Research
My research is rooted in the area of Computer Experiments/Uncertainty Quantification/Digital Twins, but spans a range of applications from COVID-19 analysis to deep learning.
Computer model simulations have become increasingly important for studying complex phenomena, such as climate change, response to natural crises, and the spread of pandemic diseases. Despite the popular use of computer simulations, they often encounter three challenges in practice:
- many computer simulations are prohibitively expensive, which require conducting an experiment by which a cheaper, accurate statistical emulator needs to be developed to approximate the computer simulations;
- computer models may be imperfect, so in order to make meaningful predictions, it is essential to calibrate a computer model by aligning the model’s response with physical experimental data such that it can accurately reflect the real-world system;
- optimizing computer model outputs that are expensive to simulate can be almost impossible.
My research focus is to tackle these challenges by providing modern, efficient statistical approaches.
Grants
- NSF DMS 2338018: 2024-2029 (PI, $423,591) CAREER: Single-Fidelity vs. Multi-Fidelity Computer Experiments: Unveiling the Effectiveness of Multi-Fidelity Emulation
- NSF DMS 2113407: 2021-2024 (PI, $142,009) Collaborative Research: Efficient Bayesian Global Optimization with Applications to Deep Learning and Computer Experiments
