I work and teach on the interface between data, technology and science.
My research covers experimental and computational methods for combining observations and simulation models for descriptive, predictive and prescriptive purposes in large-data environments.
The major tools used are machine learning and statistical analysis, large-scale optimization and designed experimentation including sequential design.
Doctor of Philosophy, Industrial Engineering, Georgia Institute of Technology, 2015.
Master of Science, Statistics, Georgia Institute of Technology, 2011.
Bachelor of Science, Mechanical Engineering, Purdue University, 2010. research [ papers]
select theory/methodology pubs
, ", Composite grid designs for adaptive computer experiments with fast inference" , vol. 108, no. 3, pp. 749-755, 2021.
, ", Plausible screening using functional properties for simulations with large solution spaces" , 2021. Operations Research accepted
, ", Computer model calibration with confidence and consistency" , vol. 81, no. 3, pp. 519-545, 2019.
Journal of the Royal Statistical Society: B
, ", Building accurate emulators for stochastic simulations via quantile kriging" , vol. 56, no. 4, pp. 466–473, 2014. Technometrics Implementation done in `quantkriging` on CRAN by Kevin R. Quinlan and James R. Leek.
select application pubs
, ", Get on the BAND Wagon: a Bayesian framework for quantifying model uncertainties in nuclear dynamics" , vol. 48, no. 7, pp. 072001, 2021.
Journal of Physics G: Nuclear and Particle Physics
, ", High-fidelity hurricane surge forecasting using emulation and sequential experiments" , vol. 15, no. 1, pp. 460-480, 2021.
Annals of Applied Statistics
, ", Calibrating functional parameters in the ion channel models of cardiac cells" , vol. 111, no. 514, pp. 500-509, 2016.
Journal of the American Statistical Association advising
Ozge Surer , Current Postdoc 2020-
David Eckman , Postdoc 2019-2021 Gregory Keslin, Current PhD Student
Eugene Wickett, Current PhD Student
Moses Chan, Current PhD Student
Collin Erickson, PhD awarded 2019
Wenbo Sun, PhD awarded 2018
IEMS 490, Principals of Uncertainty Quantification, Northwestern University: Winter 2021
IEMS 401, Applied Mathematical Statistics, Northwestern University: Fall 2021, Fall 2019, Fall 2018
IEMS 303, Statistics, Northwestern University: Fall 2021, Winter 2021, Winter 2020, Fall 2019, Winter 2019, Fall 2018, Winter 2018, Fall 2017
IOE 465, Design and Analysis of Experiments, University of Michigan: Winter 2017, Winter 2016
IOE 591, Bayesian Data Analysis, University of Michigan: Fall 2016.
ISYE 3039, Methods for Quality Improvement, Georgia Institute of Technology: Fall 2014.
Frameworks: Bayesian Analysis of Nuclear Dynamics. National Science Foundation,
Senior Personnel (11 PI, co-PI and Senior Personnel), 07/2020-07/2025 Inducing and Exploiting Grid Structures for Fast, Adaptive, and Accurate Estimation. National Science Foundation,
PI, 08/2020-08/2023 Collaborative Research: Variational Inference Approach to Computer Model Calibration, Uncertainty Quantification, Scalability, and Robustness. National Science Foundation,
co-PI, 08/2020-08/2023 EAGER/Collaborative Research: Exploring the Theoretical Framework of Engineering Knowledge Transfer in Cybermanufacturing Systems. National Science Foundation,
Local PI, 06/2017-06/2018 service
Technometrics , 2019- Organizing Committee, Statistical perspectives on uncertainty quantification, 2017
Best student paper competition organizer, INFORMS Quality, Reliability and Statistics Section, 2018
Advisory council, INFORMS Quality, Reliability and Statistics Section, 2016-2018
Treasurer, ASA Quality and Productivity Section, 2016-2018