arnsbarger M, Lancaster V, Keller S, Schroeder A, Shipp S, Kang W, Robinson P; Predicting Postsecondart Trajectories in Virginia High Schools Using Publicly Available Data (2018), Journal of Official Statistics.