I am always interested in hearing about new and challenging problems. My area of expertise is mainly in design of experiments, where I have studied a variety of topics from experimental run order, space filling designs, Bayesian design of experiments, and covering arrays for software testing. In addition, I am recently interested in Active Learning for Gaussian Processes and multi-fidelity modeling.  I also have previous experience from my statistical consulting assistantship during my time as a graduate student at Penn State.

Ph.D., Statistics, The Pennsylvania State University (2019)

B.S., Mathematics, Duquesne University (2012)

Journal Publications:

Quinlan, Kevin R., Jagadeesh Movva, and Brad Perfect. "Non‐uniform active learning for Gaussian process models with applications to trajectory informed aerodynamic databases." Statistical Analysis and Data Mining: The ASA Data Science Journal 17.2 (2024): e11675.

Verriere, M., Schunck, N., Kim, I., Marević, P., Quinlan, K., Ngo, M. N., ... & Lasseri, R. D. (2022). Building surrogate models of nuclear density functional theory with Gaussian processes and autoencoders. Frontiers in Physics, 10, 1028370.

Quinlan, Kevin R., Movva, J., Stein, E.V.,  and Kupresanin, A. (2021) "Leveraging Multi-Fidelity Aerodynamic Databasing to Efficiently Represent a Hypersonic Design Space," ASCEND 2021. AIAA 2021-4245.

Kravvaris Konstantinos, Quinlan, K.R., Quaglioni, S., Wendt, K.A., and Navratil, P. (2020) “Quantifying uncertainties in neutron-alpha scattering with chiral nucleon-nucleon and three-nucleon forces” Physical Review C. 102(2), 024616

Schunck Nicolas, Quinlan, K.R., and Bernstein, J. (2020) “A Bayesian Analysis of Nuclear Deformation Properties with Skyrme Energy Functional” Journal of Physics G: Nuclear and Particle Physics. 47(10), 104002

C.M. Anderson-Cook, L. Lu, K.L. Myers, K.R. Quinlan, and N. Pawley, "Improved learning from data competitions through strategic design of training and test sets" Quality Engineering (2019)

C.M. Anderson-Cook, K.L. Myers, L. Lu, M.L. Fugate, K.R. Quinlan, and N. Pawley, "How to host an effective data competition: statistical advice for competition design and analysis." Statistical Analysis and Data Mining (2019) 1-19

K.R. Quinlan, C.M. Anderson-Cook, "Bayesian design of experiments for logistic regression to evaluate multiple nuclear forensic algorithms." Applied Stochastic Models in Business and Industry 34.6 (2018) 908-921.

K.R. Quinlan, C.M. Anderson-Cook, and K.L. Myers. "The weighted priors approach for combining expert opinions in logistic regression experiments." Quality Engineering 29.3 (2017): 484-498.

K.R. Quinlan, and D.K.J. Lin. "Run order considerations for Plackett and Burman designs." Journal of Statistical Planning and Inference 165 (2015): 56-62.

Invited Talks:

“Active Learning for Multi-Fidelity Aerodynamic Databases of Hypersonic Design Spaces” Aerospace and Mechanical Engineering Seminar Series at University of Arizona, AZ (March 2022)

“The construction of ε-bad Covering Arrays” Invited Talk at Joint Statistical Meetings Substitute Speaker For Dennis Lin Denver, CO (August 2019)

"Bayesian Design of Experiments with Multiple Priors for Kaggle Competition Design" Invited Paper at Joint Statistical Meetings ASA Vancouver, CA (August 2018)

“Bayesian Design of Experiments for Logistic Regression to Accommodate Multiple Forensic Algorithms” Invited Session at Quality and Productivity Research Conference (June 2017)

Selected Contributed Talks and Poster Presentations:

“Surrogate Models and Sampling Plans for Multi-Fidelity Aerodynamic Performance Databases” Contributed Talk at Dataworks Virtual Conference (March 2021)

“Improved Learning in Data Competitions through Strategic Generation of Informative Data Sets” Student Poster at Conference on Data Analysis (March 2018)

“Run Order Considerations for Plackett and Burman Designs” Contributed Paper at Joint Statistical Meetings ASA Chicago, IL (August 2016)